796 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 (1800–1950s), machines replaced muscle power (humans or animals), initially using the steam engine and then later electric and gasoline engines. These changes enabled a significant increase in productivity, and this period of history is well known as the Industrial Revolution. We can characterize this time period as the “age of mechanization” and call it Industry 1.0. During this time, NDT was characterized by techniques that used human senses (visual/sound) and then simple techniques to enhance the signals that can be detected by humans (including liquid penetrant testing and magnetic particle testing). The 20th century is considered to be the “age of information.” The Nobel Prize in Physics 1909 was awarded jointly to Guglielmo Marconi and Karl Ferdinand Braun in recognition of their contributions to the development of wireless telegraphy (Nobel Foundation 1967). It subsequently took 100 years for this first step to evolve into today’s cellphones and global telecommunication. However, the fundamental capabilities needed to enable this age were electricity and later electronic machines (computers), which became tools used to supplement the capabilities of the human brain. In manufacturing, these develop- ments began with the application of electricity to what created the first physical network and the introduction of conveyer techniques, which established mass production (Industry 2.0). In the second half of the last century, mass production was improved further by the introduction of electronic-controlled and automated production (Industry 3.0). This prompted the need for reliable NDE to provide 100% inspection for large quantities of parts and the quantification of NDE performance using probability of detection (POD). With the development of electronics and portable computers, it was also possible to develop automated inspection techniques and increasingly replace analog instruments with digital ones. This was essential in enabling various advancements in UT, such as phased array UT (PAUT), that we use today. There were corre- sponding advances in most NDE technologies. Both X-ray and electromagnetic testing techniques where pushed forward by developing new sensors and data analysis procedures, including image processing and computed tomography (CT) (Thompson and Chimenti 1980–2019). What Comes Next? It is the authors’ opinion that the next 50 to 100 years will be characterized by increasing capabilities to collect and manage digitized data, which are produced by various forms of big-data processing and ME FEATURE w nde 4.0: challenges and opportunities TABLE 1 Impact of industrial revolutions on NDE* Industrial revolution NDT/NDE NDE techniques introduced 1st (1750–1850) 1.0 • Visual tesing • Mechanization • Using human senses for • Acoustic emission techniques • Replacement of muscle power random inspection • Unique components 2nd (1850 –1960) 2.0 • Liquid penetrant testing • Mass production • Enhancing detectability of human senses • Magnetic particle testing • Assembly lines (for instance, for surface-breaking cracks) • Electrical energy • 100% manual inspection of selected safety • Identical components relevant parts 3rd (1960–today) 3.0 • Radiographic tesing • Automation • Using physical effects, radiation, or fields • Ultrasonic testing • Electronic control and data processing to detect discontinuities, measure material • Electromagnetic testing • Multifunctional microelectronic systems properties • Manual or automated inspection • 100% inspection of large quantities of parts 4th (next) 4.0 • Computed tomography • Cyber-physical systems • Use of cyber-physical systems • Phased array ultrasonic testing • Learning and decision-making machines (cloud computing, Internet of Things, • Optics • Individual custom-tailored components modeling) • Thermal/infrared testing • Continuous monitoring of manufacturing • Terahertz processes or components in service • Large-volume data files (3D images) *adapted from Meyendorf (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 797 artificial intelligence. These changes will be repre- sented by: l Digitizing of all (or much) of our information, which can be stored effectively forever. l Information networks that allow not only real-time telecommunication, but also remote control of processes and activities anywhere in the world. l Smart robots that can interact with humans, including much beyond keyboard control. l Machines that have the ability to learn and make decisions on their own. l Exponential convergence of atoms, bits, qubits, neurons, and genes, which also includes the merging of cyber-physical and biological systems. Due to the continuous increase of the miniaturiza- tion of electronic circuits (successfully described by Gordon Moore as “Moore’s Law”—the observation that the number of transistors in a dense integrated circuit will double about every two years [Moore 1965]), together with a significant decrease in the price and energy consumption of circuits, it can be expected that there will be computers that have the computational power of the human brain within just a few years. Already, a lot of human functions and decisions can be replaced by computers however, in the future, advanced “smart” devices will be able to learn and adapt or respond to new situations. Machines will become “smart” with decision-making capability, if not sentient. In NDE this means, for example, that there is the potential (for at least some routine inspections) to have the initial characteriza- tions of parts and potentially initial data evaluations performed by smart inspection robots. Such smart robots can then potentially enable new applications for example, allow for operation in harsh environ- ments. These capabilities can even be controlled remotely from anywhere in the world. Such advances don’t mean that we will not need NDE inspectors in the future, but tools will be available that can remove some of the tedium of routine tasks, such as viewing X-rays or C-scans, and enable the smart technology to focus the inspector’s attention to anomalies identified automatically, improve POD, and give senior inspec- tors (who are often a scarce resource) the time needed to address higher-level review and even reinspection tasks. Introducing smart NDE to the new industrial age will require the NDE community to make techniques ready for use in advanced manufacturing and the new approaches in production, by using the capabilities that will become available with new cyber-physical techniques. In the following sections, two aspects of these changes will be discussed. Develop NDE Techniques Ready for the New Age of Smart Production The fourth industrial revolution (Industry 4.0) is driven by trends that bring together collaborative advanced manufacturing networks (networks of advanced manu- facturing devices controlled by computers) and combining them into a physical-digital environment. This new age is characterized by the “smart factory.” This means that there is communication among the machines and between the products and machines. This new manufacturing philosophy and technology potentially enables the production of customized individual parts for example, by use of 3D printing/additive manufacturing. We can then poten- tially, for some applications, say goodbye to conveyor belts and traditional mass production. Each component or small batch can then potentially be individually tailored to meet the specific requests of the customer and be manufactured on demand, which will also impact inventory and production logistics. Such a change will impact the entire value chain from raw materials to end use, including through to recovery/recycling (circular economy), and with these changes, advances in design and manufacturing, including customization, will also impact business and support functions (such as supply chain and sales). For next-generation quality management (QM), this requires a paradigm shift. Until today, we used estab- lished optimized process chains. QM is characterized by statistical process control, statistical quality planning, and commonly the destructive testing of random samples combined with NDE, particularly for higher-value items. Such NDE has always been an integral part of QM in specific high-technology indus- tries, such as aviation, energy production, and trans- portation. In the future, the trend is moving toward production “on demand” with the delivery of customer-configured objects produced by additive and subtractive manufacturing technologies in combina- tion. This step change in manufacturing requires a new paradigm for quality assurance with capabilities that employ integrated intelligence and self- learning/teaching smart systems. Statistics-based random destructive testing is simply not possible for many small-batch additive products. “Sample sets” may consist of just one item, and it can be both unique and an item of high value (Wunderlich 2016). As smart manufacturing evolves, there will be a need for 100% NDE inspection for many cases where parts are safety critical. Such new manufacturing techniques will also require new approaches and implementa- tions of NDE methods. For example, if metallic or ceramic parts are created in a 3D printer, the material’s microstructure and volumetric defects like