机加零件质量预测与工艺参数优化方法

A novel method based on machine learning algorithms was proposed to realize the quality prediction and the process parameter optimization, in order to reuse the process information and the inspection information of machining parts effectively. A model-based definition (MBD) model which was integrated with process information and inspection information was treated as input. Process and inspection parameter extraction based on the MBD model was developed and the corresponding structured data set was established through the secondary development of three-dimensional modeling software. Several classifiers in machine learning were used to construct the quality prediction model based on process parameters and quality classification labels. Combining the information gain algorithm, after sorting all process parameters, the process parameter that had the greatest impact on quality was selected. Quality prediction and process parameter optimization tool set was developed to realize the optimization of the selected parameter by using the gradient boost decision tree algorithm. The validity and the reliability of the proposed method were verified by the milling experiment data provided by an aviation company. Results show that the proposed method can realize the quality prediction and process parameter optimization of machining parts effectively.