Prediction of the machined surface quality of ball-end milling of H13 die steel using MLBP method. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Li, Yueen; Zhao, Jun; Zhang, Haiyan H.
• Is Part Of: Machining science and technology. Volume 23:Issue 5(2019)
• Page Start: 794
• Page End: 823
• Publication Date: 2019-09-03
• Subjects: BP -- HSM -- MLBP -- multi-linear regression -- prediction -- surface quality; Machining -- Periodicals; 671.3505
• DOI: 10.1080/10910344.2019.1636260
• Languages: English
• ISSNs: 1091-0344

Abstract

Abstract: Based on the characteristics of the surface quality prediction system of high-speed milling, the prediction model is used to predict the surface quality of analyzing the advantages of the two methods of using the multilinear and BP neural network model (MLBP) method. This article through the in-depth study of the surface quality, study the surface quality prediction based on the characteristics of multiinput multioutput nonlinear systems, respectively, established a linear regression equation, BP neural network model, and the surface quality of specific conditions to start prediction. The prediction results show that these prediction methods can play a special role as certain conditions. However, owing to the limitations of multiple linear regression and BP neural networks, their generalization ability and robustness cannot meet actual needs. Drawing on the idea of interpolation, and analyzing the advantages and disadvantages of linear regression and BP neural network to solve nonlinear problems, a new prediction method is developed. The main idea are to use interpolation method to insert preprediction under the premise of linear prediction; to process the values and obtain a unified prediction result from linear regression; to combine the experimental results from the pretreatment results; to use these input information as the input content of the BP neural network; to establish a training model based on the BP neural network model self-learning process. This training model predicts the quality of the machined surface. This method is abbreviated as the MLBP method. The experimental results and comparison of model prediction results show that this method can effectively improve the generalization ability and robustness of the prediction model, and further improve the model's prediction accuracy.