An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

Abstract

This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi's L 9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.

Optimization on Black Box Function Optimization Problem

“…It is easy to conclude that the function global maximum is 1 and its corresponding coordinate is (0, 0) from the above function expression in (6) and its function graphic. Function extremum and its corresponding coordinate are easy to be concluded in a known instead of an unknown function expression case.…”

“…Here, 8000 inputs and outputs are produced according to (6) and 7000 pieces of data are selected randomly to train BP neural network; the other 1000 pieces of data are used to test the fitting accuracy of BP neural network. Then, comparison of forecasting outputs and expected outputs is demonstrated in Figure 7.…”

“…For BBFOP optimization method, population-based optimization algorithms enjoy high popularity and have extensive applications in recent years in engineering optimization problems, including genetic algorithms (GAs) [1,2], ant colony optimization (ACO) [3,4], particle swarm optimization (PSO) [5,6], artificial bee colony (ABC) [7,8], bacterial foraging optimization (BFO) [9,10], big bang-big crunch algorithm [11,12], and teaching-learning-based optimization (TLBO) [13][14][15] and immune optimization algorithm (IA) [16][17][18][19]. These population-based optimization algorithms have one or more populations consisting of a certain number of individuals, which presents a solution of the problem to be solved, respectively.…”

Performance of Zinc Coated Electrode Wire and Parametric Optimization During Profile Cutting of Die-Steel

“…In this work, AISI D3 high carbon, high chromium steel termed as tool grade was selected as workpiece. D3 steels were characterized with extreme hardness through oil hardening and due to the presence of carbides of chromium in their microstructure [13]. The elements present in AISI D3 steel is presented in Table I.…”

Machining of Stainless Steels and Alloys Using Non-Traditional Machining Processes

“…AISI D3 die-steel possess high resistance to wear and exhibits resistance to heavy pressure which extensively used for applications like blanking, stamping and cold forming dies and punches for long runs and lamination dies [39]. Muthukumar et al [39] conducted experiments to study the influences of input factors such as T ON , T OFF , gap voltage, wire feed on MRR, R a and kerf width in WEDM of AISI D3 die-steel. They found improved quality responses through systematic analysis of WEDMed die steels.…”