Nd: YAG laser cutting of Hastelloy C276: ANFIS modeling and optimization through WOA

Abstract

ABSTRACT A hybrid approach through combining genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) for modeling the correlation of laser beam cutting (LBC) parameters and enhancing the quality performance characteristics of machined Hastelloy C276 is emphasized. The LBC experiments are performed by considering gas pressure (GP), cutting speed (CS), pulse energy (PE) and stand-off distance (SOD) as input parameters. The output responses are material removal rate (MRR), kerf taper (KT) and surface roughness (Ra ) for the present investigation. The optimal ANFIS training variables are obtained through GA. The training, testing errors, and statistical validation parameter results exposed that the ANFIS learned by GA is outperformed in forecasting LBC responses. In addition, to obtain the optimal combinations of LBC parameters, the multi-response optimization based on maximizing MRR and minimizing KT and Ra was performed using a trained ANFIS network coupled with a whale optimization algorithm (WOA). The responses such as MRR of 236.98 mg/min, KT of 1.135° and Ra of 1.109 µm are forecasted for the optimum cutting conditions: GP of 3 bar, CS of 319.8 mm/min, PE of 5.93 J and SOD of 2.97 mm, respectively. Furthermore, the WOA predicted results are validated by conducting confirmatory experiments.