Assessment of hybrid composite drilling and prediction of cutting parameters by ANFIS and deep neural network approach

Environmental awareness has driven a crucial search for biodegradable materials. Over the past few decades, the machining of these materials, particularly through drilling, has garnered significant research interest. This study presents an evaluation of the drilling performance of a hybrid composite (jute/glass), considering factors such as the concentration of chemical treatment with sodium bicarbonate, the type of glass fiber (random or woven), the degree of hybridization between the jute and glass fibers, and cutting parameters like rotation speed, feed rate, and drill diameter. Drilling operations were conducted to determine the maximum temperature in the cutting zone, the surface roughness of the drilled holes, and the maximum delamination factor. A Sobol sensitivity analysis was performed to identify the input parameters influencing these responses studied. Predictive modeling of the results based on the input factors was then conducted using the deep neural network (DNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches. The results show that the type of glass fiber significantly affects surface roughness, while rotation speed primarily influences cutting temperature. Similarly, the drill diameter directly impacts the maximum delamination factor. Finally, the predictive modeling demonstrates that the ANN-IGWO models provide the best predictions of drilling parameters compared to the ANN-GA and ANFIS models, with mean absolute errors ranging from 1.22 to 12%.