2024
Autores
Carvalho, M; Borges, A; Gavina, A; Duarte, L; Leite, J; Polidoro, MJ; Aleixo, SM; Dias, S;
Publicação
Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024, Volume 1: KDIR, Porto, Portugal, November 17-19, 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.
2022
Autores
Brito, P; Dias, S;
Publicação
Abstract
2022
Autores
Dias, S; Brito, P;
Publicação
Analysis of Distributional Data
Abstract
2022
Autores
Dias, S; Brito, P;
Publicação
Analysis of Distributional Data
Abstract
2022
Autores
Dias, S; Brito, P;
Publicação
Analysis of Distributional Data
Abstract
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