Professor Hanke, how would you define nondestructive testing?
Until a few years ago, nondestructive testing (NDT for short) was taken to mean the process of examining components and products for quality defects without having to take them apart or destroy them. That’s still the case today, but the definition has become a little more
Our current view of NDT is restrictive and incomplete, there’s no doubt about that, both in terms of the solution of NDT problems and in the range of possible application for NDT techniques. Imagine you’re in Munich on business and you’re invited to the Oktoberfest. You’d look completely out of place in a suit and tie; what you need are authentic German Lederhosen. So, you go ahead and order a pair on the Internet, wear them to the beerfest, and impress everyone with your dress sense. At the end of the evening, you take off the Lederhosen and hang them in the closet, where they will remain unworn for 364 days. Some might hit upon the clever idea of sending the Lederhosen back to the store where they were bought. And this brings us back to nondestructive testing: when they receive returned goods, vendors need to inspect their condition to determine
whether they are still salable. Nondestructive testing methods can be easily adapted to such tasks. This is a huge market for sensor technology, but nobody would describe it as nondestructive testing in the usual sense.
So, you see new markets opening up for nondestructive testing in the future?
Absolutely! Just look at the product lifecycle: A product goes through a period before manufacturing and a period after manufacturing. It all begins with the raw material and, sooner or later, ends up with the recycling or reuse of the product. Inbetween, the product goes through a whole series of value creation phases, including trade, transport and e-commerce. What you have to consider is where there might be more customers and potential users of NDT technologies, specifically, what issues are of importance to these customers and what solutions we can offer. These are by no means restricted to production scenarios.
What do you think customers want?
The first question you have to ask yourself is what customers don’t want. I think it’s safe to assume that most customers don’t have any great interest in test systems in the conventional sense. Customers want solutions that are smart, offer added value, and help them to optimize their processes. Such solutions might use, for example, cognitive sensors that are smart enough to determine what data needs to be collected and analyzed to deliver the information needed to make the right decisions. This principle applies to every business sector, any process, and any imaginable task. Our collaborative project with the startup company Mifitto is a good example. We were asked to find the most efficient and cost-effective way of extracting digital data on the internal dimensions of thousands upon thousands of different pairs of shoes so that online shoppers can be sure of choosing the right size. We were able to deliver the necessary information based on precise, high-speed computed tomography data, and we also created a further significant source of added value by combining highly accurate X-ray data with intelligent software. As a result, Mifitto could advise its customers not only on size alone, but also on what shoe would provide the optimum fit.
What do you mean by "smart" in the context of data analysis?
Smart monitoring is the key term here. In the future, it won’t just be a case of deciding whether a product is good or bad. Rather, it will be about providing customers with a monitoring system that shows them how they can optimize their processes. And by process I don’t just mean the conventional production process – I’m talking about the materials development, design, maintenance and recycling processes as well. This has
triggered a shift in our research focus. In the future, we won’t just be testing, we’ll be sorting, characterizing, monitoring and checking as well – in short, imitating the human brain that uses the body’s sensory system to extract information and/or adapt its responses to sensory input. As a result, we will be developing cognitive and self-adapting sensor systems that will be able to decide for themselves what they measure when, where and how, along with monitoring and characterization processes etc.
How does nondestructive monitoring fit into Industry 4.0?
At the moment, work in the field aims to apply self-teaching algorithms to extract information from a huge quantity of data information that allows us to better understand processes, observe and optimize them. These days, when people talk about big data, they are talking almost exclusively about factory data, logistics data, cost data, machine data and so on. What we’ve hardly considered so far in relation to big data is what we call smart materials data. In the future, we will monitor the evolution of materials and products across the entire value-added chain, that is to say, the complete lifecycle, from raw material through usage all the way to recycling – in fact, any stage at which people, machines or the environment change the material, component or product in any way. We won’t just collect material data in bulk or at random, but pick out the relevant data. As to what data is judged relevant or smart, that will be up to the smart measuring system itself, the cognitive sensor system.
In the future, I could well imagine a scenario something like this: The customer is receiving a smart monitoring system – let’s call it a Black Box. They don’t need any knowledge of NDT processes. The Black Box contains all the necessary robots, which have access to various sensor systems and use these to decide themselves which sensors they use to solve a given task.
This all sounds like science fiction. Do you really believe that such visionary ideas will soon become reality?
Absolutely! After all, this mimics the way human beings work. Everyone has a body equipped with different embedded sensor systems and a brain to process the data and control the body’s responses. Each new task is processed with a moderate degree of attention. As soon as an event is registered indicating that something isn’t right, we become more alert. We activate more of our senses and try to focus harder on what our eyes and ears are telling us. In other words, we humans always apply a nuanced approach to sensing, using our intelligence. That is the standard we ought to be aiming for in nondestructive testing, too.