Electro Discharge Machining Characteristics of Ti-6Al-4V Alloy: A Grey Relational Optimization

AbstractThe paper presents the grey relational theory based parameter optimisation in electro discharge machining (EDM) of Ti-6Al-4V alloy. The multiple responses optimized are material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR). The process parameters include duty cycle, pulse current, pulse on time, electrode type and gap voltage. Experiments were conducted using Taguchi L18 orthogonal array. From grey relational analysis, the optimum values found are duty cycle (8%), pulse current (18 Amp), pulse on time (200μsec) and voltage (40V). Experimental results revealed that the copper electrode gives the optimum performance in terms of higher MRR and lower EWR and SR. The optimized process parameters that simultaneously leading to a lower electrode wear ratio and higher material removal rate are then verified through a confirmation experiment

Comparative Analysis of Metaheuristic Approaches for Makespan Minimization for No Wait Flow Shop Scheduling Problem

This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance