Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy

Abstract

Nickel-based superalloys find their main use in missile engines, atomic devices, investigational aircraft, aerospace engineering, industrial applications, and automotive gas turbines, spacecraft petrochemical tools, steam power, submarines, and broader heating applications. These superalloys impose certain difficulties during the process fabrication owing to their levels of higher hardness. In the current study, the precise machining of Waspaloy was attempted through the wire electrical discharge machining (WEDM) technique. A multi-objective optimization has been performed, and the influence of multi-walled carbon nanotubes (MWCNTs) has been assessed using the passing vehicle search (PVS) algorithm. The effects of machining variables like current, Toff, and Ton were studied using the output measures of material removal rate (MRR), recast layer thickness (RLT), and surface roughness (SR). The Box–Behnken design was applied to generate the experimental matrix. Empirical models were generated which show the interrelationship among the process variables and output measures. The analysis of variance (ANOVA) method was used to check the adequacy, and suitability of the models and to understand the significance of the parameters. The PVS technique was executed for the optimization of MRR, SR, and RLT. Pareto fronts were derived which gives a choice to the user to select any point on the front as per the requirement. To enhance the machining performance, MWCNTs mixed dielectric fluid was utilized, and the effect of these MWCNTs was also analyzed on the surface defects. The use of MWCNTs at 1 g/L enhanced the performance of MRR, SR, and RLT by 65.70%, 50.68%, and 40.96%, respectively. Also, the addition of MWCNTs has shown that the machined surface largely reduces the surface defects.

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

“… Reference Methodology Adopted Improvements of this study Nain et al [ 29 ] Used SVM to predict the MRR and SR The MRR improved by 74.75 %, and SR 63.63 % Varun and Venkaiah [ 30 ] GRA coupled with GA The MRR enhanced by 95 %. Chaudhari et al [ 36 ] Optimization using PVS Algorithm The MRR improved by 98 %, however, SR depreciated by 60.94 % Chaudhari et al [ 37 ] Optimization using PVS Algorithm The MRR enhanced by 97.55 % and SR depreciated by 53.37 % …”

“…The authors concluded that by feeding the MWCNT in the dielectric during WEDM process enhanced the MRR by 35.85 %, reduced the SR and RLT by 42.66 % and 40.07 %, respectively. In another study, Chaudhari et al [ 37 ] attempted to WEDM the Ni-based superalloy (Waspaloy) by employing the MWCNT with different processing parameters. The multi-objective optimization suggested the improvement of 65.70 % in MRR, 50.68 % in SR, and 40.96 % in RLT when 1 g/L MWCNT were engaged in the dielectric fluid during the machining process.…”

Multi-Response Optimization and Influence of Expanded Graphite on Performance of WEDM Process of Ti6Al4V

“…However, the machined surface of PMWEDM obtained using the same machining parameters as the standard WEDM method exhibited a significant improvement in surface defects, i.e., a significant reduction in micro-pores, globules, and micro-cracks. The consistent sparking between the work material and the tool reduces microcracks significantly in the PMWEDM process [67,68]. The significant gap between the tool and the workpiece is the cause of the large reduction in deposition of debris (globules) and micro-pores in the NPMWEDM process [69].…”