ArticlePublisher preview available

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

Authors:
  • Vrana GmbH - NDE Consulting and Solutions
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

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.
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
1 3
Journal of Nondestructive Evaluation (2021) 40:61
https://doi.org/10.1007/s10921-021-00793-7
Cyber‑Physical Loops asDrivers ofValue Creation inNDE 4.0
JohannesVrana1 · RipudamanSingh2
Received: 28 May 2021 / Accepted: 21 June 2021 / Published online: 3 July 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
Across so many industries, non-destructive evaluation has proven its worth time and again through quality and safety assur-
ance 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.
Keywords 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
1 Introduction
1.1 NDT Value Perception
In the early industrial days, humans naturally lacked the
necessary experience how to safely process raw materials,
design and manufacture components and systems, and oper-
ate various machines and modes of transportation, which
resulted in severe accidents. This is where NDT develop-
ments took place. NDT identified potential material imper-
fections leading to a massive increase in machine reliability.
NDT also became a central part of early day feedback loops
by identifying potential design and production improve-
ments, through additional knowledge. Growing experience
and knowledge in engineering continues to make the world
a safer place while creating economic prosperity through
innovation and revolutions.
In the beginning, the business case for NDT was straight
forward. At that time, the companies were able to differen-
tiate themselves from competitors by technological perfor-
mance benefits for the customers. But this position changed
over time as competition became harder leading to price
wars and every company looked for savings, everywhere.
What does this mean for the current day business case
for NDT:
A traditional business case for NDT considers the poten-
tial cost which would have accrued in case of accidents.
The cost of a single accident can easily be a 7-digit num-
ber—not even considering the cost of the loss in reputa-
tion. Such costs are way higher than the cost of years of
NDT. Most NDT professionals see this traditional busi-
ness case and are therefore astonished how other groups
start to question the cost for any investments for NDT.
Over the years, as the number of accidents has dropped
to a lower level, the credit is being attributed to good
quality of design, production, and maintenance, with
* Johannes Vrana
contact@vrana.net
Ripudaman Singh
Ripi@inspiringnext.com
1 Vrana GmbH, Rimsting, Germany
2 Inspiring Next, Cromwell, CT, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... The data-driven models must be capable of learning the underlying physics governing the wave propagation phenomena from the given datasets as inferred from prior research [16][17][18][19][20]. These models are developed [21-24] for many engineering, physics, and science domain problems due to increasing computational power, data acquisition [25][26][27], and high-resolution measurements [28]. ...
Article
Full-text available
In this paper, we propose a deep neural network model to simulate the transient ultrasonic wave propagation in the 2D domain by implementing the Data driven-simulation-assisted-Physics learned AI (DPAI) model. The DPAI model consists of modified convolutional long short-term memory (ConvLSTM) with an encoder–decoder structure, which learns the representation of spatio-temporal dependence from input sequence data. The DPAI uses the data-driven approach to understand the underlying physics of elastic wave propagation in a medium. This model is trained with simulation-assisted finite element simulation datasets consisting of distributed single and multi-point excitation sources in the medium. The effectiveness of the proposed approach is demonstrated by modeling a wide range of scenarios in elastodynamic physics, such as multiple point sources, varying excitation parameters, and wave propagation in a large 2D domain. The trained DPAI model is tested and compared against FE modeling with respect to accuracy and computational time.
... If geometries are produced via generative methods, like additive manufacturing, it is imaginable to relate production parameters to defects appearing in the CT scan and use this information to improve the process. This communication among several systems within the value creation chain represents an important core property of future smart factories, which can be augmented by the use of artificial intelligence and further concepts of Industry 4.0 [93]. Similarly, information about the part stemming from its digital twin, a priori knowledge or other modalities can be used to improve the reconstruction or generate a deeper insight into the involved processes. ...
Article
Full-text available
Robot-guided computed tomography enables the inspection of parts that are too large for conventional systems and allows, for instance, the non-destructive and volumetric evaluation of mechanical joining components within already assembled cars in the automotive industry. However, the typical scan time required by such setups is still significant and represents a major barrier for its industrial large-scale application. As an approach to mitigate the necessary time demand, we propose a part-specific adjustment of the acquisition trajectory. Common circular standard trajectories are inherently inefficient, since they are applied independently of the considered inspection task, while the use of acquisition orbits tailored particularly to the investigated object effectively allows a reduction of the required number of projections, which in turn has the potential to directly decrease the scan time significantly. In contrast to former simulation-guided approaches, this work is considered to be the first successful task-specific trajectory optimization being performed on a robot-based industrial CT platform and aims towards providing a first proof of concept that such methods can be practically applied in a shop floor environment. Based on representative results, a reduction of the number of required projections by approx. 55% or an image quality improvement according to the root-mean squared error by approx. 40% compared to the conventionally applied planar acquisition trajectory was achieved.
Conference Paper
Full-text available
ZfP 4.0, NDE 4.0, oder die ZfP im Zeichen der Digitalisierung und Digitalen Transformation, begann 2017 in Deutschland [2,3] und wird zunehmend zu einem weltweiten Trend in der ZfP Community. Dieser Vortrag bietet einen Überblick über NDE 4.0 "Digitization", "Digitalization" und "Digital Transformation" Aktivitäten, wie die Erstellung der "Guideline for the Development of a Roadmap for NDE 4.0" im Rahmen der ICNDT SIG NDE 4.0 [7] und die Projekte AIFRI [8] und normPoD[9]. Er bietet einen Überblick über einige der Aktivitäten in den ZfP Landesgesellschaften, in der EFNDT und in der ICNDT, einen kurzen Rückblick auf die virtuelle Internationale NDE 4.0 Konferenz 2021, einen Vorgeschmack auf die Internationale NDE 4.0 Konferenz 2022 und eine Übersicht über weitergehende Informationsquellen zum Thema ZfP 4.0.
Conference Paper
Full-text available
Seit beinahe 50 Jahren werden Zuverlässigkeitsbewertungen zerstörungsfreier Prüfverfahren erfolgreich in unterschiedlichen Industriebranchen eingesetzt. Dennoch gibt es weiterhin viele Vorbehalte, da die Bewertungsgrundlage nicht standardisiert ist und konkrete Handlungsanweisungen fehlen. Dem gegenüber steht die Anerkennung in der ZfP-Community. Es ist bisher nur Wenigen bekannt, dass dem Wissen über die Zuverlässigkeit eines zerstörungsfreien Prüfverfahren eine Schlüsselfunktion für die ZfP 4.0 zukommt. Im Vortrag wird die Notwendigkeit für eine Norm oder eine Richtlinie zum Thema Zuverlässigkeitsbewertungen aufgezeigt. Es wird darauf eingegangen, warum es bisher (in Deutschland) noch keine Richtlinie gab und gibt und was der:die Anwender:innen aus einer Richtlinie zur Zuverlässigkeitsbewertung von ZfPPrüfverfahren erwarten kann. Es werden konkrete Schritte und Vorgehensweisen zur Bewertung der Zuverlässigkeit eines Verfahrens aufgezeigt; von der Definition eines Anwendungsfalls, über die Herstellung geeigneter Testkörper bis hin zur Durchführung und Bewertung von Prüfungen unter Einbeziehung von menschlichen Einflüssen. Die Beispiele umfassen sowohl Anwendungen aus dem Bauwesen als auch aus dem Maschinen- und Anlagenbau. Der Vortrag fasst die bisherigen und geplanten Arbeiten in dem WIPANO Projekt „Normung für die probabilistische Bewertung der Zuverlässigkeit für zerstörungsfreie Prüfverfahren“ zusammen.
Article
This paper reports the effectiveness of a novel imaging system, piezoelectric and laser ultrasonic system (PLUS), for the three-dimensional (3D) imaging of fatigue cracks with a high-resolution. The PLUS combines a piezoelectric transmitter and the two-dimensional (2D) mechanical scanning of a laser Doppler vibrometer, enabling the 2D matrix array with an ultra-multiple number of receiving points for 3D phased array imaging. After describing the principle and 3D imaging algorithm of PLUS, we show the fundamental 3D imaging capability of the PLUS in a flat-bottom-hole specimen with varying the number of receiving points under a fixed large receiving aperture. We then demonstrate that the PLUS with 4275 receiving points (i.e. 75 × 57) achieves high-resolution 3D imaging of a fatigue crack with a high signal-to-noise ratio, providing the outline of the fatigue crack geometry. We also discuss the effectiveness of the ultra-multiple receiving points for suppressing grating lobes and random noise.
Article
Cognitive sensor systems (CSS) determine the future of inspection and monitoring systems for the nondestructive evaluation (NDE) of material states and their properties and key enabler of NDE 4.0 activities. CSS generate a complete NDE 4.0 data and information ecosystem, i. e. they are part of the materials data space and they are integrated in the concepts of Industry 4.0 (I4.0). Thus, they are elements of the Industrial Internet of Things (IIoT) and of the required interfaces. Applied Artificial Intelligence (AI) is a key element for the development of cognitive NDE 4.0 sensor systems. On the one side, AI can be embedded in the sensor’s microelectronics (e. g. neuromorphic hardware architectures) and on the other side, applied AI is essential for software modules in order to produce end-user-information by fusing multi-mode sensor data and measurements. Besides of applied AI, trusted AI also plays an important role in CSS, as it is able to provide reliable and trustworthy data evaluation decisions for the end user. For this recently rapidly growing demand of performant and reliable CSS, specific requirements have to be fulfilled for validation and qualification of their correct function. The concept for quality assurance of NDE 4.0 sensor and inspection systems has to cover all of the functional sub-systems, i. e. data acquisition, data processing, data evaluation and data transfer etc. Approaches to these objectives are presented in this paper after giving an overview on the most important elements of CSS for NDE 4.0 applications. Reliable and safe microelectronics is a further issue in the qualification process for CSS.
Article
Reliability evaluations of modern test systems under the Industry 4.0 technologies, play a vital role in the successful transformation to NDE 4.0. This is due to the fact that NDE 4.0 is mainly based on the interconnection between the cyber-physical systems. When the individual reliability of the various important technologies from the Industry 4.0 such as the digital twin, digital thread, Industrial Internet of Things (IIoT), artificial intelligence (AI), data fusion, digitization, etc. is high, then it is possible to obtain the reliability beyond the intrinsic capability of the test system. In this paper, the significance of the reliability evaluation is reviewed under the vision of NDE 4.0, including examples of data fusion concepts as well as the importance of algorithms (like explainable artificial intelligence), the practical use is discussed and elaborated accordingly.
Article
Informatization is defined as the process by which information technologies, such as the World Wide Web and other communication technologies, have transformed economic and social relations to such an extent that cultural and economic barriers are minimized. What does this mean for nondestructive testing and evaluation (NDT/E)? In short: informatization in NDT and NDE has happened and will continue to happen, independent of whether individuals or companies like it or not. However, we can shape it—together.
Article
The increasing use of stainless steel in industrial structures can be attributed to its excellent mechanical properties at elevated temperatures. Martensitic grade stainless-steel is used, for example, to manufacture steam turbine blades in power plants. The failure of these turbine blades can result in equipment damage contributing to expensive plant failures and safety concerns. Degradation and structural failure of these blades is largely attributed to material fatigue, at the microstructure level. Hence, it is important to evaluate the level of fatigue prior to the initiation of macro defects to ensure the viability of these components. Conventional nondestructive evaluation (NDE) techniques such as ultrasonic testing and eddy current testing are suitable in detection of macro defects such as cracks, but not very effective in evaluating degradation of the material at a microstructure scale. This article investigates the feasibility of the nonlinear eddy current (NLEC) technique to detect fatigue in martensitic grade stainless-steel samples along with a methodology to classify the samples. K-medoids clustering algorithm and genetic algorithm are used to classify the samples according to the severity of fatigue. Initial results indicate that stainless-steel samples, in different stages of fatigue, can be classified into broad categories of low, mid, and high levels of fatigue.
Chapter
Full-text available
Digital technologies provide significant efficiency gains to NDE (Nondestructive Evaluation) procedures. Digitalization of NDE further enhances its effectiveness and simplifies the processes. Digital transformation at the NDE ecosystem level can create unprecedented value for multiple stakeholders simultaneously. The aspects of technology [1], informatization [2], and various use cases [3] have been discussed by the authors in these three chapters. This chapter identifies primary stakeholders in the broad NDE ecosystem, connects them through key value streams, and delves deeper into how the data flow along various cyber-physical loops creates value for those in the value stream.
Chapter
Full-text available
The world of non-destructive evaluation (NDE) has seen digitization since the third revolution. Over the last decade or so, digitalization has also been observed to a point where it is now ready for digital transformation, in sync with the fourth industrial revolution. The intermediary step of digitalization overlaps the third and fourth revolution and can sometimes be confusing. This chapter is aimed at demystifying the stages of informatization, starting with some general life examples, understanding the evolution at the fundamental data set level, which is then used to understand the relevant elements of nondestructive evaluation.
Article
Full-text available
Like with the previous revolutions the goal of the fourth revolution is to make manufacturing, design, logistics, maintenance, and other related fields faster, more efficient, and more customer centric. This holds for classical industries, for civil engineering, and for NDE and goes along with new business opportunities and models. Core components to enable those fourth revolutions are semantic interoperability, converting data into information, the Industrial Internet of Things (IIoT) offering the possibility for every device, asset, or thing to communicate with each other using standard open interfaces, and the digital twin converting all the available information into knowledge and closing the cyber-physical loop. For NDE this concept can be used #1 to design, improve, and tailor the inspection system hard- and software and #2 to choose and adapt to best inspection solution for the customer or to enhance the inspection performance. Enabling better quality, speed, and cost at the same time. On a broader view, the integration of NDE into IIoT and Digital Twin is the chance for the NDE industry for the overdue change from a cost center to a value center. In most cases, data gathered by NDE is used for a quality assurance assessment resulting in a binary decision. But the information content of NDE goes way deeper and is of major interest for additional groups: engineering and management. Some of those groups might currently not be aware of the benefits of NDE data and the NDE industry makes the access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be taken on now by the NDE industry. The big IT players are not waiting and, if not available on the market, they will develop and offer additional data sources including ultrasonics, X-ray or eddy current.
Chapter
Full-text available
Cyber technologies are offering new horizons for quality control in manufacturing and safety assurance of physical assets in service. 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 NDE community is coming together once again to define the purpose, chart the process, and address the adoption of emerging technologies. This handbook is an effort in that direction. In this chapter, the authors define the industrial revolutions and technologies driving the change, use that context to understand the revolutions in NDE, leading up to the definition of NDE 4.0. In the second part of this chapter, authors have proposed several value propositions or use cases under “NDE for Industry 4.0” and “Industry 4.0 for NDE” leading to clarity of purpose for NDE 4.0 – enhanced safety and economic value for stakeholders within the NDE eco-system.
Conference Paper
Full-text available
With each revolution industry, maintenance, capital and consumer devices, infrastructure, society, and many more are growing closer with rising mutual influence. The third revolution started the digitization of data and the digitalization of processes, which are now omnipresent. The fourth revolution takes the next step by digital transformation, by bringing all assets from all different areas together. It requires data transparency and enables the Internet of Things, digital twins and threads, as well as smart cities. It presents the opportunity for everybody to use the data from technical and medical testing/imaging in conjunction with high repetition sensor information enabling prediction and prescription.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
What you don't know can't hurt you' does NOT apply to Digital Transformation. The current pandemic (Covid-19) is changing the value proposition of digital transformation from 'competitive advantage' to a 'must do initiative'. The hidden cost of not adopting Industry 4.0 is likely to be far greater than the visible cost of adopting it. In the inspection world, we call it NDE 4.0. When implemented, it promises value on all three dimensions-quality, cost, and schedule; to the stakeholders in the ecosystem ; from R&D to the leading edge of inspection. However, the very nature of the revolution requires that various stakeholders make concurrent efforts to adopt and master the application. For that, every constituent must see a value proposition from their vantage point. This paper, the first in a 3-part series, aims to help different parties appreciate WHY should they care for NDE 4.0.
Article
Full-text available
Die industrielle Revolution wird von Historikern in drei Phasen unterteilt: Die Erfindung der Dampfmaschine (Mechanisierung), die Elektrizität (Massenproduktion) und die Mikroelektrische Revolution (Automatisierung). Eine ähnliche Entwicklung gab es bei der zerstörungsfreien Materialprüfung: Werkzeuge wie Linsen oder Stethoskope erlaubten, die menschlichen Sinne zu schärfen, die Wandlung von Wellen macht das Nichtsichtbare sichtbar und bietet damit einen „Blick“ in die Bauteile und schließlich Automatisierung, Digitalisierung und Rekonstruktion. Während der gesamten industriellen Entwicklung war die ZfP maßgeblich mit für die Qualität und damit für den Erfolg der gefertigten Güter verantwortlich. In der Industrie wird mittlerweile von einer vierten Revolution gesprochen: Die Informatisierung, Digitalisierung und Vernetzung der industriellen Produktion. Wie schon immer wird die ZfP entscheidend für den Erfolg dieser vierten Revolution sein, da sie die Datenbasis bietet, die in einer vernetzten Produktionsumgebung für das Feedback benötigt wird. Für die ZfP wird dies zu einem Wandel führen. Die Ergebnisse der Prüfung müssen einer vernetzten Produktionsumgebung so zur Verfügung gestellt werden, dass diese für Feedbackschleifen ausgewertet werden können, die Prüfbarkeit muss im Design mit berücksichtigt werden und die Zuverlässigkeit der Prüfaussagen wird einen immer größeren Stellenwert gewinnen. Diese Veröffentlichung zeigt Konzepte auf, wie sich die ZfP in Industrie 4.0-Landschaften integrieren kann. Das Referenzarchitekturmodell Industrie 4.0 (RAMI 4.0) ermöglicht eine Verortung einer digitalen Komponente, die Industrie 4.0 Verwaltungsschale ist das Interface zwischen Industrie 4.0-Kommunikation und dem physischen Gerät. OPC-UA ist das Kommunikationsprotokoll, das sich derzeit als Standard etabliert, DICONDE ein Kommunikationsprotokoll und Datenformat für Prüf- und Metadaten, AutomationML ein Datenformat für Anlagenplanungsdaten und die Industrial Data Space Initiative zur Sicherstellung der Datensouveränität.
Article
Up until recently, the industrial revolution was divided into three phases: (1) simple mechanization; (2) mass production; and (3) automation. Similarly, nondestructive evaluation (NDE) can be divided into three phases: (1) tools, such as lenses, sharpened the human senses; (2) the conversion of waves made the invisible visible by offering a “look” inside components; and (3) automation, digitization, and reconstruction enhanced the accuracy, speed, and ease of information sharing. During industrial development, although NDE has been decisively responsible for the quality of the manufactured goods and safety of operations, it has carried perceptions not commensurate with the value realized. Currently, industry leaders have been talking about a fourth revolution: the informatization, digitization, and networking of industrial production and the concurrent use of emerging technologies, such as artificial intelligence, augmented reality, and 5G networks. For NDE, this fourth revolution offers an unprecedented opportunity to address technical challenges and negative perceptions at the same time, leading to an enhanced appreciation of this significant discipline. The paper begins with a survey of professionals in the field to identify the perceptions surrounding NDE and moves on to demonstrate the value of integrating Industry 4.0 with NDE in the form of NDE 4.0 driven by connectivity and data mining. Building on that, this paper next presents the necessary basics and concepts, like semantic interoperability and the Industrial Internet of Things. Moreover, the key interfaces and data formats, like OPC UA and DIOCONDE, are discussed, and the International Data Spaces Association (IDSA), which goes one step further by ensuring data sovereignty, enabling data markets, and connecting the world, is introduced. The emerging reality of NDE 4.0 is that robust digital interfaces help create value, and statistical approaches combined with digital twins and threads help extract that value.