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In CNC machine tools, transient temperature variation in the headstock assembly is the major contributors for spindle thermal error. The compensation of thermal error is critical for ensuring the accuracy of machine tool. The performance of an error compensation system depends largely on the accuracy and robustness of the thermal error model. In the present work, a robust thermal error model is developed for minimizing the error in lateral direction of the spindle which significantly influences the geometrical accuracy of the workpiece. Analysis-of-variance (ANOVA) is applied to the results of the experiments in determining the percentage contribution of each individual temperature key point against a stated level of confidence. Based on the analysis of existing approaches for thermal error modeling of machine tools, an approach of LASSO (least absolute shrinkage and selection operator) is proposed in order to avoid the multi collinearity problem. The proposed method is an innovative variable selection method to remove redundant or unimportant temperature key points in the linear thermal error model and minimize the residual sum of squares. The predictive error model is found to have better robustness and accuracy in comparison to the combination of grey correlation and step wise linear regression for error compensation of CNC lathe.

Titanium nitride coatings are extensively adopted as an intermediate adhesion layer in the cutting tools because of its superior mechanical properties. The interdependence of each process parameter during the deposition of such a coating process is nonlinear, and hence, it becomes a challenge to determine the output responses without carrying out a wide range of experiments. So to minimize the experiments, Taguchi-based L9 design of experiments were employed in this study with three factors and three levels such as Argon (Ar): Nitrogen (N2) gas mixture, Pulsed direct current power, and deposition time for depositing titanium nitride thin films on silicon (100) and tungsten carbide substrates using Pulsed direct current magnetron sputtering technique, where conventional direct current magnetron sputtering cannot be deployed using titanium nitride target. Multiple output responses such as average thickness, surface roughness, nano-hardness, Young’s modulus, wear track deformation, and coefficient of friction were measured by carrying out the systematic investigations, and a single optimum solution was obtained using Grey relational analysis. From the Grey relational analysis, the optimum Ar:N2 gas flow mixture, Pulsed direct current power, and deposition time for improved titanium nitride adhesion layer are 300 W, 10:5 sccm, and 5 min, respectively. Further, grazing incidence x-ray diffractometer profiles of deposited films exhibits (111) and (200) reflections corresponding to the titanium nitride phase, and the morphological analysis also revealed the existence of strongly faceted nano-grains with a triangular-shaped morphology.