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Automated Tactile Sensing for Quality Control of Locks Using Machine Learning
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system. ASSA ABLOY.ORCID-id: 0009-0008-0054-8072
2024 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalens universitet, 2024. , s. 49
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 360
Emneord [en]
Anomaly detection, Sample size prediction, Learning curves, Machine learning, Quality control, : Explainable artificial intelligence, Counterfactual explanation
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
URN: urn:nbn:se:mdh:diva-66506ISBN: 978-91-7485-648-4 (tryckt)OAI: oai:DiVA.org:mdh-66506DiVA, id: diva2:1854238
Presentation
2024-06-07, C3-003, Mälardalens universitet, Eskilstuna, 09:15 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation, No 20200132 01 HTilgjengelig fra: 2024-04-25 Laget: 2024-04-24 Sist oppdatert: 2024-05-17bibliografisk kontrollert
Delarbeid
1. Interpretable ML model for quality control of locks using counterfactual explanations
Åpne denne publikasjonen i ny fane eller vindu >>Interpretable ML model for quality control of locks using counterfactual explanations
2024 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research.

Emneord
—Explainable artificial intelligence, Counterfactual explanation, Anomaly detection, Principal component analysis
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-66504 (URN)
Konferanse
2024 8th International Conference on Artificial Intelli-gence, Automation and Control Technologies (AIACT 2024)
Forskningsfinansiär
Knowledge Foundation, No 20200132 01 H
Merknad

In press

Tilgjengelig fra: 2024-04-24 Laget: 2024-04-24 Sist oppdatert: 2024-04-26bibliografisk kontrollert
2. Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
Åpne denne publikasjonen i ny fane eller vindu >>Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
2022 (engelsk)Inngår i: 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, s. 27-34Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2022
Emneord
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)
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-60550 (URN)10.1007/978-3-031-16407-1_4 (DOI)000869718800004 ()2-s2.0-85140472723 (Scopus ID)9783031164064 (ISBN)
Konferanse
WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022, Gyenongju, South Korea, 25-29 September, 2022
Tilgjengelig fra: 2022-11-03 Laget: 2022-11-03 Sist oppdatert: 2024-04-26bibliografisk kontrollert
3. Sample size prediction for anomaly detection in locks
Åpne denne publikasjonen i ny fane eller vindu >>Sample size prediction for anomaly detection in locks
2023 (engelsk)Inngår i: Procedia CIRP, Elsevier B.V. , 2023, s. 870-874Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Artificial intelligence in manufacturing systems is currently most used for quality control and predictive maintenance. In the lock industry, quality control of final assembled cylinder lock is still done by hand, wearing out the operators' wrists and introducing subjectivity which negatively affects reliability. Studies have shown that quality control can be automated using machine-learning to analyse torque measurements from the locks. The resulting performance of the approach depends on the dimensionality and size of the training dataset but unfortunately, the process of gathering data can be expensive so the amount collected data should therefore be minimized with respect to an acceptable performance measure. The dimensionality can be reduced with a method called Principal Component Analysis and the training dataset size can be estimated by repeated testing of the algorithms with smaller datasets of different sizes, which then can be used to extrapolate the expected performance for larger datasets. The purpose of this study is to evaluate the state-of-the-art methods to predict and minimize the needed sample size for commonly used machine-learning algorithms to reach an acceptable anomaly detection accuracy using torque measurements from locks. The results show that the learning curve with the best fit to the training data does not always give the best predictions. Instead, performance depends on the amount of data used to create the curve and the particular machine-learning algorithm used. Overall, the exponential and power-law functions gave the most reliable predictions and the use of principal component analysis greatly reduced the learning effort for the machine-learning algorithms. With torque measurements from 50-150 locks, we predicted a detection accuracy of over 95% while the current method of using the human tactile sense gives only 16% accuracy.

sted, utgiver, år, opplag, sider
Elsevier B.V., 2023
Emneord
Anomaly detection, Learning curves, Machine learning, Quality control, Sample size prediction
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66095 (URN)10.1016/j.procir.2023.09.090 (DOI)2-s2.0-85184582755 (Scopus ID)
Konferanse
Procedia CIRP
Tilgjengelig fra: 2024-02-20 Laget: 2024-02-20 Sist oppdatert: 2024-04-26bibliografisk kontrollert

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