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 (R_a) 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 R_a 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 R_a 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.

The kerf taper angle is one of the most significant parameters for AWJ cutting. Choosing the best cutting parameters is critical to reducing the kerf taper. Therefore, the aim of this work is to demonstrate on to implement RSM in conjunction with the Rat swarm optimizer algorithm to optimize the kerf taper angle of a Titanium alloy. Thirty-two number of experiments were conducted according to CCD considering five process parameters namely jet traverse rate, water jet pressure, abrasive flow rate, stand-off distance and diameter of focusing nozzle to achieve kerf taper angle. A second-order full quadratic model is used for mathematical modeling. The result demonstrates that the model is sufficiently adequate and acceptable. A recent Rat Swarm Optimization (RSO) algorithm is considered for finding out the global optimal solution. It is observed that after optimizing the process that the jet traverse rate of 1.952 mm/sec, water jet pressure of 250.548 MPa, abrasive flow rate of 0.554 kg/min, stand-off distance of 3.75 mm and diameter of focusing nozzle of 2.0 mm gives minimized with 0.754 degree of kerf taper angle. A series of verification tests are carried out to ensure that the optimization technique is accurate in determining the best levels of process.

In this paper, genetic and simulated annealing algorithm approaches are proposed for the selection of the optimal values in efficient Nd-YAG laser cutting of thin Ti-6Al-4V super alloy sheet. The pulse width, pulse energy, cutting speed, and gas pressure are considered as process parameters. Response surface methodology based Box-Behnken design is adopted to conduct the experiments for measuring the proposed performance characteristics such as kerf deviation (KD) and metal removal rate (MRR). Quadratic regression models are developed to predict the responses using response surface methodology. Analysis of variance tests have been carried out to check the adequacy of the developed regression models. Based on the developed mathematical models, the interaction effects of the process parameters on KD and MRR are investigated. Minimising KD and maximising the MRR are considered as objectives functions. The optimal laser cutting conditions are obtained to minimise the KD and maximise the MRR in considering single and multi objective optimisation methods. Validation tests with optimal levels of process parameters were performed to illustrate the effectiveness of GA and SA algorithms. It is believed that the used algorithms provide a robust way of looking the optimum process parameters for a selected laser cutting system.