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Comparison of Machi...
Comparison of Machine Learning’s- and Humans’- Ability to Consistently Classify Anomalies in Cylinder Locks
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- Andersson, Tim (författare)
- Mälardalens universitet,Inbyggda system,Mälardalen University, Sweden
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- Bohlin, Markus, 1976- (författare)
- Mälardalens universitet,Innovation och produktrealisering,Mälardalen University, Sweden
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- Olsson, Tomas (författare)
- Mälardalen University, Sweden
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- Ahlskog, Mats, 1970- (författare)
- Mälardalens universitet,Innovation och produktrealisering,Mälardalen University, Sweden
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(creator_code:org_t)
- 2022-09-19
- 2022
- Engelska.
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031164064 ; , s. 27-34, s. 27-34
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- 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)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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