Identification of product definition patterns in mass customization by multi-information fusion weighted support vector machine

In mass customization, companies have built product families to enhance design efficiency and meet customer requirements. However, the complex and diverse customer requirements make the traditional process of mapping customer needs to product families challenging and heavily reliant on prior knowledge. To address this challenge, the mapping task is treated as a classification problem, with customer requirements as classification features and product families as category labels. Based on information theory, this study considers the information gain (IG) and mutual information (MI) between the classification features and the labels. The uncertainty relationship between the two is explored using grey relational analysis (GRA). A hybrid weighting matrix is constructed by combining the effects of these three aspects, which is then used to improve the calculation of the classical support vector machine (CSVM) kernel function, forming a multi-information fusion weighted support vector machine (MIFWSVM) model. This model can take new requirements as input and output product variants that may satisfy the customer. To demonstrate the effectiveness of the proposed method, a case study of a mechanical press company was reported, comparing the MIFWSVM model with classical classifiers and exploring the impact of different weighting methods on the performance of CSVM. The MIFWSVM model achieved an average accuracy of 0.9205 with a standard deviation of 0.0506 and a macro F1 score of 0.9032 with a standard deviation of 0.0589, outperforming other methods. These results indicate that the MIFWSVM model significantly improves the accuracy and stability of customer demand mapping.