Compilation of cutting load spectrum for CNC lathe based on DBN-BP prediction model

Abstract

Load spectrum can effectively represent variations in statistical load, and it is the foundation data for the overall product’s reliability test, reliability design and fatigue life analysis. However, compiling a load spectrum of small sample conditions is difficult because of the complicated and variable load conditions of machine tools and kaleidoscopic cutting process. Additionally, extreme load extrapolating is susceptible to subjective influences, thereby resulting in some uncertainties. Therefore, this study proposes a cutting load spectrum compilation method for computerised numerical control (CNC) lathe based on deep belief network-back propagation (DBN-BP) load prediction model, combining multi-source load condition information with deep learning. Firstly, single-layer BP and multi-layer restricted Boltzmann machine (RBM) were used to create a cutting load prediction model. Mixed distribution of mean and range of dynamic cutting load and parameters in copula function are predicted by using cutting process parameters. The number of hidden layers and single-layer nodes in the prediction model were determined using mean average (MA) precision and training time. Secondly, dynamic cutting load conditions were divided into the prediction model’s training, test and prediction sets; and the model’s accuracy was verified using comparative tests. Lastly, the two-dimensional mean-range matrix of load in each typical working condition was compiled using the prediction model, and the cutting load spectrum and two-dimensional CNC lathe programme loading spectrum were constructed. The proposed method saves time and personnel for numerous cutting tests when working with small samples and also assures load prediction accuracy.