Enhancing Dyeing Processes with Machine Learning: Strategies for Reducing Textile Non-Conformities

Mariana Carvalho, Ana Borges, Alexandra Gavina, Lídia Duarte, Joana Leite, Joana Leite, Maria Polidoro, Maria Polidoro, Sandra Aleixo, Sandra Aleixo, Sónia Dias, Sónia Dias

2024

Abstract

The textile industry, a vital sector in global production, relies heavily on dyeing processes to meet stringent quality and consistency standards. This study addresses the challenge of identifying and mitigating non-conformities in dyeing patterns, such as stains, fading and coloration issues, through advanced data analysis and machine learning techniques. The authors applied Random Forest and Gradient Boosted Trees algorithms to a dataset provided by a Portuguese textile company, identifying key factors influencing dyeing non-conformities. Our models highlight critical features impacting non-conformities, offering predictive capabilities that allow for preemptive adjustments to the dyeing process. The results demonstrate significant potential for reducing non-conformities, improving efficiency and enhancing overall product quality.

Paper Citation

in Harvard Style

Carvalho M., Borges A., Gavina A., Duarte L., Leite J., Polidoro M., Aleixo S. and Dias S. (2024). Enhancing Dyeing Processes with Machine Learning: Strategies for Reducing Textile Non-Conformities. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 363-370. DOI: 10.5220/0012992800003838

in Bibtex Style

@conference{kdir24,
author={Mariana Carvalho and Ana Borges and Alexandra Gavina and Lídia Duarte and Joana Leite and Maria Polidoro and Sandra Aleixo and Sónia Dias},
title={Enhancing Dyeing Processes with Machine Learning: Strategies for Reducing Textile Non-Conformities},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012992800003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Enhancing Dyeing Processes with Machine Learning: Strategies for Reducing Textile Non-Conformities
SN - 978-989-758-716-0
AU - Carvalho M.
AU - Borges A.
AU - Gavina A.
AU - Duarte L.
AU - Leite J.
AU - Polidoro M.
AU - Aleixo S.
AU - Dias S.
PY - 2024
SP - 363
EP - 370
DO - 10.5220/0012992800003838
PB - SciTePress