The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.
Die automatische Qualitätskontrolle hat in den letzten Jahren große Fortschritte gemacht. Realisiert werden konnten diese insbesondere durch neue Entwicklungen im Bereich des maschinellen Lernens (vor allem Deep Learning), die den Einsatz von qualitativ hochwertigen Modellen für anspruchsvolle Aufgaben, wie die visuelle Inspektion oder Qualitätsvorhersage, ermöglichen. Allerdings ist die Auswahl von optimalen Methoden zur Qualitätskontrolle in bestimmten Situationen nicht offensichtlich, da sie nicht notwendigerweise mit klassischen (überwachten) Aufgaben des maschinellen Lernens übereinstimmen. In diesem Beitrag stellen wir eine automatisierte Methode zur Quantifizierung von Verschleißerscheinungen an elektrischen Kontaktstiften vor. Im Einzelnen beschreiben wir die Implementierung und den Test dieser Methode, die ein tiefes neuronales Netz zur Bildsegmentierung und Kenntnisse über die Geometrie der Kontaktstifte kombiniert. Diese Lösung dieses Problems ist wichtig, um eine quantifizierbare Aussage über die Materialqualität zu treffen und so die Materialauswahl optimieren zu können.
Funding source: BMBF
Award Identifier / Grant number: 01IS18041A
Funding statement: BH acknowledges funding in the frame of the BMBF project ITS_ML, grant number 01IS18041A.
About the authors
Florian Buckermann studied Cognitive Computer Science and Intelligent Systems at Bielefeld University and received his M.Sc. in 2019. Afterwards he started his PhD at the Data Science Chair at the University of Würzburg. His main research topics are machine learning applications in industrial production processes.
Nils Klement studied physics in Bielefeld and received his MSc. in 2018. He is working on his PhD at the HARTING Technology Group. His research focuses on the electrical and mechanical behavior of contacting surfaces and their simulation.
Oliver Beyer studied computer science at the Rheinische Friedrich-Wilhelms-Universität Bonn and received his diploma in 2008. In 2013, he obtained a PhD in computer science at the Cognitive Interaction Technology Cluster of Excellence (CITEC) at Bielefeld University. Since 2013 he is part of the research and development department at the HARTING Technology Group.
Andreas Hütten studied physics in Göttingen and received his PhD in 1989. He worked in the USA at UC Berkeley and Lawrence Berkeley National Laboratory. After further positions at the Leibniz Institute for Solid State and Materials Research in Dresden and at the Institute for Nanotechnology in Karlsruhe, he accepted a professorship for experimental physics at Bielefeld University in 2007. His research focuses on magnetic nanostructures, magnetoresistive sensors, magnetocaloric materials and electron microscopy.
Barbara Hammer is a full Professor for Machine Learning at the CITEC Cluster at Bielefeld University, Germany. She received her Ph.D. in Computer Science in 1999 and her venia legendi (permission to teach) in 2003, both from the University of Osnabrueck, Germany, where she was head of an independent research group on the topic ‘Learning with Neural Methods on Structured Data’. In 2004, she accepted an offer for a professorship at Clausthal University of Technology, Germany, before moving to Bielefeld in 2010. Barbara’s research interests cover theory and algorithms in machine learning and neural networks and their application for technical systems and the life sciences, including explainability, learning with drift, nonlinear dimensionality reduction, recursive models, and learning with non-standard data. Barbara has been chairing the IEEE CIS Technical Committee on Data Mining and Big Data Analytics, the IEEE CIS Technical Committee on Neural Networks, and the IEEE CIS Distinguished Lecturer Committee. She has been elected as member of the IEEE CIS Administrative Committee and the INNS Board. She is an associate editor of the IEEE Computational Intelligence Magazine, the IEEE TNNLS, and IEEE TPAMI. Currently, large parties of her work focusses on explainable machine learning for spatial-temporal data in her role as a PI of the ERC Synergy Grant Water-Futures.
We want to thank the HARTING Technology Group and the corresponding staff for executing the necessary measurements and providing the data and labels for our experiments.
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