A RSM based MOGOA for process optimization during WEDM of Inconel 625

Abstract

Inconel 625 is a super alloy that has excellent thermal and mechanical properties but is arduous to machine with conventional machining processes due to high melting point and hardness. Wire Electro Discharge machining, that have been an avowed unconventional process of machining that is mainly applied for the machining of tough and hard materials such as Inconel 625. An attempt, in this article, have been made to optimize the WEDM process parameters like servo voltage, peak current, spark-on time, spark-off time while machining on Inconel 625. The study considers Material Removal Rate (MRR) and Surface Roughness (SR) as performance parameters of machining process. L16 orthogonal array is implemented to design the experiments. Mathematical relations between the response parameters and the input variables are computed using of Response Surface Methodology (RSM). The mathematical models are then employed as a fitness function in the Multi-objective Grasshopper Optimization Algorithm (MOGOA) for determining optimal process parameters. Later, a comparison of MOGOA has been done with Non-dominated Sorting Genetic Algorithm II (NSGA- II) and Multiple Objective Particle Swarm Optimization (MOPSO) and found superior to both. Also, for responses like surface roughness (SR) and material removal rate (MRR), the predicted values in the present study are found to be in good proximity to the experimented values.

An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

“…Step 5: The optimal outcomes produced by the ANN-VCPSO are compared with optimum data generated by the RSM-desirability. [24][25][26] Step 6: Evaluation of the total honing cost at the optimal solution. The total honing cost (THC) comprises the labor (LAC), machine (MC), energy (EEC), honing tool (HTC), lubricant (LC), maintenance (MAC), cleaning (CLC), and disposal (CD) costs.…”

“…Step 5: The optimal outcomes produced by the ANN-VCPSO are compared with optimum data generated by the RSM-desirability. 24–26…”

A hybrid algorithm for adjusting the input parameters of the wirecut EDM machine in order to obtain maximum customer satisfaction

“…Here, the modeling method was based on Taguchi L 16 and RSM. The desired setting was obtained using a Multi-Objective Grasshopper optimization algorithm (MOGOA) [49]. In the same year, N. Kumar et al represented a review article about various parameters of WEDM using different optimization methods and concluded that MRR and SR are the most repeated output parameters [50].…”