854 M A T E R I A L S E V A L U A T I O N • J U L Y 2 0 2 0 NDT + Industry 4.0 = NDT 4.0? Figure 3 shows the different fields of Industry 4.0, which also apply to the NDT sector (Singh 2019). This section will assess some of them and will provide real examples of how they already impact NDT today. Due to space constraints, this assessment cannot be holistic, but it should provide a good overview. Robotics First, we will investigate robotics and simulations that allow repetitive handling tasks to be automated. This enables higher throughput, lower inspection costs, and higher process safety. Figure 4 shows an example solution where three robots work jointly to inspect airducts and pipes used in the aerospace industry. This team of robots collaborate together, sharing the tasks of part handling and inspection. This way, cycle time is effectively reduced from several hours to several minutes. When an operator wants to inspect a part, a barcode is scanned and the system automatically loads the applicable parameters and part holders. This results in a healthier work environment for operators, where heavy and potentially dangerous tasks are performed by machines. All images are archived under a serial number and full traceability is given. Image quality is always supervised as the system performs automatic long-term performance evalua- tions according to ASTM E2737 (ASTM 2018). New programs can be programmed offline, including the option to use a CAD/CAM simulation tool, so that the system can be utilized 100% for production and does not need to be shut down for engineering purposes, thereby significantly increasing system utilization and throughput. To further optimize the process, X-ray technicians can simulate the X-ray images digitally before even loading the part into the system. This allows operators to easily check the inspectability of the part and establish the right X-ray parameters very early in the process. Figure 5 shows a real X-ray and a simulated image. It is clear to see that the results are very closely correlated. The usage of robotics is enabled through the introduction of digital detectors that replace traditional X-ray film. This shows clearly how the single steps of the industrial revolution are building on one another. Without digitizing the image acquisition process, the improvements represented by robotics alone would be marginal. This is a great reminder of the incremental nature of implementation. For companies that want to enter the age of NDT 4.0, it is important to analyze the status quo and then create a clear roadmap where innovations are introduced in a meaningful sequential order. ME TECHNICAL PAPER w ndt 4.0: opportunity or threat? Industry 4.0 System integration Internet of Things Cybersecurity Cloud computing Additive manufacturing Big data Autonomous robots Simulation Augmented reality Figure 3. Different fields of Industry 4.0. Figure 4. Inspection by inline robots.

J U L Y 2 0 2 0 • M A T E R I A L S E V A L U A T I O N 855 AI and Big Data Another significant focus of Industry 4.0 includes AI and big data. These concepts are realized, for example, as ADR in radi- ography (VisiConsult 2020b). Indications like porosities, cracks, and inclusions are automatically detected, measured, and evalu- ated against the inspection criteria. Already widely adopted by the automotive industry, the author absolutely foresees other industries like aerospace following in these automation foot- steps. During this revolution, it often makes sense to adopt an assisted defect recognition strategy, where an AI implementation supports the human operator by cross checking or aiding in the decision-making process (Perner et al. 2001). This approach is also called supervised learning in data processing, and it allows rapid training of the underlying AI system. As soon as sufficient data is collected that can be correlated to decisions by the operator, the AI software can build the required proof for qualification of the ADR system by using probability of detection (POD) methods (Kurz et al. 2013). With increasing computational power, AI reveals its power. Figure 6a shows an example where ADR was used to evaluate a digital radiograph of an automotive casting. In order to implement automated evaluation algorithms based on AI, one needs to have a huge amount of data that can be used to train the neural networks and to perform machine learning. Therefore, it is highly recommended that companies start to collect and archive as much data as possible. In our case, this would be X-ray images along with the decision and discontinuity classifications. Before starting this process, it is important to consult with a subject matter expert in the field of image processing about establishing suitable data formats to make sure the data is machine readable. Typical neural networks require thousands of images to be trained and verified. Additive Manufacturing and CT Additive manufacturing (AM) means that parts are manufac- tured layer by layer rather than by subtractive means. One example would be parts that in the past have been carved out of blocks through computer numerical control (CNC) milling machines (subtractive manufacturing) and can now be built in an additive way using 3D printers. Typical materials include different metals and plastic. AM also allows users to build futur- istic shapes through generative design. The downside of these capabilities is that AM parts have a huge need for inspection. Due to the novelty of the manufacturing process, the industry is still lacking proven NDT standards, which are currently under development by ASTM and other committees. When looking into the inspection of AM parts, RT plays a large role. Industry experts have stated that CT is one of the leading technologies that can sufficiently inspect complex AM parts and qualify them for safety-critical environments (du Plessis et al. 2020). Figure 6b shows a scan of a tensile probe the upper part shows the horizontal cross-sectional view, and the lower part shows the vertical cross-sectional view. By acquiring substantial amounts of digital radiographs and computing them into a 3D model, we can gain information about parts like we have never been able to before. It is also possible to conduct advanced analyses like actual-nominal comparison, porosity analysis, and metrology. With increasing computational power, we currently see CT moving from a lab environment to the shop floor. This allows the implementa- tion of in-line CT systems that perform a 100% inspection of parts while at the same time checking the geometric tolerances. AM also plays an additional role in NDT. For many inspection methods, there is a need for precise fixtures and part holders. These objects can be easily manufactured through plastic AM printers. For many years, foam blocks and other crude tools have been used to position parts in the X-ray beam. By using 3D-printed fixtures, the setup time can be reduced significantly. This technology has been widely adopted, as entry-level plastic 3D printers are affordable and easy to use these days. Figure 5. X-ray image of a part: (a) simulated (b) real. (a) (b)