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Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
Mälardalen University, Sweden.
Mälardalen University, Sweden.ORCID iD: 0000-0003-1597-6738
Mälardalen University, Sweden.ORCID iD: 0000-0002-9890-4918
Mälardalen University, Sweden.
2022 (English)In: APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 663)) / [ed] Kim, Duck Young; von Cieminski, Gregor; Romero, David, Springer Nature Switzerland , 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 Nature Switzerland , 2022. p. 27-34
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-63141DOI: 10.1007/978-3-031-16407-1_4Scopus ID: 2-s2.0-85140472723OAI: oai:DiVA.org:ri-63141DiVA, id: diva2:1730886
Conference
APMS 2022: Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action
Available from: 2023-01-25 Created: 2023-01-25 Last updated: 2023-01-25Bibliographically approved

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Bohlin, MarkusOlsson, Tomas

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