Optimization of the Polishing Efficiency and Torque by Using Taguchi Method and ANOVA in Robotic Polishing

Abstract

Surface finishing and polishing are important quality assurance processes in many manufacturing industries. A polished surface (low surface roughness) is linked with many useful properties other than providing an appealing gloss to the product, such as surface friction, electrical and chemical resistance, thermal conductivity, reflection, and product life. All these properties require an efficient polishing system working with the best machining parameters. This study analyzed the effects of the different input polishing parameters on the polishing efficiency and torque in the robotic polishing system for the circular-shaped workpieces (such as ring, cylinder, sphere, cone, etc.) by using the Taguchi method and analysis of variance (ANOVA). A customized rotatory passive gripper is designed to hold the watch bezel during polishing. Under the design of experiments (DOE) technique, Taguchi’s L 18 array is selected to find the optimized process parameters for polishing efficiency (based on surface roughness) and torque. Experimental results with the statistical analysis by signal-to-noise ratio and ANOVA (95% confidence level) confirms that the polishing force and tool speed are the most influencing parameter for polishing efficiency in the system. Linear regression equations are modeled for the polishing efficiency and torque. Finally, a confirmation test is conducted for the validation of the experimentation results against actual results.

Characterization and In Vitro Studies of Low Reflective Magnetite (Fe3O4) Thin Film on Stainless Steel 420A Developed by Chemical Method

“…The signal-to-noise (S/N) ratio was performed using Minitab-17 software. It is mainly categorized according to the required output char-acteristics, that are lower-the-better, higher-the-better, or nominal-the-better [20]. Average percentage reflectance (R) should follow lower-the-better criteria, since lower R value is appreciable to obtain a low-reflective coating on SS 420A substrate surface.…”

A comparative evaluation of design factors on bubble column operation in photosynthetic biogas upgrading

“…In the ANOVA table, the degrees of freedom of a source or a model signifies the number of independent inputs required to describe itself (Montgomery, 2012). The F-value signifies the extent to which the variance due to a certain source compares with variance arising from the errors, i.e., how large or small the variations due to a certain source is with respect to the unexplained variations (Mohsin et al, 2020). Irrespective of its value, to evaluate if the F value as obtained is true, the corresponding P-value is calculated as the probability of the F value being obtained by random chance (Montgomery, 2012;Mohsin et al, 2020).…”

“…The F-value signifies the extent to which the variance due to a certain source compares with variance arising from the errors, i.e., how large or small the variations due to a certain source is with respect to the unexplained variations (Mohsin et al, 2020). Irrespective of its value, to evaluate if the F value as obtained is true, the corresponding P-value is calculated as the probability of the F value being obtained by random chance (Montgomery, 2012;Mohsin et al, 2020). Thus, a higher P-value indicates a greater probability of obtaining the F-value as a random chance and hence, a lower statistical significance of the calculated F-value.…”

“…Fcrit, given in the form of F α(k-1,n ex -k) can be obtained from standard statistical handbooks, where k is the number of predictors in the model and nex is the total number of experimental trials. The respective model P value signifies the probability that the model Fvalue was derived from the influence of noise, and hence, a lower P-value is always desired (Kishore et al, 2018;Mohsin et al, 2020). The details of each of these statistics can be further explored in multiple pieces of literature and textbooks (Roy, 2010; Montgomery, 2012).…”

Minimizing the Casting Defects in High Pressure Die Casting Using Taguchi Analysis

“…An optimal set of parameters was proposed to minimize defects in the final product. Mohsin et al [39] used the Taguchi method and ANOVA to investigate the significance of various input polishing process parameters on the polishing efficiency and torque in a robotic polishing system.…”