Authors:
Mariana Carvalho
1
;
Ana Borges
1
;
Alexandra Gavina
2
;
Lídia Duarte
1
;
Joana Leite
3
;
4
;
Maria Polidoro
5
;
6
;
Sandra Aleixo
6
;
7
and
Sónia Dias
8
;
9
Affiliations:
1
CIICESI, ESTG, Polytechnic of Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
;
2
Lema-ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, Porto, 4249-015, Portugal
;
3
Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal
;
4
CEOS.PP Coimbra, Polytechnic University of Coimbra, Bencanta, 3045-601 Coimbra, Portugal
;
5
ESTG, Polytechnic of Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
;
6
CEAUL – Centro de Estatı́stica e Aplicações da Universidade de Lisboa, Portugal
;
7
Department of Mathematics, ISEL – Instituto Superior de Engenharia de Lisboa, Portugal
;
8
ESTG, Instituto Politécnico de Viana do Castelo, Portugal
;
9
LIAAD-INESC TEC, Portugal
Keyword(s):
Textile Dyeing, Non-Conformity, Data Mining, Knowledge Discovery, Prediction, Random Forest, Gradient Boosted Trees.
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.