Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries

Hye Kyung Choi; Whan Lee; Seyed Mohammad Mehdi Sajadieh; Sang Do Noh; Hyun Sik Son; Seung Bum Sim

doi:10.1007/978-3-031-62582-4_20

Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries

Hye Kyung Choi
, Whan Lee
, Seyed Mohammad Mehdi Sajadieh
, Sang Do Noh
, Hyun Sik Son
, Seung Bum Sim

Department of Systems Management Engineering

Sungkyunkwan University
DYETEC Institute
Korea Textile Development Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In smart manufacturing, data-driven artificial intelligence algorithms are becoming increasingly important in improving decision-making by monitoring the control, analysis, and prediction of manufacturing processes in a production system. In the textile industry, there is a strong need for smart manufacturing technologies because various parameters could affect the quality dynamically. This study aims to optimize the parameters of spinning processes by developing machine learning algorithms and models which can predict the toughness and elasticity of threads. At first, meaningful variables are extracted from the shop floor data, and then a defect classification learning model is developed to predict defects in advance. In addition, a regression model is implemented for the prediction of toughness and elasticity of the textile. By transitioning from the traditional trial and error method to the data-based method for the spinning process, production costs and time can be reduced through optimal settings of the production parameters for the spinning of the desired threads.

Original language	English
Title of host publication	Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers
Editors	Christophe Danjou, Ramy Harik, Felix Nyffenegger, Louis Rivest, Abdelaziz Bouras
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	221-232
Number of pages	12
ISBN (Print)	9783031625817
DOIs	https://doi.org/10.1007/978-3-031-62582-4_20
State	Published - 2024
Event	20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023 - Montreal, Canada Duration: 9 Jul 2023 → 12 Jul 2023

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	702 IFIPAICT
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023
Country/Territory	Canada
City	Montreal
Period	9/07/23 → 12/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

data-driven prediction
smart manufacturing
spinning process
textile industry

Access to Document

10.1007/978-3-031-62582-4_20

Cite this

Choi, H. K., Lee, W., Sajadieh, S. M. M., Noh, S. D., Son, H. S., & Sim, S. B. (2024). Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries. In C. Danjou, R. Harik, F. Nyffenegger, L. Rivest, & A. Bouras (Eds.), Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers (pp. 221-232). (IFIP Advances in Information and Communication Technology; Vol. 702 IFIPAICT). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-62582-4_20

Choi, Hye Kyung ; Lee, Whan ; Sajadieh, Seyed Mohammad Mehdi et al. / Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries. Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers. editor / Christophe Danjou ; Ramy Harik ; Felix Nyffenegger ; Louis Rivest ; Abdelaziz Bouras. Springer Science and Business Media Deutschland GmbH, 2024. pp. 221-232 (IFIP Advances in Information and Communication Technology).

@inproceedings{e96b9c2dbfcf40f0bca720cbcec7d24b,

title = "Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries",

abstract = "In smart manufacturing, data-driven artificial intelligence algorithms are becoming increasingly important in improving decision-making by monitoring the control, analysis, and prediction of manufacturing processes in a production system. In the textile industry, there is a strong need for smart manufacturing technologies because various parameters could affect the quality dynamically. This study aims to optimize the parameters of spinning processes by developing machine learning algorithms and models which can predict the toughness and elasticity of threads. At first, meaningful variables are extracted from the shop floor data, and then a defect classification learning model is developed to predict defects in advance. In addition, a regression model is implemented for the prediction of toughness and elasticity of the textile. By transitioning from the traditional trial and error method to the data-based method for the spinning process, production costs and time can be reduced through optimal settings of the production parameters for the spinning of the desired threads.",

keywords = "data-driven prediction, smart manufacturing, spinning process, textile industry",

author = "Choi, \{Hye Kyung\} and Whan Lee and Sajadieh, \{Seyed Mohammad Mehdi\} and Noh, \{Sang Do\} and Son, \{Hyun Sik\} and Sim, \{Seung Bum\}",

note = "Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2024.; 20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023 ; Conference date: 09-07-2023 Through 12-07-2023",

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doi = "10.1007/978-3-031-62582-4\_20",

language = "English",

isbn = "9783031625817",

series = "IFIP Advances in Information and Communication Technology",

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booktitle = "Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers",

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Choi, HK, Lee, W, Sajadieh, SMM, Noh, SD, Son, HS & Sim, SB 2024, Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries. in C Danjou, R Harik, F Nyffenegger, L Rivest & A Bouras (eds), Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers. IFIP Advances in Information and Communication Technology, vol. 702 IFIPAICT, Springer Science and Business Media Deutschland GmbH, pp. 221-232, 20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023, Montreal, Canada, 9/07/23. https://doi.org/10.1007/978-3-031-62582-4_20

Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries. / Choi, Hye Kyung; Lee, Whan; Sajadieh, Seyed Mohammad Mehdi et al.
Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers. ed. / Christophe Danjou; Ramy Harik; Felix Nyffenegger; Louis Rivest; Abdelaziz Bouras. Springer Science and Business Media Deutschland GmbH, 2024. p. 221-232 (IFIP Advances in Information and Communication Technology; Vol. 702 IFIPAICT).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries

AU - Choi, Hye Kyung

AU - Lee, Whan

AU - Sajadieh, Seyed Mohammad Mehdi

AU - Noh, Sang Do

AU - Son, Hyun Sik

AU - Sim, Seung Bum

N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2024.

PY - 2024

Y1 - 2024

N2 - In smart manufacturing, data-driven artificial intelligence algorithms are becoming increasingly important in improving decision-making by monitoring the control, analysis, and prediction of manufacturing processes in a production system. In the textile industry, there is a strong need for smart manufacturing technologies because various parameters could affect the quality dynamically. This study aims to optimize the parameters of spinning processes by developing machine learning algorithms and models which can predict the toughness and elasticity of threads. At first, meaningful variables are extracted from the shop floor data, and then a defect classification learning model is developed to predict defects in advance. In addition, a regression model is implemented for the prediction of toughness and elasticity of the textile. By transitioning from the traditional trial and error method to the data-based method for the spinning process, production costs and time can be reduced through optimal settings of the production parameters for the spinning of the desired threads.

AB - In smart manufacturing, data-driven artificial intelligence algorithms are becoming increasingly important in improving decision-making by monitoring the control, analysis, and prediction of manufacturing processes in a production system. In the textile industry, there is a strong need for smart manufacturing technologies because various parameters could affect the quality dynamically. This study aims to optimize the parameters of spinning processes by developing machine learning algorithms and models which can predict the toughness and elasticity of threads. At first, meaningful variables are extracted from the shop floor data, and then a defect classification learning model is developed to predict defects in advance. In addition, a regression model is implemented for the prediction of toughness and elasticity of the textile. By transitioning from the traditional trial and error method to the data-based method for the spinning process, production costs and time can be reduced through optimal settings of the production parameters for the spinning of the desired threads.

KW - data-driven prediction

KW - smart manufacturing

KW - spinning process

KW - textile industry

UR - https://www.scopus.com/pages/publications/85200796542

U2 - 10.1007/978-3-031-62582-4_20

DO - 10.1007/978-3-031-62582-4_20

M3 - Conference contribution

AN - SCOPUS:85200796542

SN - 9783031625817

T3 - IFIP Advances in Information and Communication Technology

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EP - 232

BT - Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers

A2 - Danjou, Christophe

A2 - Harik, Ramy

A2 - Nyffenegger, Felix

A2 - Rivest, Louis

A2 - Bouras, Abdelaziz

PB - Springer Science and Business Media Deutschland GmbH

T2 - 20th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2023

Y2 - 9 July 2023 through 12 July 2023

ER -

Choi HK, Lee W, Sajadieh SMM, Noh SD, Son HS, Sim SB. Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries. In Danjou C, Harik R, Nyffenegger F, Rivest L, Bouras A, editors, Product Lifecycle Management. Leveraging Digital Twins, Circular Economy, and Knowledge Management for Sustainable Innovation - 20th IFIP WG 5.1 International Conference, PLM 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 221-232. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-031-62582-4_20

Machine Learning Algorithms for Process Optimization and Quality Prediction of Spinning in Textile Industries

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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