Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

Abstract

It is difficult to measure the surface roughness of large-pitch internal threads; predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in the scope of use. The homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using the Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and radial basis function neural networks (RBF-NNs) were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. It was verified that the absolute error of the SVM model was less than 0.05 μm, and the relative error was less than 4% for turning both left and right threaded surfaces, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness is difficult to measure due to structural characteristics.