Material Performance Prediction and Mechanical Structure Optimization Design Algorithm Based on Multi-Source Heterogeneous Data Fusion

Based on DL (Deep Learning) technology, this paper develops an advanced algorithm which can accurately predict the properties of materials, and applies it to the optimal design of mechanical structures. Specifically, this paper adopts a prediction model based on RNN (Recurrent Neural Network), which can learn from a large number of data and predict the properties of materials. At the same time, combined with advanced mechanical structure optimization algorithm, the effectiveness of the model and optimization method is verified by simulation experiments. The experimental environment is set on a high-performance computing cluster, which ensures the accuracy and efficiency of the experiment. The experimental results show that the proposed material performance prediction model has excellent prediction accuracy, high generalization ability and robustness. In the aspect of mechanical structure optimization, by combining the prediction model and optimization algorithm, this paper realizes the remarkable improvement of structural performance and effectively reduces the cost. These results prove the practical application value of the proposed method in engineering design. This study not only provides new ideas and methods for materials science and mechanical engineering, but also lays a foundation for the wide application of DL in engineering design.