Abstract
Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.
Highlights
In recent years, requirements for individualized garments have increased rapidly, including clothing styles, colors, and fabrics
The artificial neural networks (ANNs) predictive models with different mathematical algorithms were designed, which used the key dimensions as the input variables and the detailed dimensions needed for garment pattern making as the output variables
In order to reveal the effects of the factors affecting the estimating performance of the models, including volume of training dataset, quantity of hidden neurons, parameter σ in the hidden layer, and momentum factor α, a series of experiments were conducted
Summary
Requirements for individualized garments have increased rapidly, including clothing styles, colors, and fabrics. Excellent garment fit is indispensable, which is considered to be a critical factor affecting garment wearing comfort. In today’s apparel industry, garment pattern making is a vital procedure of manufacturing well-fitting garments. Known as paper patterns, refer to paper or cardboard templates based on which the parts of the garment are draw on the fabric before cutting out. Sometimes called garment structure design, pattern design, pattern drafting, or pattern cutting, is a complicated technique, involving a.
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