Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features

Abstract

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling) the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).

Classification of Textile Polymer Composites: Recent Trends and Challenges

“…At CRISP-DM stage, an automated machine learning (AutoML) algorithm was performed to choose optimal regression model among six different machine learning algorithms. The results demonstrated that significantly better output was achieved by the selected codes for fixed sequence of yarns and fabric finishing treatment [ 17 ]. In an experimental study, Hussain et al proposed a novel machine learning algorithm depends on transfer learning and data augmentation in order to recognize and classify the complex patterns of woven textiles.…”

“…They used time-series data for the simulation and forecasting of this problem and classified it with different machine learning algorithms [ 16 ]. Ribeiro et al proposed an automatic method to predict different properties of woven fabrics based on design and finishing features [ 17 ]. Due to the complexities of their micro-structures and boundary conditions, the classification of overall characteristics of textiles and polymer composites is still a challenging task even for machine learning.…”

An Automated Machine Learning Approach for Predicting Chemical Laboratory Material Consumption

“…The methodology involves both business and ML experts and includes six main phases: business understanding, data understanding, data preparation, modeling, evaluation and deployment. In previous works, we have employed CRISP-DM to successfully model the business needs of textile [18] and chemical [20] companies.…”

Uso de Aprendizado de Máquina Automatizado para Seleção de Provedores de Nuvem

“…Com o estudo,é possível o melhor entendimento da solução desse problema por meio do Aprendizado de Máquina Automatizado, além das técnicas, aplicações e funcionamento do pipeline de desenvolvimento utilizando o AutoML. Para a predição de propriedades físicas de tecidos entrelaçados em [Ribeiro et al 2020],é necessária a análise de estratégias para a fabricação, design e acabamento para a produção de tecidos. O trabalho usa um procedimento de Aprendizado de Máquina Automatizado (AutoML) para selecionar o melhor modelo de regressão a partir dos dados obtidos pela iteração de dois Processos Padrões Entre Setores para Mineração de Dados, do inglês CRoss-Industry Standard Process for Data Mining (CRISP-DM) de uma companhia têxtil.…”