How artificial intelligence optimizes laser processing

Artificial intelligence is currently a hot topic in all sectors – including laser technology. How to choose an AI solution for better processes.

laser processing
laser processing

How can data and algorithms be used to increase the quality and efficiency of processes in laser processing? Researchers, laser technology manufacturers and users are asking themselves this question at the same time, because with the emerging Industry 4.0, with solutions artificial intelligence (AI) are also becoming increasingly important. The first solutions in the field of laser technology that uses artificial intelligence already exist.

How is artificial intelligence used in laser material processing?

For example, machine builder Trumpf uses artificial intelligence to control laser systems using voice commands. With the system equipped with a marking laser, the system operator can issue all relevant control commands such as ‘Open/close the door’, ‘Start the marking process’ or ‘How many products have you marked today?’ speak directly into a microphone. The laser system responds accordingly and executes the voice command. You can find out more about the machine in our article ‘Trumpf: AI moves into laser material processing ‘.
However, significantly more researchers and manufacturers are dealing with AI systems in the field of quality assurance – some AI applications are already being developed, especially for laser welding. Most of the time it is about the quality control of the welds. The Fraunhofer Institute for Laser Technology (ILT) is also researching in this area.
“We want to use our AI solution to recognize different quality categories in laser welding,” reports Christian Knaak. The scientific employee of the Fraunhofer ILT deals with Zero Defect Manufacturing and in this context with Deep Learning and spoke about it at the first ‘AI for Laser Technology Conference ‘ in Aachen.
“We want to detect seam collapse, binding errors and incorrect seam widths,” Knaak continues. “And we also want to recognize when the seam is in order.” For this purpose, the measurement data from welding processes – in this example the camera images of the weld seams – are evaluated and the system is then supposed to make statements about the quality of the seams. There are two possible approaches: the classic machine learning approach and deep learning.

What do I need for a successful AI project?

“AI needs a sustainable database. And a team that implements the project together and in which the individual members have the necessary specialist knowledge from the departments involved.”
Christian Kohlschein, Hotsprings GmbH
“Machine learning sometimes needs bad process results. If too many results are good, then the algorithm says that everything is good. But then the AI ​​​​solution is useless.”
Stephan Schwarz, Mercedes Benz AG
“The biggest challenge when introducing AI solutions is the cultural transformation of the company. Faster technology and better algorithms alone cannot make a difference.”
Benjamin Kreck, Microsoft
“When selecting suitable algorithms for quality analysis, the domain knowledge of the employees is required. This is the only way to find suitable classifiers and algorithms.”
Christian Knaak, Fraunhofer Institute for Laser Technology

What is the difference between classic machine learning and deep learning?

In machine learning, knowledge about the area of ​​​​application (so-called domain knowledge) plays an important role. Image processing algorithms are used to extract and classify features from the camera images. “We have five classes,” explains Knaak. “These are typical images of welding tests with examples of lack of fusion, seam collapse, increased night width, OK seams and no seam.”
The features of these images are then used as a ‘fingerprint’ for the various seam conditions to be recognized by an algorithm. The appropriate algorithm is selected by determining the recognition rates of various algorithms using a training data set. The algorithm with the best detection rate can then be used for the process data.
Deep learning requires less domain knowledge but requires significantly more data. “The special thing about this method is that the task of extracting features, which was taken over by image processing algorithms in classic machine learning, is now handed over to a neural network,” explains Knaak.
This neural network recognizes the features and processes the data in several layers. This makes it possible to analyze very complex image features. “The end result is the same as with the classic approach, where you need a relatively large amount of domain knowledge,” reports Knaak. Both methods are therefore suitable for analyzing the seam quality, only the prerequisites differ.

The 6 most important IT trends for 2022

Companies that want to catch up on IT must now make the right strategic decisions. The IT trends in 2022 will help.

⦁ Unlimited interaction, networked intelligence, self-learning data systems, limitless modularity, self-optimizing DevOps and a zero-trust architecture are the decisive trends for 2022 and in the years to come.
⦁ A conventional IT architecture, which delays rapid progress, is still the rule in almost half of the companies. In addition, some technical skills are lacking.
⦁ Companies can catch up if they set strategic courses, quickly define ambitions, operating models and infrastructure, and create clarity with regard to the use of resources and the transformation process.

IT Trends for 2022
IT Trends for 2022

Even after the corona pandemic, many companies primarily want to advance the digitization and automation of processes.

Alphabet, Amazon, Apple, Microsoft, Saudi Aramco: A look at the industry mix of the five highest capitalized companies worldwide is enough to see the central importance of modern technologies at the beginning of the 2020s. And the digital revolution continues. A survey by the World Economic Forum has shown that even after the corona pandemic, companies are giving priority to the digitization and want to drive automation of processes. They know that they can only meet customer requirements promptly if they use state-of-the-art technologies and IT trends stake up early. And that this is the only way they can tap new growth opportunities and at the same time increase their efficiency over the long term.
But what are the key trends? From Bain’s point of view, the current trend is towards unlimited interaction, networked intelligence, self-learning data systems, unlimited modularity, self-optimizing DevOps and a zero-trust architecture. In some cases, corresponding applications are already in use. It is above all digital attackers who are driving entire economic sectors such as banks or the energy industry in front of them. You have recognized the extent to which innovative technologies will shape the world in the 2020s.

Trend 1: Unlimited interaction

The customers of tomorrow use an unlimited number of devices and interact with companies via many channels. In the future, apps will only be one access among many. The devices of the future understand language and gestures as well as their context and react to looks as well as body signals. Companies have to adapt to this and, for example, use open interfaces to guarantee access across all devices and a continuous exchange of data. Since numerous different devices are now available to customers, the concept of customer channels (omnichannel) is becoming more complex by dimensions. Only with decentralized processing of data (Edge Computing) companies will be able to cope with the flood of information in the future and derive added value from it, especially since the devices of tomorrow will also interact with each other.

Trend 2: Networked intelligence as an IT trend for 2022

The deployment and use of artificial intelligence (AI) is still mostly the responsibility of small teams of specialists. But in the future, AI will be part of every customer interaction and every process in the company. Artificial intelligence now connects all structured and unstructured information in real-time and thus enables offers and services that are really personalized. As a result, digital technologies will finally become the core of almost all business models, which in turn makes it necessary to redefine the term specialist area. This also raises the question of how and delimit operational units in the future or dissolve existing transitions

Trend 3: Self-learning data systems

For years, companies have struggled to generate real added value from the flood of data. Innovative technologies are now helping them to do this. They generate, move, store and use data in real-time across different systems and are constantly learning. Previously separate data flows into one data lake together, where new methods provide structure and access, but above all a meaningful linkage of information. With distributed ledgers or blockchain technologies, an alternative form of storing information is also gaining importance.

Trend 4: Limitless modularity

the 2020s knows no more borders. Modular components combine to form applications and are infinitely scalable. Various interfaces blur the boundaries between internal and external systems, which makes work easier for IT trends for 2022 users. Function-as-a-service models are increasingly part of everyday life. Behind this is a decentralized multi-cloud infrastructure.

Trend 5: Self-optimizing DevOps

Agile working methods and DevOps, ie the interlinking of Software development and IT operations are already common practice in many companies. Now the next wave is rolling in. In the future, DevOps will only be part of a comprehensive XOps landscape for all applications up to control and protection (SecOps). Codes that modify themselves relieve programmers of some of their work. The usual sprints and iterative progress are replaced by a continuous optimization process.

Trend 6: Zero Trust Architecture

There are increasing signs that cyber-attacks will reach an even greater extent than before in the coming years. Companies must therefore continue to upgrade. In the end, there is a so-called zero trust architecture in which every external input is viewed with suspicion. Strict authentication processes and a federal identity will make external access more difficult.

Deficits despite significant progress

In terms of security and DevOps in particular, there has been significant progress in many places recently. But in the eyes of most of those responsible, these are not yet enough. A global Bain survey of more than 200 IT executives in 2021 showed that just 14 per cent consider their company to be a technology leader. 39 per cent attest that their company has at least a modern IT architecture and a contemporary operating model. On the other hand, almost half see deficits. 22 per cent of the companies are still working with a conventional, rather cumbersome IT architecture, while 25 per cent lack basic technical skills.

Set the right course now

The flexibility and modularity of modern IT trends for 2022 systems make it possible to eliminate such deficits step by step and to catch up with the technology leaders. In order for this to succeed, CIOs should set the course quickly. This is especially true for the following aspects:

  1. Ambition: The central question is: Does a company rely on incremental improvements or does it need the much-cited green field restart? In practice, the development of new business models with new systems often runs parallel to the further development of IT for the previous business.
  2. Operating model: Following the ambition, companies can venture a fresh start with separate IT and a modern operating model or gradually modernize their existing systems.
  3. Migration: A step-by-step renewal of the existing IT trends for 2022 is just as conceivable as the construction of a modern IT architecture and a migration on day X.
  4. Skilled workers: Companies have the opportunity to develop and supplement the existing workforce or to expand their know-how in one fell swoop through acquisitions or partnerships with IT service providers.
  5. Financing: Basically, companies can finance the modernization of their IT from efficiency gains achieved up to then or from a separate budget. But given the central importance of IT, the idea of ​​self-financing is quickly dropped. Companies do better when operational units and IT come to a consensus on the necessary initiatives and provide the necessary financial resources.
  6. Speed ​​of change: A gradual modernization of IT usually takes three to seven years, depending on the initial situation. Who the transformation Giving priority can get there within two to three years.
  7. Implementation: Either a dedicated transformation team or the existing IT department can drive the conversion. The higher the complexity, the easier it is to bundle competencies in a separate team.
    The decision whether to gradually rebuild or at least partially start over depends on a number of factors. This includes the competitive position of a company and the pressure to change on the market as well as financial and human resources. Sticking to the status quo is out of the question, however. Because of the trends mentioned, the importance of modern IT for the competitiveness of companies is once again growing significantly. With the right technological basis, they will be faster, more efficient and more expedient. Therefore, technology-driven companies are likely to lead the ranking of the stock market values ​​with the highest market capitalization in this decade as well.

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