Research on Body Material and Process Optimization Based on Big Data and Deep Learning

This study takes body material and process optimization as the core, and proposes an efficient anomaly detection algorithm and optimization scheme. By constructing a deep learning model, a large amount of data in the body production process is feature extracted and analyzed to achieve precise control of material and process parameters. The designed anomaly detection algorithm can monitor the quality changes of body materials in real time during production, effectively identify and exclude abnormal data, thereby improving the reliability and consistency of the production process. CNN and LSTM are used to model the production data, and the strength, toughness and other key performance indicators of the body material are predicted and analyzed through adaptive optimization parameters. In the simulation stage, the model is verified by combining multiple materials and process parameters. The experimental results show that the optimization model based on deep learning significantly improves the performance in body material selection and process control, and its prediction accuracy reaches more than 97%. Further data analysis shows that the model can effectively reduce manufacturing costs and shorten production cycles, providing reliable technical support for the intelligent and precise development of the automobile manufacturing industry.