TY - JOUR A1 - Bertovic, Marija T1 - A human factors perspective on the use of automated aids in the evaluation of NDT data N2 - In comparison to manual NDT methods, mechanized NDT is considered to be more reliable for a number of reasons, one of which being that the role of the inspectors and, therewith, the potential for human error, have been reduced. However, human-automation interaction research suggests that in spite of its numerous benefits, automation can lead to new yet unknown risks. One of those risks is inappropriate reliance on automation, which can result in automation misuse and disuse. The aim of this study was to investigate the potential inappropriate use of automation (specifically - the automated aids) in NDT addressing therewith the prevalent belief in the high reliability of automation held by the NDT community. To address this issue, 70 NDT trainees were asked to control the results of an eddy current data evaluation, allegedly provided by an automated aid, i.e. indication detection and sizing software. Seven errors were implemented into the task and it was measured to what extent the participants agreed with the aid. The results revealed signs of both misuse (agreeing with the aid even though it is incorrect) and disuse (disagreeing with the aid even though it is correct) of the aid that can affect the reliability with which inspections are carried out. Whereas misuse could be explained by a lower propensity to take risks and by a decreased verification behaviour-possibly due to bias towards automation and complacent behaviour-, disuse was assigned to problems in establishing the sizing criterion or to general difficulties in sizing. The implications of these results for the NDT praxis including suggestions for the decrease of automation bias are discussed. T2 - 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation CY - Minneapolis, MN, USA DA - 26.07.2015 KW - Non-Destructive Testing KW - Human Factors KW - NDT KW - Automation Bias PY - 2016 UR - http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4940449 DO - https://doi.org/10.1063/1.4940449 VL - 1706 SP - 020003-1 EP - 020003-16 PB - Amer institute physics CY - Melville, NY, USA AN - OPUS4-36561 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Kruschwitz, Sabine T1 - An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT) N2 - Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling. By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact. The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework. T2 - Rilem Spring Convention CY - Milano, Italy DA - 09.04.2024 KW - Adaptive Sampling KW - Random Sampling KW - Machine Learning KW - Non-Destructive Testing KW - Condition Assessment KW - Circular Economy PY - 2024 SN - 978-3-031-70277-8 DO - https://doi.org/10.1007/978-3-031-70277-8_38 SN - 2211-0844 VL - 55 SP - 330 EP - 338 PB - Springer Nature Switzerland CY - Switzerland AN - OPUS4-62458 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bertovic, Marija T1 - Embracing AI and User-Centered Design in Modern Technological Systems: The Case of Non-Destructive Testing N2 - The use of artificial intelligence (AI) in the design of technological systems is now ubiquitous. Non-destructive testing (NDT)—a set of techniques used to evaluate the properties and integrity of materials, components, or structures without causing damage to the tested object—is one of the disciplines seeking to benefit from this technology to increase reliability, safety, and productivity. Human-machine interaction (HMI), a recognized key area within Industry 4.0, plays an important role in the design, development, acceptance, and successful implementation of these systems. The integration of AI will significantly transform NDT tasks, introducing new tools, higher levels of process automation, and fully automated integration into production systems. The addition of assistive technologies like augmented reality will alleviate manual tasks but increase complexity, marking a transitional phase. For an NDT system to fully integrate into the broader Industry 4.0 framework, it must move beyond its traditional role as a self-contained entity and become part of a larger interconnected system. This transition will require new ways of interacting with technology, new task demands, and new competencies such as data analytics, automation, usability, control, oversight, communication, and complex problem-solving. We propose a paradigm shift to new roles: the Systems Developer, responsible for strategizing, developing, and integrating NDT systems with input from multiple sources and automation systems; the Caretaker, monitoring system functionality, resolving errors, and managing day-to-day operations; and the Problem Solver, diagnosing system problems and ensuring proper functioning. This development involves a multidisciplinary team, known as the "UX Design" role, tasked with creating a physical system that mediates between users and information sources to enhance inspection reliability and user acceptance. It is agreed that AI systems will continue to be operated and controlled by humans, so addressing human-machine interaction is paramount. The transition to more advanced NDT systems presents challenges such as adapting to complex technologies, addressing skill loss, and ensuring user acceptance. To overcome these hurdles, a user-centered approach that emphasizes intuitive interfaces and appropriate roles for humans and automation is critical. In addition, training programs tailored to new roles and responsibilities can help mitigate skill loss, while clear communication and phased implementation strategies are essential to manage organizational change and user resistance. By effectively addressing these challenges, companies can maximize the benefits of advanced AI technologies. Non-destructive testing serves as an illustrative example, but the principles and strategies discussed apply broadly across various technological systems integrating AI and HMI. The evolution towards more advanced, AI-driven systems demands a careful balance between technological capabilities and human factors, ensuring that the full potential of these innovations can be realized in a practical, user-friendly manner. T2 - 6th International Conference on Human Systems Engineering and Design: Future Trends and Applications (IHSED 2024) CY - Split, Croatia DA - 27.09.2024 KW - Human Factors KW - Artificial intelligence KW - AI KW - Human-Machine Interaction KW - Non-Destructive Testing KW - Acceptance PY - 2024 AN - OPUS4-61868 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bertovic, Marija T1 - Tutorial: NDE Reliability and the Influence of Human Factors N2 - Human factors significantly influence the reliability of non-destructive testing (NDT) systems, impacting safety, efficiency, and cost. This tutorial explores the interplay between human capabilities, organizational context, and technological systems in NDT inspections. It starts with a short overview of reliability models, including the Probability of Detection (POD) framework, alongside with advanced approaches for reliability assessment. The distinction between human factors and human error is emphasized, advocating a systemic approach to human error that addresses underlying organizational and environmental conditions. The implications of increasing automation and AI integration are discussed, highlighting the challenges of trust, usability, and skill preservation in human-AI collaboration. Practical strategies, including user-centered design, targeted training, and risk management frameworks, are proposed to enhance inspection performance and maintain NDT reliability in evolving technological landscapes. This tutorial underscores the critical role of human factors in the transition to NDT 4.0 and the development of effective human-machine systems. T2 - USES2 Training week#2 CY - Berlin, Germany DA - 24.06.2024 KW - Human Factors KW - Non-Destructive Testing KW - Reliability KW - Human-Machine Interaction KW - Artificial Intelligence PY - 2024 AN - OPUS4-61874 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Prabhakara, Prathik T1 - Ein synergistischer Ansatz zur Charakterisierung anisotroper Materialien mit Hilfe von Ultraschall und Mikrostrukturanalyse N2 - Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen. T2 - DGZfP-Jahrestagung 2024 CY - Osnabrück, Germany DA - 06.05.2024 KW - Ultrasonic Testing KW - Time-offlight Diffraction (TOFD) KW - Microstructure Analysis KW - Non-Destructive Testing PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600122 UR - https://www.ndt.net/?id=29535 AN - OPUS4-60012 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Prabhakara, Prathik T1 - Ein synergistischer Ansatz zur Charakterisierung anisotroper Materialien mit Hilfe von Ultraschall und Mikrostrukturanalyse N2 - Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen. T2 - DGZfP-Jahrestagung 2024 CY - Osnabrück, Germany DA - 06.05.2024 KW - Ultrasonic Testing KW - Material Characterization KW - Microstructure Analysis KW - Time-offlight Diffraction (TOFD) KW - Non-Destructive Testing PY - 2024 UR - https://www.ndt.net/?id=29535 AN - OPUS4-60980 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -