Research and Optimization Strategies for Automated Industrial Manufacturing Applications Based on Artificial Intelligence

Automated industrial manufacturing, driven by artificial intelligence, is essential in modern manufacturing, improving efficiency, product quality, and working conditions. The study “Research and Optimization Strategies for Automated Industrial Manufacturing Applications Based on Artificial Intelligence” explores the use of neural network technology in this field and proposes optimization strategies to further enhance manufacturing efficiency. The study begins by highlighting the significance of automated industrial manufacturing in the modern era, covering production line robots and intelligent supply chain management. It then delves into the application of neural networks in defect detection, proposing optimization strategies to improve efficiency and quality. The study introduces a method to solve the problem of defect identification in multiple targets and complex backgrounds by calculating the average of identification results. The study also discusses the implementation of neural networks in these strategies and provides recommendations for manufacturing companies to adopt these technologies. Furthermore, the reliability of the model is experimentally verified. The study emphasizes the continuous evolution of automated industrial manufacturing, with ongoing research and practice leading to improved efficiency, cost reduction, and sustainable development.