The gray relational analysis is an important technique can be effectively used for forecasting, decision making in different areas of manufacturing and products processing. In this research, the gray relational analysis has been employed to optimize the input process parameters of the developed laser-assisted jet electrochemical machine (LA-JECM) for better machining performance characteristics. Taguchi method-based design of experiment L 16 (4 4) orthogonal array was employed and experiments were carried out for investigation. The optimal parametric combination for multi-response optimization was identified based on the collective implementation of Taguchi methodology and gray relational analysis during microdrilling of Inconel-718. A LA-JECM has been developed and utilized for experimental investigation. The experimental results revealed that there is 29.16% increase in MRR; 48.43% decrease in taper and 36.83% reduction in surface roughness height, R a (lm) when experiments were carried out on LA-JECM over JECM. The laser assistance with JECM improves the machining quality and reduces machining time. Taguchi methodology and gray relational analysis based multi-optimization found that the parametric setting, i.e., at supply voltage 80 V, electrolyte concentration 40 g/l, inter-electrode gap 3 mm, and duty cycle 60% gives maximum material removal rate with minimum taper angle and surface roughness height (R a , lm) of the machined hole. Keywords Laser-assisted jet electrochemical machining Á Taguchi methodology Á Gray relational analysis Á Multi-response optimization Abbreviations JECM Jet electrochemical machining LA-JECM Laser-assisted jet electrochemical machining V S Supply voltage (X 1 , volt) E C Electrolyte concentration (X 2 , g/l) IEG Inter-electrode gap (X 3 , mm) D C Duty cycle (X 4 , %) MRR Material removal rate (mg/min) TAP Taper (degree) R a Surface roughness height (lm)
A firing pin impression is usually concave in shape with a small textured area, which makes it difficult to perform automated algorithm‐based comparison. The congruent matching cells (CMC) method was invented for accurate breech face impression comparison, in which a reference impression is divided into correlation cells. Each cell is registered to a cell‐sized area of the comparison impression that has maximum similarity in surface topography. Four parameters are used to quantify the congruent matching pattern of the registration position and orientation. This paper aims to further develop the cell‐division‐matching method based on a convergence feature and to develop practical convergence‐improved algorithms for firing pin impression comparison. The convergence feature refers to the tendency of the x‐y registration positions of correlated cell pairs to converge at the correct registration angle when comparing same‐source samples at different orientations. The areal Gaussian filter is employed to extract high‐frequency micro‐features; the least‐squares matching method is used to improve each cross‐correlation precision and reach convergence in the registration positions of correlated cell pairs; and a density‐based clustering algorithm is introduced to collect the registration positions of dense cell pairs relative to a virtual common center and to remove outliers. Improvements are achieved in the reliability and accuracy of the number of congruent matching cell pairs (CMCs) collected, which represents the quantification of the degree of pairwise impression similarity. Experiments in this report used 40 firing pin impression samples on cartridge cases fired from 10 pistols. The results included no false identifications or false exclusions.
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