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Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0003-1597-6738
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-9933-8532
2022 (English)In: IFIP Advances in Information and Communication Technology: WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Springer Science and Business Media Deutschland GmbH , 2022, p. 27-34Conference paper, Published paper (Refereed)
Abstract [en]

Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2022. p. 27-34
Keywords [en]
Binary classification, Cylinder lock, Machine learning, Multiple experts, Torque data, Cylinders (shapes), Learning algorithms, Quality assurance, Quality control, Torque, Expected lifetime, Human abilities, Human operator, Machine-learning, Multiple expert, Tactile sense, Testing procedure, Locks (fasteners)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-60550DOI: 10.1007/978-3-031-16407-1_4ISI: 000869718800004Scopus ID: 2-s2.0-85140472723ISBN: 9783031164064 (print)OAI: oai:DiVA.org:mdh-60550DiVA, id: diva2:1708202
Conference
WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Gyenongju, South Korea, 25-29 September, 2022
Available from: 2022-11-03 Created: 2022-11-03 Last updated: 2024-04-26Bibliographically approved
In thesis
1. Automated Tactile Sensing for Quality Control of Locks Using Machine Learning
Open this publication in new window or tab >>Automated Tactile Sensing for Quality Control of Locks Using Machine Learning
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis delves into the use of Artificial Intelligence (AI) for quality control in manufacturing systems, with a particular focus on anomaly detection through the analysis of torque measurements in rotating mechanical systems. The research specifically examines the effectiveness of torque measurements in quality control of locks, challenging the traditional method that relies on human tactile sense for detecting mechanical anomalies. This conventional approach, while widely used, has been found to yield inconsistent results and poses physical strain on operators. A key aspect of this study involves conducting experiments on locks using torque measurements to identify mechanical anomalies. This method represents a shift from the subjective and physically demanding practice of manually testing each lock. The research aims to demonstrate that an automated, AI-driven approach can offer more consistent and reliable results, thereby improving overall product quality. The development of a machine learning model for this purpose starts with the collection of training data, a process that can be costly and disruptive to normal workflow. Therefore, this thesis also investigates strategies for predicting and minimizing the sample size used for training. Additionally, it addresses the critical need of trustworthiness in AI systems used for final quality control. The research explores how to utilize machine learning models that are not only effective in detecting anomalies but also offers a level of interpretability, avoiding the pitfalls of black box AI models. Overall, this thesis contributes to advancing automated quality control by exploring the state-of-the-art machine learning algorithms for mechanical fault detection, focusing on sample size prediction and minimization and also model interpretability. To the best of the author’s knowledge, it is the first study that evaluates an AI-driven solution for quality control of mechanical locks, marking an innovation in the field.

Abstract [sv]

Denna avhandling fördjupar sig i användningen av Artificiell Intelligens (AI) för kvalitetskontroll i tillverkningssystem, med särskilt fokus på anomalidetektion genom analys av momentmätningar i roterande mekaniska system. Forskningen undersöker specifikt effektiviteten av momentmätningar för kvalitetskontroll av lås, vilket utmanar den traditionella metoden som förlitar sig på människans taktila sinne för att upptäcka mekaniska anomalier. Denna konventionella metod, som är brett använd, har visat sig ge inkonsekventa resultat och medför fysisk belastning för operatörerna. En nyckelaspekt av denna studie innebär att genomföra experiment på lås med hjälp av momentmätningar för att identifiera mekaniska anomalier. Denna metod representerar en övergång från den subjektiva och fysiskt krävande praxisen att manuellt testa varje lås. Forskningen syftar till att demonstrera att en automatiserad, AI-driven metod kan erbjuda mer konsekventa och tillförlitliga resultat, och därmed förbättra den övergripande produktkvaliteten. Utvecklingen av en maskininlärningsmodell för detta ändamål börjar med insamling av träningsdata, en process som kan vara kostsam och störande för det normala arbetsflödet. Därför undersöker denna avhandling också strategier för att förutsäga och minimera mängden av data som används för träning. Dessutom adresseras det kritiska behovet av tillförlitlighet i AI-system som används för slutlig kvalitetskontroll. Forskningen utforskar hur man kan använda maskininlärningsmodeller som inte bara är effektiva för att upptäcka anomalier, utan också erbjuder en nivå av tolkningsbarhet, för att undvika fallgroparna med svart låda AI-modeller. Sammantaget bidrar denna avhandling till att främja automatiserad kvalitetskontroll genom att utforska de senaste maskininlärningsalgoritmerna för detektion av mekaniska fel, med fokus på prediktion och minimering av mängden träningsdata samt tolkbarheten av modellens beslut. Denna avhandling utgör det första försöket att utvärdera en AI-driven strategi för kvalitetskontroll av mekaniska lås, vilket utgör en nyskapande innovation inom området.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2024. p. 49
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 360
Keywords
Anomaly detection, Sample size prediction, Learning curves, Machine learning, Quality control, : Explainable artificial intelligence, Counterfactual explanation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66506 (URN)978-91-7485-648-4 (ISBN)
Presentation
2024-06-07, C3-003, Mälardalens universitet, Eskilstuna, 09:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, No 20200132 01 H
Available from: 2024-04-25 Created: 2024-04-24 Last updated: 2024-05-17Bibliographically approved

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