Intelligent Decision-Making of Fine-grained Product Form with Feature Transfer

A fine-grained product form intelligent decision-making method based on hybrid transfer learning is proposed. The aim is to solve the problems of weak systematicity of the product form intelligent decision-making framework, single decision mechanism of the model, and small amount of historical sample data. The method uses Swin Transformer and ResNets as the backbone network to design three parallel hybrid transfer learning sub-networks, including product form classification network (Form-CN), product form deep regression evaluation network (Form-REN) and product form distribution fitting evaluation network (Form-DFEN). Firstly, Form-CN is applied to classify the products with fine-grained form to achieve the product form design location identification. Secondly, Form-REN is applied to predict and evaluate the overall product form semantics. Then, Form-DFEN is used to evaluate the product form by distribution fitting. Finally, Form-REN and Form-DFEN are used to complete the integrated decision making. Experiments were performed on the created hand drill dataset and compared with other classical models. The results show that the three designed networks achieved 99.0% accuracy, 0.4058 mean square error and 84.3% accuracy, respectively. The proposed method can finely and efficiently assist designers to make comprehensive intelligent decisions, which provides a more systematic reference framework for intelligent decision-making of product forms.