Multi-response optimization of Mathematical, ANN, and ANFIS models using the Pareto search algorithm in combination with the I-optimal design of mixture experiments for WEDM of Inconel 718 superalloy

Abstract

Abstract Inconel 718 is a superalloy with a high nickel content that is widely used in applications requiring solid mechanical behavior and resistance to oxidation and corrosion at high temperatures. This alloy has numerous applications in manufacturing steam turbine and jet aircraft interiors, aviation sector manifolds, and rotary spindles. It can be classified as a difficult-to-cut material unsuitable for traditional machining. The purpose of this paper is to develop prediction models for a wire electrical discharge machining (WEDM) process using response surface methodology (RSM), artificial neural networks (ANN), and adaptive neuro-fuzzy inference systems (ANFIS) and to determine which model is better at making accurate predictions. Pulse_ontime, pulse_offtime, servo voltage, flushing pressure, and wire feed were considered the main factors affecting volumetric material removal rate (VMRR) and arithmetic surface roughness (Ra), which were evaluated as WEDM performance characteristics. I-optimal design made with a computer algorithm was employed to develop experimental models. The results reveal that the wire feed and pulse_ontime were the most vital factors influencing VMRR, respectively, and the most significant factor influencing Ra is the pulse_ontime. The total percentage error of the three models demonstrated that the ANN and ANFIS models are more reliable and accurate than the RSM mathematical model. Finally, multi-objective optimization using the Pareto search algorithm was used to optimize mathematical, ANN, and ANFIS models to determine the optimum WEDM process parameters for machining Inconel 718 superalloy.