Application of Combined Prediction Model in Surface Roughness Prediction

Abstract

In order to improve the stability work piece surface roughness prediction model after machining, LM algorithm, least squares algorithm and proportional conjugate gradient algorithm are used to build the prediction model of the cutting process of AL-7075 aluminum alloy. Two error analysis methods are used to compare the combination model with other single models. The study found that the forecasting model is more accurate and stable than the single forecasting model, and closer to the actual measurement results. The combination model provides a new way to predict the surface roughness of the work piece after machining and provides a theoretical basis for selecting the three main factors of machining.