Mann (Hexagon): AI needs to be set up properly so that the user can end up using it and actually benefiting from easier handling or faster cycle time. If there’s too much to do with AI training and making sure that the right and enough data is there than in most cases the full benefit isn´t achieved for the customer.
Wojek (Zeiss): You need to look at who is working with the system. The user, at an inline application, will not be training the AI. But we definitely want to make it possible for the people who are dealing with quality in the process or training these systems. Therefore, the tools must be very easy to use. You don’t want to deal with the statistics and the fine-tuning of the parameters, this is more for data scientists, but I’m optimistic we can get to this level.
Is AI only used for slow applications such as in the measuring room or is it already used for shop floor or inline applications?
Wojek (Zeiss): It’s both, but the focus is definitely a little bit different. When it comes to the measuring room, the most important thing is ease-of-use and interaction. You have someone in front of the system double-checking what the outcome is. When it comes to inline or shop floor automation, speed and high performance are more important.
Mann (Hexagon): When we look at the lab, where someone is really striving for absolute precision and very fine detail, it’s the right tool to get more out of the data that you have. On the other hand, if you take enough time to train the AI well for use in inline applications, you can use some of the precision gained to cope with potentially lower quality data due to long cycle times and still achieve good results. So one thing is to focus on gathering the data quickly and still getting precise enough results and on the other hand, it’s about achieving the greatest possible precision.
Schulenburg (VisiConsult): To be clear, the main value proposition of AI tools is to increase efficiency. That means increasing throughput and reducing the inspection time and effort. Of course, the importance for such efficiency increases are bigger on the shopfloor and in the laboratory, but today nearly everywhere NDT resources are a bottleneck and efficiency highly sought after. Our AI tools are already used in the production lines on so called in-line systems, but also off-line in the measurement room. With the right hardware, there is no limitations in applying AI to any of these settings.
Schulenburg (VisiConsult): The holy grail of our industry is to automatically create the perfect measurement plan within seconds without any human input. Particularly in CT, if you have high density areas or very thick sections, your CT system could take that into account and generate a trajectory and maybe even use some exotic inspection trajectories to find the best possible projections through the part. It is very difficult for a human to choose the perfect trajectory for a CT system. If you have an AI that actually optimizes the trajectory and chooses the right projections automatically, there’s a huge potential for better and faster scans. We’ve proven in our lab that AI-powered trajectory generation reduces the number of projections by a factor of ten without sacrificing quality.
Are AI-based metrology tools meanwhile good enough or do humans still have to make the final decision about whether or not a part is good?
Schulenburg (VisiConsult): Many factors play a role, starting with standards and regulations. The first question is: Does the standard allow an AI or computer-based system to make the decision? In many industries, like oil and gas, defense or aerospace it is a no-go. And then there is the customer specification and the technical feasibility. If the measurement results are repeatable, precise and reliable, there is absolutely no reason why the machine cannot make a final judgment. This has already been the case prior AI by using classic image processing. In other cases, the regulatory framework may make this quite impermissible. What is possible in any case, is deploying AI as an assistant tool to augment the inspector. Leading to higher efficiency and quality.
Wojek (Zeiss): We’ve done something like this before with traditional algorithms. Sometimes it didn’t work well, and in many cases AI is now a plug-in replacement. You create segmentations using AI and then do everything as before. For example, you set your standards, evaluate against the standards you have, and then you have to adhere to those rules whether you use AI or not.
Mann (Hexagon): It mainly depends on the regulations whether it is allowed or not, because there can be certain inaccuracies with AI. But if you look at human operators, the skills aren’t perfect either.
Schulenburg (VisiConsult): When AI has been brought to the table, people say, well, it’s a black box, which I can’t trust because I don’t know how the decision was derived. What I like to ask in that case: Can you look into your operator’s head and can you see what they’re thinking? Why do they make their decisions? Because a natural intelligence system, i.e. humans, also makes decisions in a black box based on experience. An AI makes decisions in a black box based on experience as well. Both are qualified based on statistical parameters such as probability of detection (POD) or an MSA study. This is a major transformation in how we thin about decision systems and we will see it reflected in the newest inspections standards that are created right now.
Will AI make it easier to use metrology systems in the future, because it is becoming more and more difficult to find the right people for metrology issues?
Wojek (Zeiss): The same applies, for example, to setting up CT scans and tactile systems, where a measurement strategy is generated from the specification. This is a task that AI can tackle and probably will do in the future.