• Epari Dinesh
• PHANI KRISHNA KADIYALA
• Suresh Challa
• Raju Chitla
• Venkatasubbaiah K

The purpose of the present research work is to investigate the effect of process parameters on Material Removal Rate (MRR) and Surface Roughness (Ra) for Wire-EDM using Maraging steel of grade 250 as workpiece. A Central Composite Design (CCD) of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) has been adopted to estimate the effect of machining parameters on the responses. The machining parameters considered in the study are Pulse on-time (Ton), Pulse off-time (Toff), Servo Voltage (V), Wire Tension (Wt) and Wire Feed (Wf). Thirty two experimental runs are conducted and the influence of parameters on each response is analyzed. The Analysis of Variance (ANOVA) has been applied to identify the significance of the developed model. The response variables have been optimized using the multi-objective optimization through Genetic Algorithm (GA). Optimal machining parameters were obtained from the pareto optimal front. In summary, the proposed work enables the manufacturing engineers to select the optimum values depending on the production requirements and as a consequence, automation of the process could be done based on the optimum values.

Wire-EDM, Maraging steel, Material Removal Rate, Surface Roughness, Central Composite Design, Response Surface Methodology, ANN, GA

"MUTLI OBJECTIVE OPTIMIZATION OF WIRE-EDM PROCESS PARAMETERS FOR MARAGING STEEL USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.b93-b102, January-2022, Available :http://www.jetir.org/papers/JETIR2201114.pdf

"MUTLI OBJECTIVE OPTIMIZATION OF WIRE-EDM PROCESS PARAMETERS FOR MARAGING STEEL USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppb93-b102, January-2022, Available at : http://www.jetir.org/papers/JETIR2201114.pdf