A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85534 |
Resumo: | In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance. |
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A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabricsTextile developmentPredictiveRegressionEvolutionary multi-objective optimizationCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyIn this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020).SpringerUniversidade do MinhoRibeiro, RuiPilastri, AndreMoura, CarlaMorgado, JoseCortez, Paulo2023-05-032023-05-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85534engRibeiro, R., Pilastri, A., Moura, C. et al. A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics. Neural Comput & Applic 35, 17375–17395 (2023). https://doi.org/10.1007/s00521-023-08596-90941-064310.1007/s00521-023-08596-9https://link.springer.com/article/10.1007/s00521-023-08596-9info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-12T01:17:42Zoai:repositorium.sdum.uminho.pt:1822/85534Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:29:27.520428Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
title |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
spellingShingle |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics Ribeiro, Rui Textile development Predictive Regression Evolutionary multi-objective optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
title_short |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
title_full |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
title_fullStr |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
title_full_unstemmed |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
title_sort |
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics |
author |
Ribeiro, Rui |
author_facet |
Ribeiro, Rui Pilastri, Andre Moura, Carla Morgado, Jose Cortez, Paulo |
author_role |
author |
author2 |
Pilastri, Andre Moura, Carla Morgado, Jose Cortez, Paulo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ribeiro, Rui Pilastri, Andre Moura, Carla Morgado, Jose Cortez, Paulo |
dc.subject.por.fl_str_mv |
Textile development Predictive Regression Evolutionary multi-objective optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
topic |
Textile development Predictive Regression Evolutionary multi-objective optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
description |
In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-03 2023-05-03T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/85534 |
url |
https://hdl.handle.net/1822/85534 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ribeiro, R., Pilastri, A., Moura, C. et al. A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics. Neural Comput & Applic 35, 17375–17395 (2023). https://doi.org/10.1007/s00521-023-08596-9 0941-0643 10.1007/s00521-023-08596-9 https://link.springer.com/article/10.1007/s00521-023-08596-9 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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