Working-hours Prediction Method of CNC Machining of Structural Parts Based on Feature Clustering

doi:10.3901/JME.2023.15.232

Abstract

Abstract: Aiming at the problem of inaccurate results of working-hours quotas for complex products such as aerospace structural parts, this paper proposes a method for predicting the working-hours of CNC machining of structural parts based on feature clustering. Based on the product characteristics, material characteristics and processing characteristics of aerospace structural parts, the factors influencing the CNC machining working-hours of aerospace structural parts are analyzed, and the feature vector analysis method of working-hours influencing factors based on BERT model and K-Means algorithm is proposed. Based on K-Means clustering algorithm, the process feature vectors extracted from BERT model are grouped, and based on this grouping result, different BP neural network working-hours prediction models optimized by genetic algorithm are established, and then the accuracy of working-hours quotas is improved from both working-hours influencing factor feature analysis and network structure optimization. Finally, the model training and prediction are completed based on the historical process data, and the effectiveness of the proposed method is verified.

Key words: working-hours forecast, feature clustering, BERT model, GA_BP algorithm

CLC Number:

TP391

LIU Juan, LIU Jianhua, ZHUANG Cunbo, XU Lei, ZHAI Sikuan, GAO Qinglin. Working-hours Prediction Method of CNC Machining of Structural Parts Based on Feature Clustering[J]. Journal of Mechanical Engineering, 2023, 59(15): 232-246.

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