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      Cyber-Physical Loops as Drivers of Value Creation in NDE 4.0

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          Abstract

          Across so many industries, non-destructive evaluation has proven its worth time and again through quality and safety assurance of valuable assets. Yet, over time, it became underappreciated in business decisions. In most cases, the data gathered by NDT is used for quality assurance assessments resulting in binary decisions. And we seem to miss out on value of the information content of NDE which goes way deeper and can help other stakeholders: such as engineering, management, inspectors, service providers, and even regulators. Some of those groups might not even be aware of the benefits of NDE data and its digitalization. Unfortunately, the NDE industry typically makes the data access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be addressed now by the NDE industry. The confluence of NDE and Industry 4.0, dubbed as NDE 4.0, provides a unique opportunity for the NDE/NDT Industry to not only readjust the value perception but to gain new customer groups through a broad set of value creation activities across the ecosystem. The integration of NDE into the Cyber-Physical Loop (including IIoT and Digital Twin) is the chance for the NDE industry to now shift the perception from a cost center to a value center. This paper provides an overview of the NDE ecosystem, key value streams, cyber-physical loops that create value, and a number of use cases for various stakeholders in the ecosystem.

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          Most cited references12

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          NDE 4.0—A Design Thinking Perspective

          Cyber technologies are offering new horizons for quality control in manufacturing and safety assurance in-service of physical assets. The line between non-destructive evaluation (NDE) and Industry 4.0 is getting blurred since both are sensory data-driven domains. This multidisciplinary approach has led to the emergence of a new capability: NDE 4.0. The NDT community is coming together once again to define the purpose, chart the process, and address the adoption of emerging technologies. In this paper, the authors have taken a design thinking approach to spotlight proper objectives for research on this subject. It begins with qualitative research on twenty different perceptions of stakeholders and misconceptions around the current state of NDE. The interpretation is used to define ten value propositions or use cases under ‘NDE for Industry 4.0’ and ‘Industry 4.0 for NDE’ leading up to the clarity of purpose for NDE 4.0—enhanced safety and economic value for stakeholders. To pursue this worthy cause, the paper delves into some of the top adoption challenges, and proposes a journey of managed innovation, conscious skills development, and a new form of leadership required to succeed in the cyber-physical world.
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            NDE Perception and Emerging Reality: NDE 4.0 Value Extraction

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              Augmented Ultrasonic Data for Machine Learning

              Flaw detection in non-destructive testing, especially for complex signals like ultrasonic data, has thus far relied heavily on the expertise and judgement of trained human inspectors. While automated systems have been used for a long time, these have mostly been limited to using simple decision automation, such as signal amplitude threshold. The recent advances in various machine learning algorithms have solved many similarly difficult classification problems, that have previously been considered intractable. For non-destructive testing, encouraging results have already been reported in the open literature, but the use of machine learning is still very limited in NDT applications in the field. Key issue hindering their use, is the limited availability of representative flawed data-sets to be used for training. In the present paper, we develop modern, deep convolutional network to detect flaws from phased-array ultrasonic data. We make extensive use of data augmentation to enhance the initially limited raw data and to aid learning. The data augmentation utilizes virtual flaws—a technique, that has successfully been used in training human inspectors and is soon to be used in nuclear inspection qualification. The results from the machine learning classifier are compared to human performance. We show, that using sophisticated data augmentation, modern deep learning networks can be trained to achieve human-level performance.
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                Author and article information

                Contributors
                contact@vrana.net
                Ripi@inspiringnext.com
                Journal
                J Nondestr Eval
                J Nondestr Eval
                Journal of Nondestructive Evaluation
                Springer US (New York )
                0195-9298
                1573-4862
                3 July 2021
                2021
                : 40
                : 3
                : 61
                Affiliations
                [1 ]Vrana GmbH, Rimsting, Germany
                [2 ]Inspiring Next, Cromwell, CT USA
                Author information
                http://orcid.org/0000-0003-2037-9778
                Article
                793
                10.1007/s10921-021-00793-7
                8254059
                34248239
                deb25e69-f124-435f-991f-c2ca7406620e
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 28 May 2021
                : 21 June 2021
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                © Springer Science+Business Media, LLC, part of Springer Nature 2021

                nde 4.0,use cases,value proposition,advanced nde,future of nde,automation,ndt 4.0,industry 4.0,cyber-physical loop,digital twin,digital thread,digital weave,iiot,industrial revolution

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