Wonderware InTouch – Features

Wonderware InTouch controls more than 100,000 plants and factories around the world. InTouch has enabled these plants to achieve world-class performance, as well as reduce costs and maintain product quality.

Wonderware InTouch
Wonderware InTouch

What is InTouch?

Wonderware InTouch is the world’s most advanced and well-known Human Machine Interface (HMI) and process visualization software. It offers world-class innovation, brilliant graphics, maximum ease of use, and unmatched connectivity. InTouch is simply the most sophisticated graphics technology and the most intuitive product on the market for process visualization.
Wonderware InTouch, the world’s most appreciated HMI and used in more than a third of manufacturing and industrial plants, enables users to quickly create standardized, reusable, one-click visualization and installable applications across the enterprise, including to mobile users.

For practical training on Installation and other training in Wonderware visit our website Burraq Engineering Solutions

Evaluation of the situation for greater effectiveness of the operator

Through 30 years of countless innovative visual and technological advancements, InTouch brings unmatched levels of clarity, consistency, and meaning to embedded data. Together, these visual innovations enhance the ability to better understand the recent past, present, and possible future of the process.
The ArchestrA Graphics Situational Awareness Library provides a superior set of functional blocks for dynamic process visualization. It is a unique resource that helps operators focus on the most useful content, solve problems, and minimize distraction and fatigue. As a result, there are fewer interruptions and less downtime, and a greater focus on improving performance, safety and cost control. Simply Wonderware.

Viewing is accessible from anywhere

The world’s favourite HMI is also fully mobile. Eventual and remote Web HMI and mobile SCADA users can now view and control plant operations data in real-time via a secure web browser from virtually any “smart” device such as tablets and smartphones.
Wonderware® InTouch Access Anywhere is an extension of Wonderware InTouch. It offers access to InTouch applications through any HTML5 compatible web browser and completes our vision of enabling multi-level viewing, collaboration and execution in the organization, with no customer installation and no maintenance. It enables users to safely monitor or troubleshoot plant equipment or processes from anywhere, or on any device, at any time. Simply Wonderware.

Maximum investment protection

After a 30-year history of leaving no customers behind, Wonderware provides year after year updates that protect customer investments in InTouch applications. An InTouch application deployed decades ago can continue to function, unchanged, with the latest version of the InTouch software. You get all the benefits of the latest hardware and operating system enhancements with no retrofit costs; no other company can claim the same. Simply Wonderware.

The core of the company’s unification

In today’s modern industrial facilities there is a multitude of data sources, from field devices and PLCs to distributed control systems (DCS). InTouch has been the leader in open systems since 1987 and has earned that reputation by connecting to more devices and systems in the plant than any other software. Industrial plants around the world often substitute supplier software for the PLC vendor for InTouch. It connects to hundreds of I / O and OPC servers and the Wonderware DA Server toolkit allows you to create specialized data servers, easily and as needed. There is no data outside the scope of InTouch. Simply Wonderware.

Powerful and sophisticated

Virtualization technologies play a key role for companies trying to reduce their hardware costs. No one in the industry offers more virtualization options than Wonderware, including the latest virtualization technology from Microsoft®, Hyper-V and VMware. InTouch leverages Hyper-V and VMware so you can deploy redundant HMI applications locally or remotely for more cost-effective high availability and disaster recovery options.
Dynamic resolution conversion allows you to adapt runtime between screen resolutions, so you can view InTouch applications at various screen resolutions without the need to modify the application. This enables operational agility and the ability to build and run applications anywhere.
Resolution-independent graphics can also be resized or enlarged without distortion, so they can be designed in one resolution and reused without distortion in a different resolution or on various devices, whatever the screen size.
InTouch natively incorporates “smart” features for consistent data handling and data quality visualization on operator screens. All this power is at your fingertips without writing a single line of code. Simply Wonderware.

Ready-to-use symbol library

InTouch comes equipped with a comprehensive library of pre-built and tested stunning graphic symbols and faceplates containing over 500 professionally designed ArchestrA graphic symbols, most with customizable “intelligence” already built-in, providing drag-and-drop access. drop ”to previously built engineering components. InTouch reduces engineering costs and enables you to quickly and easily develop custom graphical views of your processes in real-time. Simply Wonderware.

Versatile and expandable

InTouch is an open and extensible HMI with intuitive graphical animation and scripting capabilities that bring incredible power and flexibility to application designers. InTouch offers the ability to use existing vector graphics, bitmap graphics, library symbols, .NET controls, and ActiveX controls.
ArchestrA symbols are compatible with embedded .NET controls, giving you the freedom to extend your application without restrictions without programming. They also offer access to standard protocols such as Web browsers, desktop applications, mapping tools, ERP components, and any other. NET-compliant control-based application. Simply Wonderware.

Top 10 largest hydrogen projects

The future should always be greener – also with the help of hydrogen. This overview shows the world’s largest hydrogen projects.

hydrogen projects
hydrogen projects

More and more cars are being operated electrically, electricity is generated by wind turbines and solar systems, and hydrogen is supposed to make production plants greener and protect the environment. Hydrogen is used in many areas, for example as an alternative fuel or fuel. It can also be converted into heat and electricity.
By being greener If hydrogen can be produced completely without fossil raw materials, it is considered to be really climate-friendly. Due to the many advantages that green hydrogen brings with it, interest is increasing and production is also expected to grow.

We show you the 10 largest hydrogen projects that are currently planned

Ammonia project by BP

The British oil company BP has received support from the Australian government for the implementation of its project. A feasibility study is to determine whether the western Australian city of Geraldton is a suitable location for a pilot plant for the production of 20,000 tons of green ammonia per year.
The pilot plant will initially produce environmentally friendly hydrogen, which will then be converted into green ammonia. This is then intended for both domestic and export use. According to BP, the commercial-scale plant would need a capacity of 1.5 gigawatts of electricity. If the pilot plant works successfully, the operator could increase the capacity to one million tons of green ammonia in the long term.

What is Green Ammonia?

Ammonia is a colourless gas at room temperature. The chemical compound consists of nitrogen and hydrogen with the molecular formula NH3. Green ammonia describes the manufacturing process: In contrast to ammonia produced conventionally from fossil fuels, green ammonia is based on renewable energies.
Because green ammonia can be used both as a basic chemical, as a fuel and as a comparatively easy-to-transport hydrogen storage medium, toxic gas plays an important role in the global energy transformation.

Project HyEx

As early as July 2019, the French energy supply group Engie and Enaex, a Chilean manufacturer of ammonium nitrate, formed a strategic alliance.
According to the Chilean “Diario Financiero”, the joint HyEx project will include a 2 GW solar park and a 1.6 GW hydrogen electrolysis plant. This plant could produce 124,000 tons of green hydrogen per year, which could then be used in an ammonia plant.
In this plant, 700,000 tons of green ammonia could be produced annually. 50 per cent of the ammonia would go to Enaex’s ammonium nitrate plant, eliminating the need to import it and the rest could be used for fuel, green manure production and the export market.
The goal by 2024: The operation of a pilot plant that includes a 36 MW solar system, a 26 MW hydrogen electrolyzer and 18,000 tons of ammonia every year. Full operation is scheduled for 2030.
According to estimates by Engie and Enaex, the HyEx project has an annual CO 2 reduction potential of over 600,000 tons.

Project HYPORT Ostend

For this project, the seaport of the Belgian city of Ostend, the company Deme, active in the field of marine technology, and PMV have joined forces. The goal by 2025: The commissioning of a green hydrogen plant in the port area of ​​​​Ostend. Until then, the project goes through the following steps:

⦁ Phase 1: Checking the general feasibility and creating a development plan
⦁ Phase 2: Start of a demonstration project with mobile shore power supply
⦁ Phase 3: Roll-out of a green hydrogen-powered electricity project (starting by 2022)

In addition, 399 wind turbines are to be put into operation off the Belgian coast. Together, these would have a total capacity of 2.26 GW. According to Dewe, there would also be room for several hundred more such systems, which could generate an additional 1.75 GW. This means that the total generation capacity for green electricity is 4 GW. Half of the Belgian households could be supplied with it.
The green hydrogen, the product of the HYPORT project, is intended to act both as an energy source and as a raw material. After completion of the project, an annual CO 2 reduction of 500,000 to one million tons should be achieved.

Project H2 Hub Gladstone

Australian infrastructure company Hydrogen Utility (H2U) is also planning a new project to produce green hydrogen. The company is working with Thyssenkrupp to do this. A 3 GW electrolysis plant is to be built near the Australian port city of Gladstone, Queensland.

Pacific Solar Hydrogen Project

This project was started by an Australian start-up: The renewable energy provider Ausstrom Hydrogen. According to the company, it chose the city of Callide in Queensland as the location for a solar park and a plant for the production of hydrogen. Up to 3.6 GW of green electricity in the form of hydrogen is to be produced in one plant. 
The project is still in the development phase, but in the long term, the hydrogen is to be exported by ship to Japan, South Korea and other countries. 
The solar park will use a mixture of existing and newly created infrastructures to be able to supply the hydrogen plant with green electricity. Because the electricity is generated near the hydrolyser, there should only be a very small loss in performance.
According to outflow Hydrogen, other advantages and goals of the project are:

⦁ The creation of thousands of jobs in Queensland.
⦁ Annual production of over 200,000 tons of hydrogen.
⦁ The Development of a New Export Industry for Australia.
⦁ Obtain the necessary permits by the fourth quarter of 2022.
⦁ Start of construction by the second quarter of 2023.

Project Helios Green Fuels

1.5 million euros – with this amount, Germany is participating in a planned 20 MW hydrogen electrolysis plant from Thyssenkrupp, which is to be built in Saudi Arabia. The facility, which can produce hydrogen from solar and wind power, is set to become part of Project NEOM, the Saudi “planned city,” which could cover an area of ​​26,500 square kilometres.
The environmentally friendly ammonia plant is to be operated with renewable energy. To this end, Air Products (provides the technology for producing nitrogen through air separation), ACWA Power and NEOM have reached a joint agreement worth five billion US dollars to build a hydrogen-based ammonia production plant.
Also present: the Danish catalysis company Haldor Topsoe, whose technology is to be used for the production of green ammonia.
From the planned commissioning in 2025, that’s part of the project, 650 tons of hydrogen and 3,000 tons of ammonia are produced daily from over 4 GW of environmentally friendly electricity. The green ammonia is then to be shipped overseas.

Project Murchison Renewable Hydrogen

Solar and wind power plants with a capacity of 5 GW are to be built near the Australian city of Kalbarri. The company Hydrogen Renewables Australia (HRA) is pursuing this project and has secured cooperation with the Danish fund manager Copenhagen Infrastructure Partners (CIP) .
The electricity generated by the plants is to be used to produce green hydrogen. The water required for the electrolysis is to be obtained by a seawater desalination plant.
A feasibility study is also to be carried out first for this project. The construction of the plants would create jobs for 2,000 to 3,000 people. Once the plants are up and running, 250 to 300 people could be employed on a permanent basis.
The Murchison Renewable Hydrogen project can be broken down as follows:

  1. Production of hydrogen for vehicles
  2. Checking whether, and if so how, feeding into the nearby Dampier-Bunbury natural gas pipeline is possible
  3. Export of hydrogen to Asian markets, especially to Korea and Japan

AquaVentus project

This project, which is based on a support association currently consisting of 40 companies, is also pursuing a major goal: by 2035, a generation capacity of 10 gigawatts for green hydrogen from offshore wind energy is to be achieved on the island of Helgoland and then transported to land.
Thanks to electrolysis technology, one million tons of green hydrogen could soon be extracted from the North Sea every year, from Helgoland to the Dogger Bank. Aqua Ventus is also striving for long-term integration into a European hydrogen network. The Doggerbank forms the endpoint with a connection to other offshore hydrogen hubs and with cross-connections to Great Britain, Denmark and the Netherlands.

Everything you need to know about CO2-neutral industry

Do you want to know everything about CO 2 -neutral industry? Then you are right here. You can find out everything about the current status of the climate-neutral industry, which technical innovations there are, how mechanical engineering is reacting and what the legal situation is in the article “The large overview of CO 2 -neutral industry “.
Regenerative energies are needed to make the climate-neutral industry a reality. Read here which renewable energies are available and how they are most useful in the industry.
Or are you more interested in the subject of hydrogen? 

Project NorthH2

Shell EquinorGasunie, RWE and the port of Groningen Seaports – are all involved in the hydrogen project NortH 2. Together they want to set up a system of offshore wind farms, electrolysers, gas storage facilities and pipelines. This should not only allow offshore wind to be converted into green hydrogen but this electricity should also be stored and brought to industrial centres in northwest Europe.
A centre for green hydrogen is to be built in the north of the Netherlands with a capacity or electrolysis capacity of 4 GW by 2030 and more than 10 GW by 2040. In this way, one million tons of green hydrogen could be produced each year and eight to ten million tons of CO 2 avoided at the same time.

Asian Renewable Energy Hub project

The goals for the Asian Renewable Energy Hub were set very high – it is currently the largest planned hydrogen project. The site was 6,500 square kilometres “cut out” of a 14,000 square kilometre property in the East Pilbara region of Western Australia. Initially, 26,000 MW are to be generated there with the help of wind turbines and photovoltaic systems. The energy thus obtained is to be used primarily to produce environmentally friendly hydrogen products for both domestic and export use.
The long-term goals:

⦁ The generation of 26 GW of renewable wind and solar power
⦁ At least 3 GW of low-cost, clean power generation capacity for the Pilbara region
⦁ Up to 23 GW of electricity generation for the production of green hydrogen and green ammonia
⦁ The creation of 20,000 jobs during the 10-year construction period and 3,000 jobs during the operating period
⦁ Up to 100 TWh of total annual generation
⦁ A system service life of more than 50 years

Industry 4.0 | Economy in Digital Upheaval

After ten years of Industry 4.0, many German companies have achieved more in terms of networking and digitization than they think. Where is the journey going now?

Industry 4.0
Industry 4.0

Angela Merkel was immediately enthusiastic. Shortly before the Hanover Fair 2011, the President of the German Academy of Science and Engineering (acatech), Henning Kagermann, the Director of the German Research Center for Artificial Intelligence, Professor Wolfgang Wahlster, and the current State Secretary in the Federal Ministry of Education, Professor Wolf-Dieter Lukas, described the networking of machines via the Internet of Things (IoT) and the digitization of the processes running on them for the first time as Industry 4.0. The Chancellor found the concept so conclusive that she spontaneously used it in her opening speech at the industry show as proof of the performance of German technology providers.

Ten years later, in April 2021, a full 94 per cent of the companies surveyed by the digital association Bitkom on Industry 4.0 stated that they could only remain competitive by networking their processes. However, 65 per cent of the survey participants fear that they have missed the boat when it comes to digitizing their production or are laggards. “Medium-sized companies in particular answer the question of the status of Industry 4.0 in their companies with relentless honesty,” explains Dominik Rüchardt, Head of Business and Market Development in Central Europe at the provider of technologies for Industry 4.0, PTC. “They complain that they don’t have the IT know-how for the change and don’t know how to network and monitor their existing systems and machines.”
It’s different in larger industrial companies: Two out of three of the companies surveyed by Bitkom now use intelligent robots or systems for real-time communication between their machines. Four out of ten large companies also network their systems with IoT platforms on which they evaluate the data transmitted by the machines.

Today all future technologies of the year 2011 are in use

In addition, basically, all technologies have found their way into their production and development, their sales and purchasing, from cloud computing and augmented reality (AR) to digital twins, artificial intelligence (AI) and software solutions for product lifecycle management (PLM). held, with which acatech President Kagermann and his colleagues wanted to connect products and their manufacture in 2011 to so-called “cyber-physical systems”.
“The individual technologies interact with the physical product such as the nervous system, brain, memory and thinking abilities with the human body,” . The digital twin serves as a kind of brain. Because it reflects all the properties and behaviour of the product. “The IoT serves as a nervous system that connects it to the physical product, registers how it behaves and transmits signals between the analogue and the virtual world,” adds Rüchardt. With these impulses, the behaviour of the product can also be influenced.

AI is the mind of Industry 4.0

At the same time, AI and machine learning could correlate the received signals and recognize when changes in the behaviour of the product are necessary – for example, to achieve better manufacturing quality by adjusting the parameters of the process running on a machine. “So AI is, so to speak, the thinking ability of the cyber-physical system. As such, she recognizes at an early stage when repairs or the replacement of wearing parts will be necessary and thus helps to increase the availability of machines through predictive maintenance.
The PLM, in turn, remembers the findings of the AI. Similar to the human memory, it not only knows how the product should work but also how it has behaved and changed over time. The information it provides helps development engineers to optimize future product versions.
“Finally, augmented reality makes all this data usable and helps people to optimally influence devices and systems – for example, by providing a maintenance worker with the data needed to repair a machine on his tablet or showing him where with the help of AR glasses he has to lend a hand,” adds. This means that maintenance work can also be carried out by less experienced technicians. Machines fail for a shorter period of time if an employee from their manufacturer does not have to travel to repair them first.

The digital transformation of industry needs a reliable set of rules

“Anyone who wants to assess what German companies have achieved in the first ten years of Industry 4.0 must not only think technologically,” warns Rüchardt. In order for the smart networking of production, development and procurement processes to succeed, numerous participants must be integrated into a digital ecosystem – from the manufacturing company to its suppliers and clients to logistics service providers. “Industry 4.0 is therefore also a far-reaching structural change that changes the relationships between manufacturers, suppliers and customers and pushes new providers into established markets.
In order for this to be legally secure, a set of rules consisting of laws, contract models, reliable payment systems and active business relationships is needed that everyone involved can rely on. Only such a reliable framework for business relationships in Industry 4.0 allows new business models to be developed. According to the Bitkom survey, three out of four industrial companies are striving for this.

“Machine as a Service” – the supreme discipline of Industry 4.0

Above all, “as a service” business models are a profound disruption for providers. “A mechanical engineering company, for example, then no longer collects the price for its product when it is sold, but spread over many years. This puts a lot of stress on his balance sheet. In order to be able to survive economically, the manufacturer would need new partners, such as financial holdings or leasing companies, who buy their machines and rent them out as operators according to an “as a service” model.
To do this, they must be able to guarantee customers that the devices will perform as promised in the rental or leasing contract. This presupposes that operators continuously monitor the machines and know at all times what condition they are in, what quality they are producing and how their economic operation is. The machine builder’s new partners must also be able to maintain their devices and, in the event of a fault, repair them reliably and, if possible, without a time-consuming journey by a service technician.
The manufacturer of a machine can sell all these services in addition to the machine. Because their rental company must be able to access the technical expertise of the machine builder as well as the complete technology stack of Industry 4.0. Otherwise, his business model will fail. In order for the interaction between the manufacturer of the machine, its operator and its customers to work, all those involved also need a legal framework in which liability issues and the respective rights and obligations in their business relationships are reliably regulated. “Machine as a Service is thus becoming the supreme discipline of Industry 4.0,” summarizes Dominik Rüchardt.

Industry 4.0 in brownfield environments – individual solutions lead to chaos

Companies encounter similar problems when they want to network and monitor older machines and systems in order to increase their availability and performance. In order to be able to develop its potential, Industry 4.0 also needs a framework in such brownfield environments – in this case, however, a technological one.
“Older machines and systems were mostly developed in a downstream process and trimmed for operational excellence. This makes it very easy to monitor and optimize each machine individually. However, the approach is not sufficient to use the existing systems to react quickly and flexibly to changes in demand for the products manufactured with them.
To do this, the machines must be networked via an IT architecture that can extract meaningful information from the data supplied by the devices even if the data follows the logic of the operating systems from different manufacturers and is available in different formats.
“This requires clearly structured and comprehensible IT architecture concepts that can deal with the heterogeneity of brownfield environments. Otherwise, there will be a lot of individual solutions and thus chaos,”. Networking of manufacturers, their suppliers and customers are then hardly possible.

The digital platform economy is not far away

“However, the first corresponding sets of rules are now available – for example, the reference architecture model Industry 4.0, RAMI,” assures Rüchardt. At the same time, the first consortia would implement these frameworks in practical cooperation between companies from sectors such as mechanical engineering, the process industry or automotive production.
“If things continue to develop like this, in three to four years the economic interaction between companies and their partners will mainly take place on digital platforms.

Artificial intelligence – clearly explained

Whether in industry or in the private sphere – artificial intelligence is on everyone’s lips. But what does artificial intelligence mean? In this article, we answer the most important questions on this topic

Artificial intelligence

Artificial intelligence is one of the really big future topics of our society

  • What is artificial intelligence?
  • How intelligent is artificial intelligence really?
  • How does artificial intelligence work?
  • How do you program artificial intelligence?
  • What do we need artificial intelligence for?
  • Where is artificial intelligence used?
  • Smart machines: How is artificial intelligence changing our lives?
  • How is artificial intelligence changing our society?

What is artificial intelligence?

When developing systems with artificial intelligence (abbreviated: KI, English: Artificial Intelligence), researchers and developers try to emulate human perception and human action using machines. An official definition of the term Artificial However, there is no such thing as intelligence, which is partly due to the abstract nature of the term intelligence and the rapid change in the subject area. Therefore, AI is mainly defined by its properties and the associated sub-areas, such as speech recognition, image processing or machine learning.
Properties that are characteristic of AI are autonomy and adaptivity. AI systems have the ability to perform tasks in complex environments without constant human guidance. They are also able to improve their performance independently by learning from their experiences.
In general, the systems are divided into weak and strong AI:

⦁ Weak AI refers to machines that can replace a single human cognitive ability. Systems with weak AI perform a specific task and behave intelligently.
⦁ A strong AI would be a machine that has the same abilities as a human or even surpasses human abilities. The system with strong AI could thus fulfil any intellectual task. It would be intelligent (compared to the weak AI that just behaves intelligently) and would be conscious.

The AI ​​​​solutions that exist to date all fall into the ‘weak AI’ category. Strong AI systems only exist in the field of science fiction.
The term artificial intelligence includes many sub-areas, which in turn include numerous AI methods and AI applications, for example:

⦁ knowledge acquisition and representation
robotics
⦁ Pattern recognition (images, text, speech)
⦁ Prediction (Big Data / Predictive Analytics)
Machine learning (deep learning/neural networks)

Machine learning or the special form of deep learning, in particular, are often regarded as the most important method of artificial intelligence, because learning from experience and the ability to generate new actions from it is evidence of intelligence.

How intelligent is artificial intelligence really?

The answer to this question is closely related to the definition of the word ‘intelligent’. Because the question is whether intelligence is the same as intelligent behaviour or whether intelligence requires a brain and a consciousness.
Provided that intelligence equals intelligent behaviour, the English mathematician and logician Alan Turing developed a test to determine whether a machine is intelligent. In the so-called Turing test, a human investigator interacts with two chat partners – one of them is human, one is a computer. If the investigator is not able to distinguish artificially from human intelligence by exchanging written messages, the computer has passed the test and must therefore have reached the level of human intelligence.
But even if a machine behaves intelligently and thus passes the Turing test, it still does not have a human brain. If this is assumed for real intelligence, then only strong AI systems would be really intelligent.
For the areas in which AI is currently being used, the question of ‘real intelligence’ is not that important. What is far more important is how well a system can perform the task for which it is designed. In this context, the following often applies: artificial intelligence is only as smart as the data with which it is trained

How does artificial intelligence work?

The topic of artificial intelligence includes many AI methods and AI applications, each of which has different functionalities. In general, the intelligent behaviour of the technologies is simulated using computer science and mathematics/statistics. The computers are trained for specific tasks, often by processing large amounts of data and recognizing patterns in it.
This requires certain skills that can be divided into four areas: perception, understanding, action and learning.

  1. Perceive: This skill creates the data that an AI-powered system operates on. 1. Sensors of all kinds, including cameras and microphones, are used for this purpose.
  2. Understanding: This part of the AI ​​​​is the processing component from which the control panel emanates. The data obtained is processed using statistical analysis, speech/image recognition, algorithms that define special rules, or machine learning.
  3. Action: This is the output component of the AI ​​​​that issues commands to connected devices, triggers follow-up processes, or outputs other things such as images, translations, and the like.
  4. Learning: This area is special about current AI technologies. You can learn from mistakes and feedback from the areas of ‘understanding’ and ‘acting’ during the training phase and also during operation.

Algorithms and neural networks play an important role in both ‘understanding’ and ‘learning’.

What are algorithms?

Algorithms are detailed and systematic instructions that define step by step how a mathematical problem can be solved. Algorithms are implemented, ie translated into a programming language, so that the computer/machine can generate the desired solution from the given information.
There are, for example, sorting or search algorithms (these include Google’s algorithms), but also algorithms that can make complex decisions based on all relevant factors. To do this, an algorithm takes all the factors (which have to be defined beforehand by a human) and links them in all possible variants. He then goes through every possible combination and its consequences and compares them with the programmed specifications. From this, the algorithm calculates the answer that is most likely to be correct.
The class of learning algorithms, which are summarized under the generic term machine learning, is particularly important for AI. You can learn from a large number of example cases and derive general rules. After the learning phase, you can apply these insights to real cases. Among the machine learning algorithms, there are also deep learning algorithms that are used to analyze and process particularly large amounts of data – in connection with neural networks.

How do neural networks work?

Artificial neural networks (ANN) are the attempt to artificially reproduce aspects of the human brain known from research. It is mainly about simulating and using the interaction of nerve cells (neurons) and their connections (synapses).
The goals of artificial neural networks are, on the one hand, a better understanding of the human brain and the processes taking place there and, on the other hand, advances in machine learning by linking huge amounts of data (big data) and deep learning techniques.
A single neuron is a simple information processor that can examine exactly one aspect of information. To process very complex information, many neurons are therefore connected to each other. These connections are called synapses, they form a complex network between the neurons. A neural network (regardless of whether it is biological or artificial) consists of a large number of neurons that can receive signals and transmit them to other neurons via connections.
There are three types of neuron layers in an ANN:

  1. Input Layer: The first layer of neurons for raw processing of the information. These neurons feed pre-processed information into the network.
  2. Hidden Layer(s): One to a finite number of neuron layers with linked neurons. During the training, you will learn how to read patterns from the raw information using examples. From these, complex typical characteristics are worked out in order to solve the task.
  3. Output Layer: The last neuron layer – the so-called output layer. It can represent any possible outcome.

The big advantage of artificial neural networks compared to ‘classical’ data processing is that they can consider several aspects at the same time. While a ‘simple’ algorithm analyzes each property of a data set one after the other, in the layers of the ANN all properties can be analyzed simultaneously.
ANN is used, for example, for image processing, speech recognition, early warning systems or process optimization. It is precisely this process optimization or error detection (via image processing or audio analysis) that is already being used in companies as part of Industry 4.0.

How do you program artificial intelligence?

How a system is programmed with artificial intelligence, depends a lot on what you want the system to do. As with the programming of any computer program, the first priority is choosing the right programming language. The most commonly used programming languages ​​​​for AI are:

  • Python has become the de facto standard for machine learning and big data analysis. It is very versatile and suitable for numerous fields of application, especially in the area of ​​​​artificial intelligence and the Internet of Things.
  • Prolog is considered the most important logical programming language and is therefore particularly suitable for simulators, generators, as well as systems for diagnosis and prognosis – exactly what should often be created in AI projects. For example, the language processing components of IBM’s AI ‘Watson’ are written in Prolog. In addition to Prolog, Watson and the associated DeepQA software engine are also based on Java and C++, among other things.
  • find out why IBM is opening its Watson IoT headquarters in Munich.
  • As an object-oriented programming language, Java is very versatile and can therefore also be used well for AI programming. Java is particularly suitable for natural language processing, search algorithms and neural networks. Java is slower than C++ but easier to program.
  • C++ is particularly relevant for time-critical AI projects, as it is the fastest computer language. It is also particularly extensive and strong in statistics. This makes it ideal for machine learning and neural networks.
  • LISP is particularly strong in inductive logic and machine learning, making it well-suited for AI as well.
  • R is mainly used in the context of big data for data analysis. The programming language is mainly suitable for statistical methods and allows the free calculation of statistics and graphics.

In addition to an AI-friendly programming language, program libraries and frameworks such as TensorFlow, Torch, Keras or Caffe are required for AI implementation. The most important subprograms, routines and algorithms for AI development are already implemented here, which can then be transferred to your own AI solution.

What do we need artificial intelligence for?

Algorithmic decision-making systems are intended to support or replace people in making decisions. In the area of big data, in particular, this is necessary in order to make decisions possible at all, because here people would not be able to make any well-founded decisions based on a large amount of data alone.
Processes can be accelerated with AI support, especially with the increasing computing capacity of computers. This makes it possible to carry out tasks that humans or machines without AI could not previously handle due to time constraints.
In addition, AI-based systems, especially machine learning, make it possible to predict what will happen in the future.
In general, artificial intelligence is one of many tools to solve problems. AI makes it possible to do things that were previously not possible. It allows overcoming long-standing borders. Therefore, AI systems make a strong contribution to competitiveness.

Where is artificial intelligence used?

The areas of application for artificial Intelligence are extremely diverse. They range from spam filters and product recommendations in e-commerce or streaming services to language assistants such as Alexa or Siri and chess computers to self-driving cars. With the digitization of industry, ie Industry 4.0, many companies are also adopting AI-based systems (although not all Industry 4.0 applications contain AI in the same way).
According to the Federal Association of the Digital Economy, AI solutions are generally found in the area of ​​​​data collection and analysis in the industrial environment. Examples for this are

  • optical inspections through AI applications in the field of quality assurance, which make error or process analyzes possible,
  • Process automation in manufacturing and assembly, which includes self-regulating adjustment of control parameters,
  • Robots with artificial intelligence, which can perform even better⦁ ‘bin picking’,
  • forward-looking data analysis in order to maintain machines and systems as required ( ⦁ predictive maintenance),
  • voice control of machines,
  • Chatbots for ⦁ virtual customer service other
  • ERP systems with AI strategies that optimize internal production structures and processes.

Smart machines: How is artificial intelligence changing our lives?

Spam filters, personalized advertising on the Internet, more relevant search results in online searches, chatbots for customer service inquiries and navigation devices that include traffic jams and construction sites in the route calculation – these are all systems that work with artificial intelligence and are changing our daily lives.
In many cases, it ensures greater efficiency: while in the past you could spend hours waiting in the manufacturer’s queue because of every small question or problem with a product, today chatbots can provide quick and uncomplicated help in some cases. Cars park automatically, stays in lane and keep their distance, and recognize street signs.
Our smartphones help us write messages, correct our spelling mistakes and even predict which words we will use. And you recognize our face and then unlock it automatically.

How does Face ID / facial recognition work?

What is certain is that artificial intelligence is and will be finding its way into many areas of life. Until autonomous driving spreads it will take a while, but smart home applications (eg for saving energy) and digital voice assistants are becoming more and more popular. While only 390 million people used AI-supported assistants in 2015, in 2019 there were almost 1.38 billion. According to a forecast by Tractica, the numbers will continue to rise; By 2021, voice assistants are expected to have 1.83 billion users.
Not only are the users of language assistants such as Apple’s Siri, Amazon’s Alexa or Google Assistant increasing, more and more people are also using AI systems in general: 73 per cent of those surveyed in a Bitkom study stated that they had already used a simple application based on builds AI.
And that’s no coincidence: The majority of Germans consider artificial intelligence to be useful – 88 per cent of the Germans questioned in a PwC survey stated that artificial intelligence will help to master the challenges of the future. They consider AI to be particularly helpful in the areas of cyber security (49 per cent), clean energy/climate change (45 per cent) and protection against diseases (43 per cent).
So if AI researchers manage to develop systems that can master these very challenges, they will be a thing of the past. The technologies can help reduce traffic congestion, speed up administrative tasks, and improve medical diagnosis and treatment.

How is artificial intelligence changing our society?

Artificial intelligence has an impact on almost all areas of our society. It changes how we communicate, meet new people, consume news and do our work – in positive and negative ways.
For example, many people consume News less via platforms aimed at the general public such as television, but in social networks, where they receive personalized content with AI. From 2013 to 2019, the use of social media as a news source in Germany has increased from 18 per cent to 34 per cent; This is according to the Digital News Report 2019 of the Reuters Institute for the Study of Journalism.
According to a Kantar study, the main reasons for this are easy access to a wide range of news sources and the ability to comment on news and share it with others. While on the one hand-personalized messages are very pleasant, they also encourage the emergence of so-called ‘filter bubbles’ and the separation of different social classes. This can lead to societal problems.
Autonomous means of transport and intelligent traffic control will not only support climate protection in the future but will also create more free time for people, which in turn can be used for work or hobbies. At the same time, people’s areas of responsibility are shifting to areas in which creativity and empathy are required.

How do self-driving cars work?

Likewise, artificial intelligence will transform policing and the justice system. Face recognition will become just as common as fingerprints. And analysis algorithms will also become more relevant: Courts in the USA, for example, already use software that calculates the risk of reoffending by criminals. The calculations are based on information about the person and their radius, but also on the Analysis of all similar crimes committed.
The emergence of many new technologies is changing our society not only directly, but also indirectly. For example, according to the KI-Bundesverband, the compulsory subject ‘digital education’ or ‘computer science’ urgently needs to be introduced in German schools. Because it is irresponsible to release young people into the increasingly digitized world without active support.

Digitization | The gap between IT and OT slows

Digitization is the goal of many companies, but the gap between IT and OT is holding them back.

digitization
digitization

Each of you has probably heard this sentence very often over the past few years: advancing digitization offers enormous potential at all levels. But this is now actually becoming apparent in many ways, on the one hand through the emergence of new business models, on the other hand, through the partially implemented optimization of the organization of production and work processes. However, this undoubtedly places a lot of demands on the employees. Because this process requires, among other things, open-mindedness and openness to new things, ie not wanting to remain in tried-and-tested patterns, and where everyone really has to make their contribution accordingly in order to support the company in the transformation.

It is useful that our brain capacity is so great and consequently well equipped to meet the demands that life continually throws at us. Because of the ability of billions of neurons to communicate, it is possible to activate large networks in our brain – which in principle enables people to adapt to new circumstances. To get physical and online training in Electrical Automation and Electrical Engineering visit our website Burraq Engineering Solutions

It doesn’t work without communication

This shows that an exchange is important in order to be able to cope with difficult or current tasks. This actually applies everywhere – also, to come back to the production and work processes, between IT (Information Technology) and OT (Operational Technology). Because an existing gap between the two areas is still one of the major challenges in digital transformation. Why? Clearly – because it endangers the security of the entire company. To find a solution here, we first have to take a closer look at why IT and OT departments rarely want to talk to each other or are reluctant to do so.
One reason could be the following – they argue from different technical perspectives: IT is focused on software, hardware and communication technology, the OT on production and industrial plants, some of which (still) work in closed systems without connection to the Internet. For the IT department, the priority is to protect the infrastructure as best as possible by implementing appropriate protective measures – because, for them, security means defending against criminal attacks using the technology available. The OT department tends to think conventionally when it comes to safety, so they prefer to think first of concepts for emergency stops or continuity or the need for fire extinguishers. Colleagues from the IT department are used to fast innovation cycles – for OT this term is more of a nightmare.

So all of this makes the gulf between the two departments understandable, but nonetheless, it needs to be bridged. Because with each expansion of the Industrial Internet of Things (IIOT), more and more devices are networked with each other. This increasingly offers new options for cybercriminals to find a vulnerability for an attack and to be able to successfully implement it. Therefore, good cooperation between the two departments is not only desirable but absolutely necessary. But, very important here – it must be set up and implemented correctly.

Cybersecurity: Properly secure every access point to prevent sabotage or espionage

Digitization makes it possible to generate useful information from production data that can be used for decision-making processes at all levels of a company. For example in relation to predictive maintenance – with the aim of either minimizing or at best-preventing machine downtimes. Because the risk of a standstill can be due to timely maintenance, the timing of which can be determined by anomaly detection.
To carry out this task, data from defined data pools are accessed via specified interfaces, which can alternatively only be based on historical data sets or are constantly being supplemented with new ones. No matter which approach is followed where – the data is the be-all and end-all in the evaluation. As a result, compromising these, for example through unauthorized access from outside, could enable an act of sabotage and thus cause serious consequences. If predictive maintenance is optimized in terms of overall equipment effectiveness (OEE) through the use of remote maintenance, there are other safety-related aspects to consider.

So here I come to the sticking point for which a solution must be found: both the measures to protect data and access options and those to secure remote maintenance are managed by IT. However, from the point of view of employees in production and manufacturing, the involvement of those responsible for IT always means that they are confronted with additional hurdles and are thus hindered in their work.

My recommendation is, therefore:

  1. Take all relevant measures to protect the data: First and foremost, attention should be paid to access rights. This means only granting access to employees who actually have technical/disciplinary authorization
  2. Clearly define the tasks and authorizations of the individual departments: In the case of remote maintenance, a good approach is for the remote maintenance system to be set up and audited by IT. The setup must be carried out under the provision that the process can be initiated and controlled by the OT. This means that an OT employee can release the process without those responsible here having any influence on the configuration.

In order to be able to map this process securely, it is recommended, among other things, to use two-factor authentication to ensure that the risks of unauthorized access are significantly reduced. Because here it is necessary for each authentication process to use two factors for proof, which are combined to form an authentication chain – for example “knowledge-based factors” such as passwords with “biometric factors“, such as a fingerprint.

My conclusion

The ability to work in a team is one of the most frequently requested soft skills in job advertisements today. But in practice, this quality is sometimes neglected – which can ultimately lead to a dispute over competence because everyone wants to maintain and represent their position. Alpha versus alpha – or IT versus OT in production companies – often still seems to be part of the order of the day. But this is no longer a contemporary approach because the increasing professionalization of cybercriminals requires a unified approach from both departments.

How to work more efficiently with Robotic Process Automation

Robotic Process Automation
Robotic Process Automation

This case study shows which criteria you should definitely know for the successful use of Robotic Process Automation

A manufacturer of machine systems and special machines ensures more efficient work processes in the company with Robotic Process Automation (RPA). Together with the management consultancy ROI-Efeso, it determines areas of application for RPA in eight business areas. After only three months, the software bots complete 14 processes independently and reliably. You can find out how the experts from ROI-Efeso proceeded here.
The case study: initial situation and measures in brief
Starting position:

⦁ Data maintenance and information exchange in different formats cost time and generate errors
⦁ The process landscapes in operations, quality management, controlling and other business areas are to be redesigned.

Method:

⦁ Determination of fields of application and tasks for software bots (Robot Process Automation) including detailed recording and development in all departments involved
⦁ Immediate use after testing in practice

Solution approach:

⦁ Test phase with the real and the automated process running in tandem
⦁ Calculation of the ROI of the software bot for the sum of all use cases

Robotic Process Automation: What software bots can do

For work tasks that are very repetitive, time-consuming and error-prone, there are now smart technology assistants: In the context of digitization and Industry 4.0, software bots take on a wide range of tasks in the operations area.
Under the term Robotic Process Automation, they provide relief for simple but time-consuming work processes. In this case study, one objective was to centralize bookkeeping from five locations at the company’s headquarters without hiring additional staff. Instead, bots should take over tasks such as invoicing, dunning or the preparation of data from different sources for reporting from the first day of the changeover. The focus in the selection was on regular activities which are on the one hand so specialized that their automation in systems like SAP is not worthwhile – but which on the other hand tie up considerable time resources. 

Industry 4.0: Award from ROI-Efeso and the magazine Production

Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade newspaper PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases.
The second objective and special challenge of the project was to expand the change from manual to RPA-controlled work processes in just a few months to include operations, R&D, production and logistics. Together with ROI-Efeso, the company quickly implemented the appropriate bots. Just two weeks after the start of the project, the first bot was already saving half an hour a day with a previously manual inventory correction of shortfalls in SAP.

Testing and deploying

The project kicked off with an information event at which all employees involved got to know the deployment options of RPA. ROI-Efeso explained the limits of the technology very clearly – after all, those involved should be able to develop a realistic picture of the effort involved in introducing RPA to its benefit.
After this kick-off, the project team gathered ideas and suggestions on work processes that the employees believe could adopt RPA tools. In the days that followed, it selected 14 suitable processes from over 40 ideas in one-on-one discussions, and a detailed analysis was then carried out on each of them.

Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso

On the basis of this knowledge, the project team created an automation concept for all areas involved, in which it prioritized the most attractive processes. The selection of the RPA software suitable for the company’s requirements and the assignment of authorizations were completed within two weeks in coordination with the IT department, so that implementation of the identified work processes could start immediately afterwards.
The project team now brought the RPA into everyday life – department by department, process by process – always via the detailed recording, development, and test stations. In the test phase, the previous and the automated process ran simultaneously according to the “tandem” principle. In this way, errors could be corrected or hurdles overcome without delays in day-to-day business. Once handed over, every work process ultimately ran through the RPA, which immediately relieved the workload.

If you hate it – automate it!

In the project, three experiences proved to be particularly valuable for the implementation of RPA projects:

  1. Identify “processes in the middle”! The maxim “If you hate it – automate it!” Is a good starting point for RPA projects. It is important to classify the intended use correctly: Which use cases are so simple that the programming effort for a system adaptation is not worthwhile – but automation? Software bots are intended for precisely these processes in the middle. In sum, this is where the greatest leverage for savings lies. 
  2. Exploit software bots 24/7! The license model of most RPA providers is designed in such a way that a software bot is available around the clock on every work and public holiday. As with employees, payment is made per bot. If the bot is not busy with tasks, the invoice is still due. Therefore, you should constantly look for other, suitable work tasks for the bot.
  3. Make your expectations realistic! RPA is not a ready-made software that takes care of all extensions independently after purchase. If the process that the software bot has taken over changes, this change must be adapted. This should be clearly communicated in RPA projects in order to achieve realistic expectations of the possible uses of the bots.

4 consultant tips for your successful RPA use!

⦁ Operate targeted stakeholder communication! An RPA introduction is only successful if it is (pro) actively driven forward by the specialist department – in cooperation with IT.
⦁ Clearly define roles and responsibilities! Determine at an early stage who will monitor the bots, make adjustments or pay attention to compliance requirements.
⦁ Look beyond the existing rules and limits! RPA is a technology that helps with process optimization – but only for those who are able or willing to think process-oriented.
⦁ Build up internal RPA competencies! Nobody knows the processes as well as your employees. You should therefore create a Center of Excellence with internal consultants for the (further) development of RPA as well as for support with its application.

Careful testing saves expensive mistakes

In this as well as in other RPA projects by ROI-Efeso, controlling the previous manual process alongside the new automated process proved to be an important success factor. This is the only way to compare whether the desired results can be achieved. This also makes special cases and sources of error visible that were not yet present in the process recording. With this start-up support for each process, the RPA introduction takes a little longer – the transferred bot then works more reliably than with an implementation that is too fast. And that in turn saves time and money for troubleshooting later.

Electric car more efficient | How machine tool manufacturers are making

Mechanical engineers support the automotive industry with electromobility. What more productive machining strategies and precise components can do for e-cars.

Electrical car
Electrical car

In this article you will get the answers to the following questions:

  • How do mechanical engineers help with the technical challenges of e-mobility?
  • How do machine tools make electric cars cheaper?
  • Why are innovative machine tools increasing the range of electric cars?
  • What should machine tool manufacturers keep in mind with regard to electromobility in the future?

Tip for those in a hurry: Simply use the links to jump to the question that interests you most.
By 2023, electric cars should be as attractive as cars with internal combustion engines on the European market. This is the forecast of the VDMA study ‘Drives in Transition’, which was updated in 2019. By then, battery electric vehicles and plug-in hybrids should account for 42 per cent of newly registered vehicles in Europe. Hence there will continue to order for machine and plant manufacturers in the field of combustion engines, but the market volume for components for electric drives, in particular, will grow.
For this reason, many machine tool manufacturers are currently working on getting in shape for electromobility. It is about expanding core competencies, advancing the transformation process and thus securing future competitiveness.
“We are concentrating on the topic of e-mobility, even if it involves a great deal of uncertainty,” reports Stefan Birzle, Head of Global Account Management Automotive at Chiron. “It is not yet clear where the journey is headed and how many electric cars there will really be in the future. Nevertheless, despite the current downturn, the demand for components for e-mobility is stable or even increasing.”
The uncertainty of the vehicle market is also an issue for the plant and machine tool manufacturer Grob: “Our customers don’t yet know which way things are headed,” explains Steffen Pohl, head of the innovation management and e-mobility department at Grob-Werke. “It makes sense to position ourselves broadly, which is what we are doing.” That’s why Grob is currently primarily developing efficient assembly lines for fuel cells and electric motors. “Grob believes in the fuel cell, even if it still lags behind battery-electric vehicles in terms of development,” says Steffen Pohl. “But you always need electric motors, whether for hybrid, fuel cell or battery drives.”

How machine builders help with the technical challenges of e-mobility

With all the uncertainty about which type of drive will prevail in vehicles, one thing is undisputed among machine tool manufacturers: the components for electric vehicles and for the remaining more efficient internal combustion engines will be significantly more complex.
“The complexity of the required parts increases with the electric car and that is a challenge for manufacturers of machines and controls,” explains Jürgen Kläser, Senior Manager Application at the machine manufacturer Okuma, which offers CNC machines, motors, spindles and controls from a single source. “As the components are becoming more and more complex, our customers are demanding highly integrated machines for electromobility.” This is also due to the fact that the product cycles in the field of electric vehicles are currently still particularly short and the flexibility of the machine tools is therefore becoming increasingly important. “That’s why universal machines like Okuma’s have an advantage over special machines.”
The complexity of the parts is not only a challenge for the machine tool industry, but also for its customers. This is why Chiron, for example, also relies on advice in the field of electric vehicles. “The parts for vehicles in the field of electromobility are very special and therefore often a major challenge for our customers,” explains Stefan Birzle. “That’s why we offer our customers to accompany them throughout the project with product and process know-how – across the entire process chain.”
At the machine tool and plant manufacturer Emag, a holistic approach is also the focus, especially when it comes to the production of components for electromobility. For example, the company develops manufacturing systems for electric motor shafts in which manufacturing systems, peripheral machines and automation technology are coordinated. According to Emag, electromobility will primarily benefit hardening processes (Emag Eldec division) and electrochemical metalworking (Emag ECM).
“The parts for electric vehicles have to be light, material has to be saved and the high torques involved require particularly wear-resistant parts,” explains Gerd Killinger, Hardening Systems Sales at Emag Eldec. Hardening is good for tensile strength and protects against wear. “Inductive hardening is therefore becoming more relevant and offers Emag good opportunities to benefit from electromobility with the Mind-L 1000 hardening machine.”

Electrochemical metalworking also contributes to an optimized process chain, because the method enables different designs and processing steps than machining. For example, parts that are already hardened can be machined with almost no tool wear. This fits in well with the existing process chain.
“Solutions for electromobility are about considering the entire process chain in order to connect several stages and make process chains more efficient and shorter,” summarizes Jochen Laun, Managing Director of Emag ECM. “For our customers, this results in a cheaper overall package; for the buyer of an electric car, a cheaper vehicle.

How these machine tools make electric cars cheaper

In order for electric cars to become cheaper for end users, not only must the number of electric vehicles produced increase, production must also become faster and more efficient. “The component cycle times must be significantly shorter so that electric cars become more affordable,” emphasizes Gerd Killinger from Emag. “High quantities and a high level of repeat accuracy are particularly important and this is exactly what our production systems enable.”
“In order for electric cars to become more affordable, the component cycle times must be significantly shorter.”
Gerd Killinger, Emag Eldec
Chiron would also like to help make electric cars more affordable and supports the car manufacturers with particularly productive processing centres. “Our multi-spindle machines are definitely making their contribution to making electric cars cheaper,” affirms Chiron’s Stefan Birzle. “Because the productivity of the machines and the digitization products of our company allow a more productive manufacture of the components and thus also of the electric cars.”
An example of such a machine is the new Chiron DZ 25 P, which premiered at EMO Hannover 2019. The double-spindle machine should be particularly productive even with very large components, such as those required for energy storage boxes, without losing precision

Because not only productivity but also precision plays an important role in electric vehicles. “Production accuracy and the resulting reduction in the need for rework also reduces the CO 2 footprint of the manufactured products, for example,” explains Jürgen Kläser from Okuma.

Innovative machine tools are helping to increase the range of electric cars

Particularly precise components reduce more than just the CO 2 footprint, namely, for example, the weight as well; and that in turn affects the range of electric vehicles.
“Weight is an even bigger issue with e-mobility than with conventional types of drive,” says Jürgen Kläser. “Lightweight construction requires precision and therefore a different type of mould – Okuma machines can do that, especially when it comes to body construction.” Because in this area Okuma has already gained a lot of experience with the conversion to new production methods. “Body mould construction has always been constantly changing and precision is and will remain the be-all and end-all.”

Electrical car
Electrical car

Weight does not only play a role in the bodywork and precision play a role, also with the electric motors. Jürgen Kläser: “The ultra-high precision also brings with it an increase in efficiency in the field of electric motors, because more precise parts mean less friction and less energy loss.” And that ultimately means more range for the electric car with the same battery performance.
The particularly good thing about it is that the know-how that the company collects in the field of electric motors can not only be used in the field of electromobility but can also open up new business areas – for example, the production of electric motors for machines.
“More precise parts mean less friction and less energy loss. This ultimately means more range with the same battery performance.”

Outlook: What should machine tool manufacturers keep in mind with regard to electromobility?

New business areas, the expansion of existing and well-thought-out transformation processes will have to have a high priority for the manufacturers of machine tools in the near future. “New sales channels must also be considered because, in the course of electromobility, other partners than before are coming into focus,” reports Steffen Pohl from Grob.
At Grob, the new partners are the suppliers, because the OEMs are buying more and more in the field of electric vehicles – mainly due to the uncertainty of the market. Steffen Pohl also emphasizes that machine tool builders should above all be open and well prepared: “Change is happening very quickly and you can no longer rule anything out. You have to be prepared for all eventualities.”

Concentrated input on the topic of machine tools

Read our hands-on overview “These are the key trends in the machine tool industry”. In it, you will find out which future topics are particularly relevant for machining.

Further editorial recommendations on the subject:

  • These are the top-selling manufacturers of cutting machine tools
  • How artificial intelligence is changing jobs in machining
  • These are the 15 largest machine tools in the world
  • How sustainable machining works and what it brings

At Chiron, this ‘being prepared’ also includes monitoring the global market for electric vehicles, particularly the market in China. “Electric mobility will be decided in China,” postulates Stefan Birzle. “Especially in the Asian regions, there are many new players. That’s why you have to be wide awake at the moment to identify them and to take advantage of the opportunities that arise there.”
The VDMA also recommends in the study ‘Drive-in transition’ to take the market for electric vehicles seriously and to drive the transformation process forward quickly. It is important to establish innovation networks and identify individual opportunities to participate in the ‘electric drive’ trend. In the long term, participation in the sales market for components of electric drives is an absolute prerequisite for the economic success of component manufacturers and machine and plant builders.

Storage technology | where automation really pays off

How the right automation strategy in intralogistics can save money and significantly increase efficiency in Storage technology

Storage technology
Storage technology

When building or redesigning logistics systems, the question of the degree and type of automation inevitably arises at the beginning. Because a warehouse that is automated with foresight can be operated much more cost-effectively in the long term and also offers other advantages compared to conventional manual warehouses.
Basically, the type of warehouse is always a compromise between safety and efficiency: In a theoretical world in which every part is always available, warehouses would be unnecessary. Logistics concepts such as Just in Time and Just in Sequence are approaching this ideal, but they also show the limits, as the automotive industry recently had to painfully experience the border closings with the Czech Republic due to Corona: Here arose due to delivery delays Bullwhip effect, which ultimately messed up the supply chains from OEM to 3rd tier supplier.
Such effects can, in their two extremes, lead to a complete overload of the system on the one hand. On the other hand, it can happen that entire shifts have to be sent home due to missing orders. Both situations are sometimes associated with enormous increases in costs.

Intralogistics: How can my warehouse work more cost-effectively?

But smart, automated intralogistics can be a worthwhile alternative. In general, automation always pays off if the number of storage and retrieval is constant and at a high level. This is often the case in the B2B area, since here the flow of goods is more calculable compared to the B2C area. In addition, it must be taken into account that automation costs more money: In addition to the hardware components such as storage and retrieval machines and driverless transport systems, experts estimate that around 40 to 50 per cent on top must be calculated in order to optimally network such a logistics solution and integrate it into the existing IT and infrastructure to involve.

Industry 4.0: Award from ROI-Efeso and the magazine Production

Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade journal PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases. You can find out more about the award here.
But in addition to the classic automation solutions mentioned here, systems such as Autostore are now also available. With the help of picking robots, storage space can be saved in addition to full automation.
Of course, it should not be forgotten that these systems are also connected to the Internet and must therefore be secured against unauthorized access from outside.

If automation solutions are implemented across the group, synergies and thus significant cost savings can be achieved through standardized interfaces. In this way, a not inconsiderable savings potential can be created with a higher-level solution.

Tip: Is a combination solution worth it?

The big advantage is, of course, that with an optimally coordinated system, all costs are quickly amortized. Therefore, hybrid solutions are also recommended for systems with certain fluctuations, ie an automated area for the base load and a manual area for peak loads.

What influence does the load carrier used have

Another point that can be of crucial importance when automating a warehouse is the use of standardized load carriers: the fewer different boxes and pallets that have to be stored and retrieved, the easier it is to implement automation. It should be noted that many components in logistics, from storage and retrieval machines to driverless transport systems to conveyor and storage technology, are mostly designed for standardized transport containers. This makes planning a new system much easier and the individual components are cheaper to obtain and combine.
Such standardized systems can then be used in the automotive industry as well as in e-commerce or in the pharmaceutical industry. Of course, automation solutions are also feasible for individual load carriers. Examples of this can be found in furniture production or metal production. There, however, more engineering effort and higher costs can be assumed. But even this additional effort can ultimately pay off.

Returns management included

Finally, you should definitely take a look at the tiresome subject of returns management. You have to differentiate how far the product range of the returns to be processed goes: with large full-range stores, the individual warehouse worker is certainly not in a position to determine the quality of each individual return right away. Here, too, a certain degree of automation combined with smart technologies can help. An example of this is data glasses or special display systems at the workplace that provide the employee with important information for a return. For example, an employee who is responsible for processing returns is able to decide whether or not the product can return to the first cycle for jeans or a smartphone.

Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso

Of course, such smart solutions are of crucial importance for an effectively working system not only in returns processing but also in the order picking process in general. Here, too, automation technology can help ensure that the systems are ergonomically optimally connected to the respective employees. This has now gone so far that the employee no longer comes to the shelf, but the shelf to the employee (moving shelves), which can be achieved through fully networked transport systems. In this way, in addition to increasing efficiency, picking errors can also be avoided, ie making a return in advance superfluous.
This is the only way to achieve a high level of acceptance and the resulting high efficiency of the logisticians. It is also crucial that the employee is provided with the information they need quickly at all times. Instruments from Industry 4.0 such as 3D glasses, voice systems, picking aids and monitors, which are networked via intelligent systems, are of crucial importance and will become more and more essential in the future of Storage technology

Industrial IoT platform as a pioneer for medium-sized companies

Together with Intel as a partner, Thyssenkrupp Materials Services has not only networked its own production landscapes in one platform: the project resulted in toii, a digitization platform for medium-sized manufacturing companies.

Industrial IoT platform
Industrial IoT

The networking of machines and systems is still one of the greatest challenges in the Industry 4.0 environment. The aim is to network heterogeneous landscapes from new and, in some cases, decades-old systems (by means of retrofitting) within a common IoT-based data model. As one of the world’s largest material suppliers, ThyssenKrupp Material Services has over 4,500 production machines and systems from a wide range of manufacturers in use in its plants. The aim was to flexibly network these assets in order to increase plant productivity and create more transparency for more efficient production.
At the same time, they wanted to keep the respective individual production environments. The company did not find what it was looking for on the market: The offers of the major platform providers proved to be too complex and inflexible. That is why the decision was made to set up its own, tailor-made IIoT platform with Intel as a partner for the hardware. Thyssenkrupp Materials IoT GmbH (tkMIoT), a subsidiary of thyssenkrupp AG, was responsible for the project.

The Industrial IoT platform incorporates edge and cloud analytics

A whole range of tasks was mapped with the Industrial IoT platform, from machine data acquisition (including connection, transmission and storage) to production data acquisition from users and devices to machine automation with bidirectional communication. The platform enables the visualization of production data, for example for benchmarking, and organizes the complex data integration from different data sources, including ERP systems.
The Manufacturing Execution System was also integrated. A particularly important part were the topics of edge analysis for production optimization and quality assurance with production screening in real-time. The end-to-end platform enables the implementation of AI and machine learning at the edge of the network on-site or in the cloud.

Optimally coordinated hardware and software

Thanks to the individual modules, the platform maps many application scenarios and can be easily scaled. The solution consists of Intel servers and industrial PCs (IPCs) with the necessary storage and network resources, including gateway technology for connectivity.
With IIoT, the interaction between software and hardware determines the efficient real-time processing of data. Thanks to the intensive cooperation, tkMIoT was able to rely on the optimal combination of hardware technologies: The solution consists of Intel servers and industrial PCs (IPCs) with the required storage and network resources, including gateway technology for connectivity.

Make success accessible to other companies

The platform has been successfully implemented at more than 30 locations since 2017 and the entire range of machines and multi-level production systems, as well as the IT systems, have been connected to toii. ThyssenKrupp Materials Services was able to achieve significant advantages: Thanks to process automation, downtimes were reduced by up to 50 per cent and production increased by 20 per cent compared to the previous year. In addition, many error-prone, paper-based procedures have been eliminated.
ThyssenKrupp Materials IoT decided to use the solution to pave the way for other companies to digitize and automate production and to market toii as part of the Intel IoT Market Ready Solutions program. External customers include GGK, a subsidiary of the Grün group, which relies on the platform for extensive networking of its production. Steel Service Krefeld introduces toii. Lights to digitally network analogue machines and collect data for further processing.

What 5G really brings to mechanical engineering

What 5G actually brings in production and what advantages the technology has over WLAN was discussed at Automatica sprint.

5G
5G

A panel discussion at the Automatica sprint dealt with the topic ‘5G, WLAN and more: Wireless communication for the smart factory. Production editor-in-chief Claus Wilk moderated the talk with participants from Nokia, SEW Eurodrive and the VDMA.
It is clear that the smart factory Needs flexibility. Mobile robots drive autonomously and optimize the material flow, the highly adaptable ‘matrix production’ replaces rigid production lines. This also makes the automation of batch size 1 economical. For this, machines and devices must also be networked and they communicate directly with one another.
However, this requires wireless communication technology such as 5G. The speakers clarified what campus networks can do, what has already been technically solved and where there are still challenges. A detailed look was given above all to the first steps in the introduction as well as to existing practical applications.

“Users can set a campus network to suit their own needs. This makes them independent of public 5G networks, where you only ever get an intersection of all requirements,” 

says Tom Richter, Global Head of Discrete and Process Manufacturing Verticals at Nokia.
Moderator Claus Wilk asked whether 5G is actually the driver of networked production and thus the basis for Industry 4.0, as we all imagine and what exactly is hidden behind the campus networks.
To said Tom Richter, Global Head of Discrete and Process Manufacturing verticals at Nokia. “It’s a 5G network for a campus, which will open up completely new possibilities because everyone 5G can bring to its factory area is important that the Users can adjust the network to their own needs. This makes them independent of public 5G networks, where only an intersection of all requirements is made available. “
5G also offers the possibility of connecting machines and people wirelessly, with a performance that is only known from wire networks today.

Self-built 5G network, optimized for industry

According to Eike Lyczkowski, Funk & Navigation specialist group, SEW Eurodrive relies on a private, self-constructed 5G network, “which we operate ourselves. This is a solution that has been optimized for industry and is significantly simplified compared to what network operators do nowadays. “
Lyczkowski is very satisfied with the first performance test and justifies this approach: “We all know the trends towards Industry 4.0, Smart Factory and Matrix production. What all these things have in common is that we get more flexibility and mobility in the factory. And mobility always means wireless connectivity. Today’s WLAN reaches its performance limits very quickly and does not support mobility as well. “

Machine builder as exporter asks for certifications

When switching from WLAN to 5G, two cases take effect, as Lyczkowski described: “On the one hand, the insufficient bandwidth can be supplemented by WLAN and, on the other hand, new applications such as remote processing or edge computing are possible. If you ask people from production or logistics, what They wish they did not have access to additional data. We expect that 5G will make a lot of things easier. “
But what are the machine-builders concerns about 5G? Miriam Solera from the Working Group Wireless Communications for Machines (VDMA WCM) explained: “Of course, the machine-builders want to know which technology is suitable for industry, which costs are incurred and which added value there is exactly.” There are also questions about the certifications. “Because the machine builder as an exporter wants to know whether his machines can also be used worldwide, which frequency bands are needed and which certification processes are in place. That is why we consider a technology-neutral approach to be very important,” emphasized Solera

“What all of these things have in common is that we get more flexibility and mobility in the factory. And mobility always means wireless connectivity. Today’s WLAN reaches its performance limit very quickly and its mobility is not that good,” 

says Eike Lyczkowski, expert group Radio & navigation at SEW Eurodrive.

What is already possible today with 5G

But what is already possible with 5G today and at what entry-level costs? In addition, Richter explained that the user can start with the ‘5G stand-alone mode’, which is the target architecture for industrial applications. “Then we have the very comfortable situation in Germany that we even have a private spectrum for the campus at very affordable prices, which you can apply to the Federal Network Agency so that you can operate your own network on your own frequency. “With the end devices, the network could be put into operation.
“We assume that we are working in a heterogeneous environment. The company, therefore, needs an integration partner, possibly a transformation partner, for the implementation. The question of who operates the network and whether there is perhaps a partner for the operation must also be answered of the network. It’s complex, but any company can start with it today, “said Richter, encouraging everyone.
Lyczkowski added that it is imperative that the company’s IT department be involved in the process.

“Of course, the machine builders want to know which technology is suitable for industry, what costs are incurred and what added value there is,” 

says Miriam Solera from the Working Group Wireless Communications for Machines (VDMA WCM).

How the machine manufacturer can benefit from 5G

But how can machine builders benefit from 5G in practice? “He can network his corresponding assets on his campus, for example for the topic of ‘economic operation of the campus’ under the aspect of energy consumption. This way, the corresponding sensor data can be merged and displayed in a digital twin on his campus,” explained Richter.
There are many use cases on the shop floor when communicating from machine to machine or from machine to worker. 5G could help. “5G is also helpful when using AR and VR to support the operation of machines and to display the operating data in real-time in order to make machine control more effective or to support repairs,” says Richter with certainty.
Then, according to Richter, there is a large area of ​​the connected worker – i.e. communication between workers – because 5G also supports data, voice and video communication.
judge Further: “Finally, there is the large area of ​​the Industrial IoT, where it is a matter of networking the many more or less intelligent assets with one another. Driverless transport systems are already in frequent use today – especially when using an entire fleet with the corresponding control system. This is where conventional systems such as WLAN reach their limits. “

5G is also advantageous when retrofitting

Lyczkowski is also pleased about the use of tablets to be able to transmit videos in real-time

for the first time and to enable mobile HMIs. 5G also plays retrofitting issues in the brownfield into the cards. “Always there where I can’t or don’t want to pull a cable and still need connectivity – where I want to introduce a pick by light, for example. Where I don’t want to pull a cable and with everything that moves, 5G offers great advantages over WiFi. “
In addition to the shop floor, Solera also referred to the use of 5G in agricultural technology, mining and construction. “There, too, the machines are used – for example in the cooperation between machines in agricultural engineering and sensors in machines for collecting data and for the safety of people on the construction site.”

Standardize 5G technologies and roll them out globally

But Solera again reminded me of the different norms around the world, but of the dependence of mechanical engineering on export. “Mechanical engineering has to adapt to regulations,” she said.
Richter has a solution for this: “We bring something with us from the telecommunications industry that has been in place for over 20 years. That means that we standardize technologies, lay down the standards and then roll them out globally.”
That opens up the possibility of scaling accordingly. “That is a different way of thinking than what automation technicians still think today. With this standardization We also have the option of using the same standard around the globe with 5G, “said Richter confidently.

Artificial intelligence | startups and mechanical engineers cooperate

Artificial intelligence
Artificial intelligence

AI is playing an increasingly important role in mechanical engineering – innovation is mostly driven by startups. A VDMA study shows examples of successful cooperation.
42 per cent of the mechanical engineering-relevant AI startups come from Europe. – Picture: Blue Planet Studio – stock.adobe.com
More and more companies in mechanical and plant engineering are relying on artificial intelligence (AI) to enrich their products with data-based added value. Start-up companies are playing an increasingly important role as cooperation partners. In an analysis of the past ten years, the VDMA, together with data specialist Delphi, identified a total of 825 startups in 46 countries that offer AI solutions for mechanical and plant engineering. 42 per cent of them come from Europe – so the continent trumps both North America (33 per cent) and Asia (24 per cent) in terms of the number of start-ups.
The analysis also shows that more and more money is flowing into AI startups for mechanical engineering. Since 2015, the number of financing rounds has exceeded the number of start-ups. Almost 80 per cent of the total of 13.2 billion euros invested in AI startups worldwide from 2010 to 2020 was accounted for in the period from September 2017 to September 2020.

Europe as a startup forge for AI innovations

“Artificial intelligence for mechanical engineering will be the next stage in digital transformation. And our start-up scene in Europe is impressive. Due to the high degree of networking between regional start-ups and research networks with the vital mechanical engineering industry, Europe is a start-up forge for AI innovations in an industrial context. Germany is the centre of gravity in Europe with more than a third of the start-ups, ”explains Hartmut Rauen, deputy VDMA managing director. “Our strong mechanical engineering
4.0 domain attracts, creates dominance. As a user and provider of these startup solutions, we as a European mechanical engineering company make a significant contribution to bringing AI into the industry on a broad scale. We are therefore ideally positioned in the global race. “

AI is being used more and more in the industry

The new VDMA study “Startup Radar: Artificial Intelligence – Navigator through the global AI startup scene for mechanical and plant engineering” gives a deep insight into the AI ​​startup scene for mechanical engineering. In addition, it uses practical examples to show the importance and application potential for the industry. It becomes clear that the market for AI startups for mechanical engineering is not only characterized by high investment and funding dynamics but that the solutions are also being used more and more in the industry.
“Start-up activities are a strong indicator for the development of future markets. The investment dynamism reflects the high financial expectations that investors have of the automation of industry. With the key technology of AI, startups are taking the
next level in networked production. And mechanical engineering also recognizes this and positions itself to take the threshold to this evolutionary stage, ”explains Dr Robin Tech, co-founder and managing director of the market intelligence company Delphi, which played a key role in preparing the study.

New services and business models

As the study also shows, artificial intelligence can be used to implement efficiency potentials in various fields of application of industrial value creation and to set up new services and business models. There are currently six innovation clusters that are particularly noticeable for mechanical engineering. These innovation clusters outline areas of application in which a high number of start-ups can currently be recorded and which are of high relevance for mechanical and plant engineering:

⦁ Process Monitoring & Operational Excellence: Data-supported monitoring and optimization of production and business processes using digital twins
⦁ Product Inspection & Quality Control: AI solutions for automating quality control and testing
⦁ Predictive Maintenance: AI-based predictive maintenance to reduce unwanted downtimes and extend the life cycles of operational systems
⦁ Autonomous Factory & Process Automation: AI-based automation solutions and robots in and outside the factory
⦁ Generative Design & Product Simulation: AI-supported creation of technical drafts or simulations of production and operational environments
⦁ Supply Chain Intelligence and Demand Forecasting: Optimizing supply chains and forecasting product demand using AI

Weaknesses in the investment ecosystem

Although Europe competes well in terms of the number of innovative AI start-ups for the industrial sector, it has weaknesses in the investment ecosystem. The study shows that the US and China are in number in terms of financing and investment volumes. Of the total of 22 financing rounds in the three-digit million range between 2010 and 2020, nine were made by Chinese companies, eight by American companies, two by Japanese companies, and one each by a German (Celonis) and an Israeli company ( Innoviz). Europe, therefore, needs more approaches to allow corporate venture capital to flow into its own promising start-ups and thus strengthen the European economy. According to the study, this requires both a different attitude in companies and government measures to promote the innovation and investment ecosystem.

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