Opportunities of artificial intelligence in the field of calibration services
-
Matthias Ohlrogge
Abstract
The use of artificial intelligence (AI) is playing an increasingly important role in the automated monitoring and failure prediction of production processes (H. Ding, R. X. Gao, A. J. Isaksson, R. G. Landers, T. Parisini, and Y. Yuan, “State of AI-based monitoring in smart manufacturing and introduction to focused section,” IEEE/ASME Trans. Mechatron., vol. 25, no. 5, pp. 2143–2154, 2020). The resulting possibilities can also be extended to the monitoring of test equipment in the process, opening up new possibilities for the automated detection of deviations in reference measuring equipment. This possibility will be demonstrated in the following report on the basis of a practical example at one of Europe’s largest calibration service providers. Within the project carried out, a deviation within a reference measuring device was recorded by means of pattern recognition with the help of the calibrations. This deviation was also metrologically confirmed in an independent interlaboratory comparison, which proves that the method used is suitable for detecting deviations in the process at an early stage and without additional acquisition of measurement data or carrying out further measurements.
Zusammenfassung
Der Einsatz von künstlicher Intelligenz (KI) spielt eine immer wichtigere Rolle bei der automatisierten Überwachung und Fehlervorhersage von Produktionsprozessen (H. Ding, R. X. Gao, A. J. Isaksson, R. G. Landers, T. Parisini, and Y. Yuan, “State of AI-based monitoring in smart manufacturing and introduction to focused section,” IEEE/ASME Trans. Mechatron., vol. 25, no. 5, pp. 2143–2154, 2020). Die sich daraus ergebenden Möglichkeiten können auch auf die Überwachung von Prüfmitteln im Prozess ausgeweitet werden, wodurch sich neue Möglichkeiten zur automatisierten Erkennung von Abweichungen bei Referenzmessmitteln ergeben. Diese Möglichkeit soll im folgenden Bericht anhand eines Praxisbeispiels bei einem der größten europäischen Kalibrierdienstleister demonstriert werden. Im Rahmen des durchgeführten Projektes wurde mit Hilfe der Kalibrierungen eine Abweichung innerhalb eines Referenzmessgerätes mittels Mustererkennung erfasst. Diese Abweichung wurde auch in einem unabhängigen Ringversuch messtechnisch bestätigt, was beweist, dass die angewandte Methode geeignet ist, Abweichungen im Prozess frühzeitig und ohne zusätzliche Messdatenerfassung oder Durchführung weiterer Messungen zu erkennen.
About the author
Matthias Ohlrogge received the master’s degree in microsystems engineering and the Ph.D. degree with a focus on high-frequency technologies from the Albert Ludwigs University of Freiburg, Freiburg, Germany, in 2012 and 2016, respectively. From July 2017 to May 2023, he joined Testo Industrial Services GmbH, Kirchzarten, Germany, where he was responsible for calibration services and for the field of electrical calibration. His current research interests include the metrology and traceability of calibration in the fields of RF and LF systems.
Acknowledgments
I would like to express my gratitude to the company Testo Industrial Services GmbH for carrying out the project and giving me the opportunity to investigate the possibilities of machine learning advantages in an industrial calibration process. Additionally, I would like to thank the company IconPro GmbH for the encouraging support during the project time.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of large language models, AI and machine learning tools: None declared.
-
Competing interests: The author states no competing interests.
-
Research funding: None declared.
-
Data availability: Not applicable.
References
[1] Lufthansa Industry Solutions : Whitepaper, “Artificial Intelligence as a Service,” 2022. Available at: https://www.lufthansa-industry-solutions.com/de-de/studien/whitepaper-artificial-intelligence-as-a-service-aiaas [accessed: Jul. 27, 2022].Search in Google Scholar
[2] OpenAI, “Introducing ChatGPT 30.11.2022,” Available at: https://openai.com/blog/chatgpt [accessed: Apr. 19, 2023].Search in Google Scholar
[3] M. Abdullah, A. Madain, and Y. Jararweh, “ChatGPT: fundamentals, applications and social impacts,” in 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), Milan, Italy, 2022, pp. 1–8.10.1109/SNAMS58071.2022.10062688Search in Google Scholar
[4] C. Frey and L. Wessels, Maschinelles Lernen für die Produktion, Germany, Maschinenbau, 2022.10.1007/s44029-022-0717-0Search in Google Scholar
[5] “Ifo business survey – 2 August 2023,” Available at: https://www.ifo.de/en/facts/2023-08-02/artificial-intelligence-use-companies-germany [accessed: Mar. 26, 2024].Search in Google Scholar
[6] H. Ding, R. X. Gao, A. J. Isaksson, R. G. Landers, T. Parisini, and Y. Yuan, “State of AI-based monitoring in smart manufacturing and introduction to focused section,” IEEE/ASME Trans. Mechatron., vol. 25, no. 5, pp. 2143–2154, 2020. https://doi.org/10.1109/TMECH.2020.3022983.Search in Google Scholar
[7] B. M. Ellerhoff, “Quantencomputer heute und morgen,” in Mit Quanten rechnen. Essentials, Wiesbaden, Springer Spektrum, 2020.10.1007/978-3-658-31222-0Search in Google Scholar
[8] V. Chauhan, S. Negi, D. Jain, P. Singh, A. K. Sagar, and A. K. Sharma, “Quantum computers: a review on how quantum computing can boom AI,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2022, pp. 559–563.10.1109/ICACITE53722.2022.9823619Search in Google Scholar
[9] High-Level Expert Group on Artificial Intelligence, A Definition of AI: Main Capabilities and Scientific Disciplines, European Commission, 2019. Available at: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341 [accessed: Mar. 26, 2024].Search in Google Scholar
[10] W. Zhang, D. Yang, and H. Wang, “Data-driven methods for predictive maintenance of industrial equipment: a survey,” IEEE Syst. J., vol. 13, no. 3, pp. 2213–2227, 2019. https://doi.org/10.1109/JSYST.2019.2905565.Search in Google Scholar
[11] A. Paranjape, P. Katta, and M. Ohlenforst, “Automated data preprocessing for machine learning based analyses,” in COLLA 2022: The Twelfth International Conference on Advanced Collaborative Networks Systems and Applications, Venice, Italy, 2022.Search in Google Scholar
[12] “What is calibration? – Fluke corporation,” Available at: https://eu.flukecal.com/de/literature/about-calibration [accessed: Mar. 26, 2024].Search in Google Scholar
[13] B. Saju, M. K. Gopal, B. Nithya, V. Asha, and V. Kumar, “Analysis on role of quantum computing in machine learning,” in 2022 Fourth International Conference on Cognitive Computing and Information Processing (CCIP), Bengaluru, India, 2022, pp. 1–8.10.1109/CCIP57447.2022.10058679Search in Google Scholar
[14] M. Belkhir, H. Benkaouha, and E. Benkhelifa, “Quantum vs classical computing: a comparative analysis,” in 2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC), Paris, France, 2022, pp. 1–8.10.1109/FMEC57183.2022.10062753Search in Google Scholar
[15] H. Talbi, A. Draa, and M. Batouche, “A new quantum-inspired genetic algorithm for solving the travelling salesman problem,” in 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT ’04, vol. 3, Hammamet, Tunisia, 2004, pp. 1192–1197.10.1109/ICIT.2004.1490730Search in Google Scholar
[16] C. G. Almudever, et al.., “The engineering challenges in quantum computing,” in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, Lausanne, Switzerland, 2017, pp. 836–845.10.23919/DATE.2017.7927104Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Measurement systems and sensors with cognitive features III
- Research Articles
- Opportunities of artificial intelligence in the field of calibration services
- Neuronale Netze zur Startwertschätzung bei der Identifikation piezoelektrischer Materialparameter
- Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers
- Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation
- Surface mortar detection and performance evaluation of recycled aggregates based on hyperspectral technology
- A comparative study of classification methods for state recognition in injection molding
Articles in the same Issue
- Frontmatter
- Editorial
- Measurement systems and sensors with cognitive features III
- Research Articles
- Opportunities of artificial intelligence in the field of calibration services
- Neuronale Netze zur Startwertschätzung bei der Identifikation piezoelektrischer Materialparameter
- Spectral reconstruction using neural networks in filter-array-based chip-size spectrometers
- Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation
- Surface mortar detection and performance evaluation of recycled aggregates based on hyperspectral technology
- A comparative study of classification methods for state recognition in injection molding
