Faulty Process Detection Using Machine Learning Techniques
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Congress on Smart Computing Technologies : Proceedings of CSCT 2022. Hrsg. / Jagdish Chand Bansal; Harish Sharma; Antorweep Chakravorty. Springer Singapore, 2023. S. 321-333 (Smart Innovation, Systems and Technologies).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Faulty Process Detection Using Machine Learning Techniques
AU - Dastgerdi, Amin Karimi
AU - Lange, Dirk
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023/7/11
Y1 - 2023/7/11
N2 - These days, a shortage of resources leads to the adaptation of new digital manufacturing systems. These systems are playing a critical role in improving productivity and quality assurance in real-time process monitoring tasks and are being designed to monitor a production process and aim to save resources, energy, and time. This becomes even more vital in drilling processes to be as optimized and accurate as possible since it often comes last in the processes and a tiny error could lead to undesirable drilling part breakage. It could be even more costly in case the drilling part itself is in an inappropriate condition or damaged. Especially, if the part has a long production time and, in the end, the drilling or threading process goes wrong. Therefore, faulty processes must be forecasted sufficiently in advance to prevent damage and further costs. In this study, a set of machine learning algorithms have been used to develop an analysis of industrial manufacturing processes to detect faulty processes with the purpose of tool and machine protection as well as product quality assurance. The results of this study show that machine learning algorithms can detect faulty processes in the production process with high accuracy.
AB - These days, a shortage of resources leads to the adaptation of new digital manufacturing systems. These systems are playing a critical role in improving productivity and quality assurance in real-time process monitoring tasks and are being designed to monitor a production process and aim to save resources, energy, and time. This becomes even more vital in drilling processes to be as optimized and accurate as possible since it often comes last in the processes and a tiny error could lead to undesirable drilling part breakage. It could be even more costly in case the drilling part itself is in an inappropriate condition or damaged. Especially, if the part has a long production time and, in the end, the drilling or threading process goes wrong. Therefore, faulty processes must be forecasted sufficiently in advance to prevent damage and further costs. In this study, a set of machine learning algorithms have been used to develop an analysis of industrial manufacturing processes to detect faulty processes with the purpose of tool and machine protection as well as product quality assurance. The results of this study show that machine learning algorithms can detect faulty processes in the production process with high accuracy.
KW - Drill tool breakage
KW - Faulty process detection
KW - Machine learning
KW - Tool condition monitoring
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85168996986&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/0f74059d-3666-3c6a-a64f-0f390f1ab81e/
U2 - 10.1007/978-981-99-2468-4_25
DO - 10.1007/978-981-99-2468-4_25
M3 - Article in conference proceedings
AN - SCOPUS:85168996986
SN - 978-981-99-2467-7
SN - 978-981-99-2470-7
T3 - Smart Innovation, Systems and Technologies
SP - 321
EP - 333
BT - Congress on Smart Computing Technologies
A2 - Bansal, Jagdish Chand
A2 - Sharma, Harish
A2 - Chakravorty, Antorweep
PB - Springer Singapore
T2 - Congress on Smart Computing Technologies, 2022
Y2 - 11 December 2022 through 12 December 2022
ER -