Innovative Application of Intelligent Mechanical Manufacturing Based on Self-Supervised Learning and Graph Neural Network Fusion Optimization

Abstract

In the current context of industrial automation, with the rapid development of the Internet, artificial intelligence and other technologies, intelligent mechanical manufacturing technology has received widespread attention. The study builds an intelligent manufacturing optimization model using methods based on self-supervised learning and graph neural networks to address the issue of insufficient efficiency and accuracy in mechanical manufacturing. Based on the traditional manufacturing method, the comparison self-supervised learning model is utilized to mine the available data and monitor the quality of mechanical equipment by constructing pre-training tasks to improve the efficiency of equipment production. Then it is fused with graph neural networks to design intelligent manufacturing optimization model based on self-supervised learning and graph neural networks intelligent manufacturing. The experimental results indicated that the comprehensive performance and practical application of the research model were good on both. Compared to the three performance types of multi-behavior models on different datasets, the model improved by 10.8%, 8.5%, and 6.3%, respectively. The noise robustness of the research model improved by 5.36% and 6.27% compared to the NGCF and EHCF models, respectively. The performance of the research model reached its optimal state when the selected parameters were β=0.01, K=30, and L=2. The results demonstrate that the designed optimization model based on self-supervised learning and graph neural networks intelligent manufacturing has advantages in terms of operational efficiency and index performance. The research results are of great significance for intelligent mechanical manufacturing