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Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2023 (English)In: International Conference on Agents and Artificial Intelligence, Science and Technology Publications, Lda , 2023, p. 368-379Conference paper, Published paper (Refereed)
Abstract [en]

Good performance of the Machine Learning (ML) model is an important requirement associated with ML-integrated manufacturing. An increase in performance improvement methods such as hyperparameter tuning, data size increment, feature extraction, and architecture change leads to random attempts while improving performance. This can result in unnecessary consumption of time and performance improvement solely depending on luck. In the proposed study, a quantitative performance analysis on the case study of chip detection is performed from six perspectives: hyperparameter change, feature extraction method, data size increment, and concatenated Artificial Neural Network (ANN) architecture. The focus of the analysis is to create a consolidated knowledge of factors affecting ML model performance in turning process quality prediction. Metal peels such as chips are designed at the time of metal cutting (turning process) and the shape of these chips indicates the quality of the turning process. The result of the proposed study shows that following a fixed recipe does not always improve performance. In the case of performance improvement, data quality plays the main role. Additionally, the choice of an ML algorithm and hyperparameter tuning plays an essential role in performance.

Place, publisher, year, edition, pages
Science and Technology Publications, Lda , 2023. p. 368-379
Keywords [en]
Machine Learning, Manufacturing System, Performance Analysis, Quantification
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-65799DOI: 10.5220/0011800100003393Scopus ID: 2-s2.0-85182554050OAI: oai:DiVA.org:mdh-65799DiVA, id: diva2:1833058
Conference
15th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 22-24 February, 2023
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-12-19Bibliographically approved

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Sheuly, Sharmin SultanaAhmed, Mobyen UddinBegum, Shahina

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