Faulty Process Detection Using Machine Learning Techniques

Amin Karimi Dastgerdi; Dirk Lange; Paolo Mercorelli

doi:10.1007/978-981-99-2468-4_25

Faulty Process Detection Using Machine Learning Techniques

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

Faulty Process Detection Using Machine Learning Techniques. / Dastgerdi, Amin Karimi; Lange, Dirk; Mercorelli, Paolo.
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

Harvard

Dastgerdi, AK, Lange, D & Mercorelli, P 2023, Faulty Process Detection Using Machine Learning Techniques. in JC Bansal, H Sharma & A Chakravorty (Hrsg.), Congress on Smart Computing Technologies : Proceedings of CSCT 2022. Smart Innovation, Systems and Technologies, Springer Singapore, S. 321-333, Congress on Smart Computing Technologies, 2022, New Delhi, Indien, 11.12.22. https://doi.org/10.1007/978-981-99-2468-4_25

APA

Dastgerdi, A. K., Lange, D., & Mercorelli, P. (2023). Faulty Process Detection Using Machine Learning Techniques. In J. C. Bansal, H. Sharma, & A. Chakravorty (Hrsg.), Congress on Smart Computing Technologies : Proceedings of CSCT 2022 (S. 321-333). (Smart Innovation, Systems and Technologies). Springer Singapore. https://doi.org/10.1007/978-981-99-2468-4_25

Vancouver

Dastgerdi AK, Lange D, Mercorelli P. Faulty Process Detection Using Machine Learning Techniques. in Bansal JC, Sharma H, Chakravorty A, Hrsg., Congress on Smart Computing Technologies : Proceedings of CSCT 2022. Springer Singapore. 2023. S. 321-333. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-99-2468-4_25

Bibtex

@inbook{8a631c4d56254b8185d1f38086471d6d,

title = "Faulty Process Detection Using Machine Learning Techniques",

abstract = "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.",

keywords = "Drill tool breakage, Faulty process detection, Machine learning, Tool condition monitoring, Engineering",

author = "Dastgerdi, {Amin Karimi} and Dirk Lange and Paolo Mercorelli",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Congress on Smart Computing Technologies, 2022, CSCT 2022 ; Conference date: 11-12-2022 Through 12-12-2022",

year = "2023",

month = jul,

day = "11",

doi = "10.1007/978-981-99-2468-4_25",

language = "English",

isbn = "978-981-99-2467-7",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Singapore",

pages = "321--333",

editor = "Bansal, {Jagdish Chand} and Harish Sharma and Antorweep Chakravorty",

booktitle = "Congress on Smart Computing Technologies",

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RIS

TY - CHAP

T1 - Faulty Process Detection Using Machine Learning Techniques

AU - Dastgerdi, Amin Karimi

AU - Lange, Dirk

AU - Mercorelli, Paolo

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

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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 -

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