Mechanical Automation Design and Manufacturing of Production Equipment Combined with Artificial Intelligence Technology and Algorithm Implementation

Abstract

Abstract This paper explores the impact of artificial intelligence and machine automation on productivity. It focuses on analyzing the production model of machinery automation based on Petri nets. Through the invariant analysis method, the article constructs a model and presents its structure as a correlation matrix. The study results show that the model has an error rate of only 2.26% and an accuracy of 90.14% in terms of operational performance. Regarding time performance, its training time is 3854 seconds and response time is 351 milliseconds, which are better than other algorithms. In the practical application of automotive parts production, the method significantly reduces the busy probability and blocking probability during order processing and material delivery, and also improves the equipment load rate, waiting rate and blocking rate in the production process. This indicates that the Petri net-based method of machine automation production has significant advantages in improving productivity and reducing costs.