Simultaneous Simulation Study of Metal Product Design Based on Machine Learning Algorithm

With the rapid development of automation technology, the manufacturing industry is facing the transformation from traditional manufacturing to intelligent manufacturing. Manufacturing product design inspection is an important process to ensure product quality. With the development of computer technology, especially the continuous improvement and application of computer vision and deep learning algorithm theory, more and more companies apply computer vision to product design inspection to improve the automation of equipment, reduce the cost of employing people, and improve the accuracy and efficiency of product design inspection. In order to meet specific design requirements, this paper designs and implements a metal product design system based on machine learning design. The system is not only able to meet the needs of manual design, but also can use mature computer vision-related algorithms to complete the initial automatic design and manual modification, thus greatly improving the design efficiency. It is worth mentioning that the system also has good scalability, which lays a good foundation for the preparation of image datasets. On this basis, the detailed design and implementation inside each module is presented, using class diagrams and data tables to show the implementation of methods in the classes. Immediately afterwards, each function of the designed system is tested, describing the expected and actual results. Finally, a summary of the completed work is presented, and the next steps related to the image design system are envisioned.