Optimization of LSTM based on Gray Wolf Optimization Algorithm for Part Error Compensation

In the field of precision manufacturing, error compensation of parts is the key to improve product quality and manufacturing efficiency. This paper presents a Long Short-Term Memory Network (LSTM) model based on the Gray Wolf optimization algorithm designed to optimize part error compensation. First, we introduce the sources of part errors and their impact on the manufacturing process. Then, we elaborate the application of LSTM network in predicting and compensating part errors by selecting appropriate features through correlation analysis. Through experiments, we verify the effectiveness of the Gray Wolf optimization-based LSTM model in part error prediction and compensation. The experimental results show that compared with the traditional method, the model in this paper has a significant improvement in both error prediction accuracy and compensation efficiency.