A Comparative Modeling and Multi- Objective Optimization in Wire EDM Process on H21 Tool Steel Using Intelligent Hybrid Approach

Abstract

H21 steel is one of the hot work tool steel, which exhibits superior red hardness, high mechanical strength and difficult-to-machine. Wire electrical discharge machining (WEDM) always demands high-speed and high-precision machining to fulfill productivity and accuracy of machining hard materials. Cutting speed determines the productivity of machining and the width of kerf determines the tolerance of finished product. Two methodologies viz. response surface method (RSM) and artificial neural network (ANN) are compared for their modeling, sensitivity analysis and optimization abilities. The predictability of ANN model is better than RSM which indicating the advantage of ANN in mapping the nonlinear behavior of the system. Finally, the ANN fitness function is integrated with particle swarm optimization (PSO) algorithm to optimize the process parameters of the WEDM process.

“Research Work for Optimization and Effect of WEDM Cutting Process Parameters on Performance Measures- a Literature Review”

“…response surface method (RSM) and artificial neural network (ANN) were compared regarding their modeling, sensitivity analysis and optimization abilities which concluded that the predictability of ANN model is better than RSM which emphasize the advantage of ANN in mapping the nonlinear behavior of the system. However, the ANN fitness function is integrated with particle swarm optimization (PSO) algorithm to optimize the output process parameters of the WEDM process [7]. B.…”