Design of 3D printing graphics reconstruction and data processing intelligent system for personalized manufacturing

In this paper, an intelligent system design of 3D printing graphics reconstruction and data processing for personalized manufacturing is proposed. There are problems such as high design threshold and low data processing efficiency in the unified process. Therefore, a three-level cascade system architecture based on hybrid intelligence is constructed in this study, which includes requirement intelligent analysis layer, hybrid-driven modeling layer and adaptive process planning layer. The requirement analysis module realizes efficient analysis of natural language and sketch input through improved BERT model and CNN-Transformer hybrid encoder. Hybrid-driven modeling module combines Generative Adversarial Network (GAN) with geometric constraint optimization to improve the quality and efficiency of model reconstruction. Adaptive process planning module adopts curvature sensitive slicing algorithm and graph theory-based supporting structure generation strategy to optimize printing process. The experimental results show that the system is significantly superior to traditional methods in requirements analysis accuracy, model reconstruction quality and process planning optimization. The end-to-end processing time is controlled within 146s, and the peak value of GPU memory is 5.2GB, which can efficiently respond to personalized manufacturing requirements and provide strong support for the application of 3D printing technology in complex customized scenes.