This project aims to address key challenges in precision manufacturing by developing adaptive AI models for analyzing and predicting quality issues in manufacturing processes. The research focuses on building and optimizing multi-physical field coupling models that integrate diverse physical data such as temperature, stress, and vibration. Reinforcement learning techniques will be employed to continuously optimize manufacturing parameters based on real-time sensor feedback. Additionally, immersive visualization and mixed-reality environments will be used to provide actionable insights and validate material designs. This integrated approach will enhance manufacturing efficiency, adaptability, and scalability, offering solutions to modern industrial design challenges.