Predicting Fabric Consumption for Jeans Using Artificial Neural Network
Main Article Content
Abstract
Fabric consumption is a critical factor in industrial garment manufacturing, as fabric cost constitutes a substantial portion of total product cost. In the study, a systematically constructed dataset of jeans markers under multiple conditions was developed, and an Artificial Neural Network (ANN) model was proposed to predict fabric consumption based on fabric width, the number of garment components, and the number of garments per marker. The dataset was normalized and partitioned into two subsets: 70% for model training and 30% for testing. A four-layer feedforward ANN architecture with two hidden layers comprising 10 and 5 neurons, respectively, was employed, utilizing a unipolar sigmoid activation function and trained via the backpropagation algorithm. The evaluation results demonstrated high predictive accuracy, with a Mean Absolute Error (MAE) of 0.01, Root Mean Square Error (RMSE) of 0.02, and a coefficient of determination (R²) of 0.9296 on the test dataset. The close alignment between actual and predicted fabric consumption confirmed the model’s effectiveness. Compared with traditional linear models such as Bayesian Model Averaging (BMA), the ANN model exhibited superior capability in capturing nonlinear relationships among input variables. These results highlight the strong potential of ANN for practical applications in textile and apparel production, offering a fast and reliable approach for fabric planning.
Keywords
Artificial Neural Network, fabric comsumption for jeans, marker layout, prediction.
Article Details
References
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