Research on the Application of Data-Driven 3D Printing Technology in Product Design

This paper proposes a 3D printing optimization algorithm based on deep learning. By combining CNN and GAN, the efficiency of 3D printing path planning and structural design is improved. The algorithm uses CNN to plan the printing path and generates optimized support structures through GAN, thereby reducing printing time and material waste. Experimental results show that the printing time of the optimized system is reduced by 20%, and the printing accuracy is improved by 15%, especially in complex structures and some details. At the same time, the optimized material consumption is reduced by 18%. The system can automatically adjust the printing path and support structure according to the different characteristics of product design, maximizing the printing efficiency and quality. Through the real-time feedback mechanism, the system can dynamically adjust the printing parameters according to the sensor data during the printing process to further optimize the effect. The experiment verifies the effectiveness of the algorithm in practical applications, proving that it can significantly improve the comprehensive performance of 3D printing, especially in efficient customized production and complex design. It has strong application potential. This study provides new solutions for the customization and optimization of 3D printing technology and lays the foundation for its widespread application in the future industry.