A review of empirical modeling techniques to optimize machining parameters for hard turning applications

There has been a tremendous development in the field of modeling and optimization methods starting from Taylor’s tool life model. Use of costly tools such as polycrystalline cubic boron nitride, polycrystalline diamond and ceramics in high-end computer numerical control machining forces the researcher to minimize the experimental runs to achieve the best cutting conditions with minimum tool wear and overall production cost. Machining process optimization to achieve said objectives comprises selecting optimum cutting parameters by applying low-cost mathematical models. This article attempts to evaluate the applicability of various modeling and optimization methods to specific response parameters in hard turning problems. Various empirical modeling techniques such as linear regression modeling, artificial neural networks, polynomial and fuzzy modeling along with process optimization through Taguchi, response surface methodology and genetic algorithm for hard turning applications have been discussed in length to provide the production engineers a ready database to compare relative merits and suitability of these techniques for a particular machining application. Also, article discusses integration of different modeling and optimization techniques to achieve desired goals when a single optimization technique is not able to provide the acceptable solution. The last part of the article highlights the current trends in hard turning applications and research priorities for future work. </jats:p

In-process detection of chipping in ceramic cutting tools during turning of difficult-to-cut material using vision-based approach

Ceramic cutting tools are prone to failure by chipping and fracture rather than gradual wear mainly because of their low impact resistance. This results in poor surface finish and low dimensional accuracy of the machined parts. In this work, a vision-based approach has been developed to detect the onset of chipping in aluminum oxide ceramic cut-ting tools during the dry turning of AISI 01 oil-hardening tool steel. The profile of the workpiece surface opposite the cutting tool was captured during the turning using 18-megapixel DSLR camera at a shutter speed of 0.25 ms. The surface profile of the workpiece was extracted to sub-pixel accuracy using the invariant moment method. The effect of chipping in the ceramic cutting tools on the surface profile signature of the machined workpiece was investigated using autocorrelation analysis. Chipping in the ceramic tool was found to (i) cause the peaks of autocorrelation function of the workpiece profile to decrease rapidly as the lag distance increased and (ii) cause the envelope of the peaks of the autocorrelation function to deviate significantly from one another at different workpiece rotation angles. The sum of squared deviation (SSD) of the envelope of the peak of autocorrelation function was also found to increase sharply right after tool chipping. Significant variations in the SSD at different workpiece rotation angles were observed beyond the cutting time of 11.1 s because of the continuous chipping of the ceramic insert during turning