A hybrid GA-ANFIS and F-Race tuned harmony search algorithm for Multi-Response optimization of Non-Traditional Machining process

• Published in: Expert systems with applications Vol. 199; p. 116965
• Main Authors: Devaraj, Rajamani, Mahalingam, Siva Kumar, Esakki, Balasubramanian, Astarita, Antonello, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.08.2022; Elsevier BV
• Subjects: Abrasive cutting; Abrasive machining; Adaptive algorithms; Adaptive systems; Algorithms; ANFIS; Artificial neural networks; Box-behnken design; Clay; F-race; Fiber composites; Fuzzy logic; Genetic algorithm; Genetic algorithms; Harmony search algorithm; Kerf; Laminates; Material removal rate (machining); Optimization; Parameters; Prediction models; Regression models; Search algorithms; Statistical analysis; Surface roughness; Taguchi; Genetic algorithm; Box-behnken design; Taguchi; F-race; ANFIS; Optimization; Harmony search algorithm
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.116965

•Integrated approaches for modelling and optimization of non-traditional machining process.•Adaptive neuro-fuzzy inference system is used for modelling the machining process.•F-race tuned harmony search algorithm is proposed to optimize the machining parameters.•The significance of proposed approaches is statistically validated. The present study focuses on development of prediction models with respect to various cut quality characteristics such as material removal rate, kerf taper and surface roughness for a well-known non-traditional machining process namely abrasive aqua jet cutting (AAJC) of natural fibre composite laminates through combined taguchi-genetic algorithm (TGA) and adaptive neuro fuzzy inference system (ANFIS). The AAJC experiments are conducted based on box-behnken design methodology by considering jet pressure, stand-off distance, traverse speed and wt% of nano clay inclusion in composites as input parameters. The ANFIS parameters are optimized using a hybrid taguchi-genetic training algorithm. The statistical results of hybrid TGA-ANFIS models shows that they are outperformed in prediction of AAJC parameters when compared with the results of multiple-linear regression models. Further, the optimization of AAJC parameters is carried out using a trained ANFIS network and the F-race tuned harmony search algorithm (HSA). The superlative responses such as MRR of 76.9 g/min, KT of 2.23° and Ra of 3.17 µm are forecasted at the optimum cutting conditions such as jet pressure of 303.08 MPa, stand-off distance of 2.16 mm, traverse speed of 375.64 mm/min, and nano clay wt% of 1.27, respectively. The experimental results show that the error between predicted and actual results are lower than 6%, indicating the feasibility of adopting the proposed F-race parametric tuned HSA in optimization of AAJC process.