Thermal Distortion Error Compensation for a Two-Axis CNC Machine Tool Using Regression Analysis

The major requirement of the manufacturing industry is to produce a high-precision machined component. The precision of the components depends on the accuracy of its geometry. The major geometrical error in the workpiece is due to the thermal expansion of machine components and the workpiece. The present work is focused on building a thermal error compensation model to reduce geometrical error in the workpiece. The model is developed based on a multiple linear regression technique, which simulates the correlation between several dependent and independent variables. The identification of the heat source and their locations needs to be considered as at most important parameter in the study. The ambient parameters such as surrounding temperature equally affect the machining accuracy of the machine. Hence, a robust multiple linear regression-based thermal compensation model has been proposed. It incorporates the significant heat sources and their locations on the machine along with the ambient parameters that affect the deviation between the tool and workpiece. Further, the accuracy of the model is enhanced by identifying the predominant parameter and assigning a higher weightage to it and eliminating the non-influencing parameters from the computation. The model developed based on these optimized parameters is tested for accuracy and validated by conducting the real-time thermal error compensation test on a CNC turning machine. Based on the results of several experimental studies, it is concluded that the radial positioning error decreases from ± 17 μm to ± 5 μm, which is a substantial improvement in the accuracy of the developed model.