798 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 microcracks or microporosity are mostly unknown. It becomes necessary to provide material state awareness/characterization, including obtaining such data in process, as microfeatures can potentially harm the integrity of the part. This is different from the present situation, where the starting materials and products and end products can be characterized before or to some degree after fabrication, and then again after different process steps like heat treatments. To meet these needs, some challenges that must be solved are: l Real-time monitoring of the manufacturing process, by detecting thermal and acoustic emissions that can be related to the position of where the emission occurred. l High-resolution volumetric NDE techniques, like X-ray, CT, or acoustic microscopy. For nonmetallic and transparent or opaque parts, both optical or gigahertz techniques might be considered. l 3D volumetric characterization of the whole part for example, taking density or elastic modulus meas- urements and mapping the grain structure. NDE strategies are based on experience gained through making repeated measurements on similar objects and understanding the types of discontinu- ities, their significance and impact on performance, and the responses they produce when using sensors and instruments to look for the anomalies. Determination of a POD strategy is most useful for assessing the performance of an inspection involving a larger number of similar objects (Meyendorf et al. 2017b). The challenge becomes the need to address quality assurance and maintainability for unique struc- tures and components, such as those increasingly produced by additive manufacturing. In a Frost & Sullivan report (Kimbara 2015), the author made the following predictions: l The current business model for NDT inspection services is increasingly coming under threat and will change over the next few years. l While historically innovation has been incremental in the NDT industry, going forward the model will be disruptive innovation. l Organizations need to adapt and embrace the disruptive business ecosystem to be relevant in 10 years’ time (note: this statement was written in 2015!). The NDE community needs to look at how best to enable the inspection of complex and unique compo- nents, and make the ability and experience of the senior inspectors (Level IIIs) locally available at remote locations. This might be able to happen in conjunction with using modeling capabilities (“digital twins”) and providing remote viewing for example, when performing ultrasonic inspection and interpretation of results (Meyendorf et al. 2017a). The ability to enable and move toward this future will be supported by ME FEATURE w nde 4.0: challenges and opportunities Medical doctor Selects medical diagnostics methods “Machine doctor” Knows about NDE, material, and service conditions selects NDT methods Doctor makes diagnosis and chooses treatments Patient’s behavior and symptoms SHM and condition monitoring Diagnosis about continuation of use (prognostics) or required repair Technicians perform NDE measurements Nurses perform noninvasive diagnostic methods Specialists analyze this data Medical record Component life record file Level III engineers analyze this data Mirroring Component Patient Figure 2. Learning from medicine (adapted from Meyendorf et al. 2017).

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 799 innovation management systems such as ISO 56000. The inspection of unique components will require well- trained specialists that not only understand the NDE methods and inspection requirements, but also the materials and components to be inspected. The human factor and their related skill sets will need to be developed so as to significantly enhance the relia- bility of NDE results. If a specialist is not locally available, remote NDE can be utilized. “Tele-NDE” has been demonstrated, in a simple form, previously by the authors by using standard telecommunication software and PC-attachable UT instruments (Meyendorf 2017). Increasing bandwidth and 5G networks will potentially better facilitate the telepresence of NDE. This inspection of novel individual parts made using advanced manufacturing will also challenge how NDE is organized. There needs to be a funda- mental change, which can be inspired by the changes already seen in medicine where diagnostics is always geared toward the individual. However, this requires an excellent trained specialist, which is made available by an increasing use of telecommunication or tele-NDE. In Figure 2, this is seen in a concept called the “machine doctor,” who could be an NDE specialist or small team, who has expertise in the materials, design, and loading conditions of the components. This will require capabili- ties and expertise that go beyond those found in a typical Level III. The needed workforce development will be a major challenge. NDE 4.0 will disrupt the skill sets required by Level III inspectors: not only will it require the incorporation of “digital” skills, but also the addition of a much wider multidisciplinary engineering skill set. Use of the New Cyber-Physical Techniques in NDE New smart and remote technologies can impact and improve NDE in several different ways. The Internet of Things (IoT) potentially allows the networking of all machines and products. These networks can include NDE tools. NDE inspection has to be integrated into the manufacturing process for individual custom products. For process planning, designing/optimizing, and assessing inspectability, NDE modeling will be essential in applying digital twins. Modern advanced sensor networks and measure- ment tools create a tremendous amount of data. This could be, for example, the continuous measurement data created by the next generation of structural health monitoring (SHM) systems or the 3D volume data created by X-ray, CT, or PAUT. Cloud computing potentially enables capabilities to safely store, organize, and analyze the various NDE and parts-related data. Smart robots and intelligent self-learning machines could be used to assist inspec- tors and support decisions in ways that go beyond the typical inspector’s skill set. A growing database of NDE data will help to improve the decision-making process, supported by deep learning algorithms. It is important that these NDE data are seen as an “item of value.” Saving NDE, SHM, and operational data, organizing them by creating new NDE databases, and linking the data to CAD data can have significant benefits for the service teams. NDE and SHM data need to be linked and provide data that can be related to standards (Figure 3). Safety margin Change? SHM data: Detection? Location? Size? Discontinuity size Basic safety Critical crack size for unstable Crack size limit for fitness for purpose Acceptance criteria for quality assurance Recording threshold Structural and other noise Versus Figure 3. Schematic showing relationship between crack size and NDT acceptance criteria (adapted from Bond and Meyendorf 2019).