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.

中文翻译：

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H13模具钢球头铣削加工表面质量的MLBP方法预测

摘要 根据高速铣削表面质量预测系统的特点，利用预测模型对表面质量进行预测，分析了使用多线性和BP神经网络模型(MLBP)方法两种方法的优点。本文通过对表面质量的深入研究，研究了基于多输入多输出非线性系统特性的表面质量预测，分别建立了线性回归方程、BP神经网络模型，并从具体条件下的表面质量入手预言。预测结果表明，这些预测方法可以在一定条件下发挥特殊作用。然而，由于多元线性回归和 BP 神经网络的局限性，它们的泛化能力和鲁棒性不能满足实际需要。借鉴插值的思想，分析线性回归和BP神经网络解决非线性问题的优缺点，提出了一种新的预测方法。主要思想是在线性预测的前提下，利用插值的方法插入预测；对数值进行处理，从线性回归中得到统一的预测结果；结合预处理结果的实验​​结果；将这些输入信息作为BP神经网络的输入内容；建立基于BP神经网络模型自学习过程的训练模型。该训练模型预测加工表面的质量。该方法简称为MLBP方法。