Early prediction of fabric quality using machine learning to reduce rework in manufacturing processes

Sema Aydın; Koray Altun

doi:10.11121/ijocta.1462

Authors

Sema Aydın Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey https://orcid.org/0009-0001-3948-0529
Koray Altun Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey https://orcid.org/0000-0003-0357-9495

DOI:

https://doi.org/10.11121/ijocta.1462

Keywords:

Fabric quality, Rework reduction, machine learning, Artificial neural networks

Abstract

The increasing competition and rapid technological advancements in today's business world have raised customer expectations. People now expect quick delivery, low prices, and high-quality products. As a result, companies must adapt to this competitive environment to survive. Rework, which is a significant cost in production, increases expenses, reduces production efficiency, and can lead to customer attrition. Research shows various efforts across different sectors to reduce rework, although there is still a gap in the textile sector's fabric dyeing units. Common problems in these units include non-retentive colors, customer dissatisfaction with shades, and repeated dyeing due to environmental factors or dye vat issues. This study uses logistic regression and artificial neural networks models from machine learning to predict which fabrics will need rework, using data from a textile company in Bursa. The analysis indicates that artificial neural networks models perform better.

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Author Biographies

Sema Aydın, Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey

Sema Aydın holds a BSc degree in Industrial Engineering from Hacettepe University, and a MSc degree in Industrial Engineering from TU Bursa. She has a keen interest in AI applications in industry.

Koray Altun, Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey

Koray Altun is an Assistant Professor of Industrial Engineering at TU Bursa and a Research Collaborator at GLORAD. He is also the founder and manager of a startup focused on digital innovation and software consultancy. He holds a Ph.D. and a BSc degree in Industrial Engineering from Gaziantep University and Erciyes University, respectively. He has published papers in well-respected journals and has led consulting engagements in reputable companies with a focus on R&D, technology, and innovation management. His recent research interests include the Innovation Excellence Model, Systematic Innovation, Technology and Innovation Management, and R&D Management.

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