Research on Precision Machining Error Compensation Algorithm Based on Machine Learning

In the field of precision machining, especially for mechanical thin-walled blades with complex geometric shapes, the precise control of machining errors is the key to achieve high quality products and improve production efficiency. In this paper, an error compensation algorithm based on machine learning is proposed, which aims to significantly reduce the errors in precision machining process through intelligent prediction and real-time correction. The algorithm design integrates deep learning model and traditional control theory to automatically learn and optimize compensation strategies based on historical processing data. The system simulation experiments were carried out in a virtual machining environment, covering the blade machining process with different materials and machining conditions. The experimental results show that the algorithm can reduce the machining error by 30% on average, and the error compensation rate is as high as 45% under certain conditions. In addition, the nonlinear relationship between machining parameters and error compensation effect is revealed, which provides a theoretical basis for optimizing machining parameters.