An Analysis on the Parametric Optimization of Electrochemical Honing Process

Abstract

Electrochemical honing (ECH) is a nontraditional machining process hybridizing the conjoint benefits of electrochemical machining (ECM) and mechanical honing actions. In this process, maximum amount of material is removed through anodic dissolution, followed by mechanical abrasion. In present day manufacturing industries, it has found wide ranging applications, mainly in finishing of varieties of gears, due to its various advantages, like increased material removal rate, long tool life, burr-free operation, achievement of higher surface finish and dimensional accuracy, generation of no residual stress, reduced noise, less material damage, etc. In order to achieve maximum machining capability from this process, it is always recommended to set its various input parameters at their optimal operating levels. In this paper, four powerful metaheuristic algorithms, i.e. firefly algorithm, differential evolution (DE) algorithm, cuckoo search (CS) algorithm and teaching–learning-based optimization (TLBO) algorithm are applied for single as well as multi-objective optimization of pulsed-ECH (PECH) and ECH processes. It is observed that TLBO algorithm supersedes other techniques in optimizing the two ECH processes with respect to the value of the derived optimal solution, consistency of the solutions and computational speed.