Scholarlink

<p>Weaving is a crucial technology in textile production. Rejection is an inherent aspect of the industrial output. The textile industry experiences significant wastage due to wrong assumptions about rejection. This study found that fabric allowance can be predicted from required gray fabrics by using logarithmic function. Similarly, required gray fabrics can be calculated from a linear equation with 99% goodness of fit. Crimp percentage and warp&#xa0;beam length (yards) are most informative for fabric production and rejection, derived from mutual information. The first six Principle Components Analysis (PCA) components account for 94% of the information, underscoring crucial features like required gray fabrics, fabric allowances, yarn density, and yarn fineness. Besides, the PC1–PC2 Biplot represents the required gray fabric, required finished fabric, and required warp&#xa0;beam length, which have the highest impact on the first principal components (PC1). Then, 14 classical machine learning techniques were applied to the datasets. Among these, random forest, decision tree, and LightGBM demonstrated the highest accuracy. The optimal hyperparameters for these best-performing algorithms were also selected using RandomizedSearchCV. Interestingly, traditional machine learning models achieved more than 95% accuracy without any data preprocessing. In contrast, artificial neural networks (ANN) require data preprocessing to achieve high accuracy rates. Additionally, adjusting hidden layers adjustment is crucial. A seven-layer ANN model with one hot encoded (OHE) and scaled with a min–max scaler demonstrates an accuracy exceeding 96%.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月24日

查看全文
  • Toufique Ahmed

摘要

Weaving is a crucial technology in textile production. Rejection is an inherent aspect of the industrial output. The textile industry experiences significant wastage due to wrong assumptions about rejection. This study found that fabric allowance can be predicted from required gray fabrics by using logarithmic function. Similarly, required gray fabrics can be calculated from a linear equation with 99% goodness of fit. Crimp percentage and warp beam length (yards) are most informative for fabric production and rejection, derived from mutual information. The first six Principle Components Analysis (PCA) components account for 94% of the information, underscoring crucial features like required gray fabrics, fabric allowances, yarn density, and yarn fineness. Besides, the PC1–PC2 Biplot represents the required gray fabric, required finished fabric, and required warp beam length, which have the highest impact on the first principal components (PC1). Then, 14 classical machine learning techniques were applied to the datasets. Among these, random forest, decision tree, and LightGBM demonstrated the highest accuracy. The optimal hyperparameters for these best-performing algorithms were also selected using RandomizedSearchCV. Interestingly, traditional machine learning models achieved more than 95% accuracy without any data preprocessing. In contrast, artificial neural networks (ANN) require data preprocessing to achieve high accuracy rates. Additionally, adjusting hidden layers adjustment is crucial. A seven-layer ANN model with one hot encoded (OHE) and scaled with a min–max scaler demonstrates an accuracy exceeding 96%.