FOCUS ON AI
Korbinian Weiß is training the AI with over 100,000 images captured in the TruLaser Center 7030 ’ s enclosure .
Image : TRUMPF .
Installing miniature cameras in a TruLaser Center 7030 .
Data meets AI
But what role does AI play in the TruLaser Center 7030 as it slices its way through one sheet after the next ? And how do we define AI in this context , anyway ?
AI is a research field with numerous subspecialties , one of which is machine learning . For machines , such as the TruLaser Center 7030 , to “ learn ” – in other words , to get better and more efficient at what they do – they need tools and methods in the form of appropriate software . Computer vision is one of those methods .
“ Ninety-five per cent of the solution is data , and just five per cent is AI ,” confirmed Weiß , explaining where the team had to focus its attention . “ The challenge was collecting the data in the first place , curating and labelling it , compiling datasets to meet different problemsolving goals and getting the right balance within the data .”
Sometimes traditional algorithms are all that are needed to solve a customer ’ s problem , but often it takes more . A lot has happened in computer vision since TRUMPF launched its Sorting Guide in 2020 . As well as improvements in the technology and algorithm databases , there has also been a shift in people ’ s mindset .
“ Nowadays , we ’ re thinking about data from the moment we start developing a product ,” said Weiß . That is why the TruLaser Center
Image : TRUMPF .
7030 contains cameras and also why TRUMPF now has access to entirely new business models , one of which is ‘ Pay Per Part ’.
Sorting Guide solution
Thirty-seven-year-old Weiß trained as a mechanical engineer , but he began developing software-driven initiatives not long after joining TRUMPF . One of the projects that he led was the TRUMPF Sorting Guide , which did not originally include AI in its plans . The project team initially thought the Sorting Guide would work on the basis of conventional algorithms .
“ Everything seemed great in our test facility ; the results were fantastic ,” said Weiß . But then he took it to the test customer “ and nothing worked ”. The problem was the lighting . The algorithms were overwhelmed by the mix of light and dark materials , the reflective surfaces and all the objects in the visual environment .
Image : TRUMPF .
“ Without AI , we wouldn ’ t have been able to cope ,” said Weiß .
To train the AI , the computer vision team had to manually classify and label 100,000 images . By telling the system whether a sheetmetal part was visible or not in each image , and using the corresponding algorithms , the team was able to teach the software to continuously improve the accuracy of its predictions in multiple training loops .
Remote night shifts
In this business model , TRUMPF ’ s fully automated flagship is based at the customer ’ s site , where it produces the required parts . However , control of the machine lies in the hands of a TRUMPF team at the Neukirch site in Saxony ( Germany ), which operates in three shifts ( including at night ).
The cameras keep the team updated on
The TRUMPF computer vision expert is happy to explain how AI can improve sheet metal cutting .
40 | ismr . net | ISMR December 2024 / January 2025