Prediction and Optimization of Mechanical Manufacturing Quality Based on Deep Learning

The application of deep learning technology in mechanical design parameter optimization has opened up new research directions. The limitations of traditional design methods, especially in the search for global optimal solutions, have prompted researchers to explore more efficient solutions. Deep learning, with its outstanding performance in pattern recognition and predictive analysis, is considered a powerful tool for mechanical design optimization. Through comparative analysis and case studies, the accuracy of deep learning models in predicting mechanical manufacturing quality and their potential application in design parameter optimization were demonstrated. Through experiments, it was found that the deep learning technology quality prediction system predicted 187 perfect products, 145 medium products, and 147 defective products for mechanical quality, with a prediction error of 21, and a high overall judgment accuracy rate. It can be seen that deep learning methods can significantly improve design efficiency, reduce costs, and improve the overall performance of mechanical systems. This study aimed to provide an efficient optimization strategy for the field of mechanical design to promote the improvement of performance and reliability.