User Demand Prediction and Solution Generation in Textile Product Development and Design Based on Machine Learning
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Abstract
Accurate translation of heterogeneous user requirements into manufacturable design solutions remains a challenging task in intelligent product engineering systems. This study proposes a process-constrained multimodal conditional variational autoencoder (PC-MCVAE) framework for demand representation, solution generation, and engineering feasibility optimization. The proposed architecture integrates multimodal information from textual descriptions and visual inputs through contrastive representation learning to construct a unified latent demand space. To incorporate domain knowledge into the generation process, a process knowledge graph is transformed into differentiable constraint functions that characterize feasible manufacturing regions and are embedded directly into the optimization objective. A conditional variational decoder is then employed to generate hybrid design solutions consisting of discrete structural representations and continuous engineering parameters. Furthermore, an end-to-end training strategy is developed to jointly optimize semantic consistency and process compliance. Experimental results demonstrate that the proposed framework achieves a semantic alignment score of 0.782, a process compliance rate of 91.4%, and a Top-3 user preference prediction accuracy of 83.7%. The model exhibits strong robustness across different product categories and application scenarios while maintaining high generation quality and manufacturing feasibility. The proposed framework provides an effective methodology for multimodal information fusion, knowledge-guided generative modeling, and intelligent decision support in engineering-oriented design systems.
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