Hybrid carbon and glass fiber-reinforced composites have attracted significant research interest for primary load-bearing structural components in the field of aviation manufacturing owing to their low weight and high strength to weight ratio. However, the anisotropic and heterogenic nature of carbon and/or glass fiber-reinforced composite prevents high machining quality due to the directionality effect of fibers in the polymer matrix. As such, this study investigates the effect of drilling process for hybrid fiber-reinforced composite and reports optimal drilling parameters to improve the drill quality. Experimental studies indicate that an increased point angle (i.e., from 80° to 120°) resulted in low delamination upon entry due to reduced thrust force, which in turn produces better surface finish with minimal tool wear. The optimal feed rate (0.2 mm/min) ensures lower delamination at entry, since higher feed rates can increase the thrust force due to elevation in the shear area or raise the self-generated feed angle, which in turn reduces the effective clearance angle. To this end, drilling parameters were optimized using Dandelion optimizer (DO)—a cutting-edge metaheuristic search algorithm (MSA). We report the excellent consistency of DO to solve the proposed drilling optimization problem while achieving promising results as ascertained by the small standard deviation values.

Drilling is a quite common operation being performed in the manufacturing of components. Instrumental response in drilling is geometrical accuracy and surface integrity of the drilled parts. For the application where geometrical tolerance is very small, an operation is to be carried out very carefully. If not, rejection of drilled samples will be higher and consequently production loss will be higher. The use of prediction model in this scenario is much more appropriate and cost-effective. This research aimed to apply extreme gradient boosting (XGBoost) regressor to develop a drilling prediction model. Drilling experiments were conducted after developing design of experiments with twenty-seven unique sets. Experimental data analysis was then carried out on experimental data sets that have features such as speed, feed, angle, hole length, and surface roughness. After correlation analysis, the k-fold cross validation method was applied for parameterisation. Hyperparameters estimated from the k-fold cross validation were then applied to train and test the XGBoost regressor-based machine learning (ML) model. It is concluded from the model evaluation metric (R²) that the XGBoost regressor model has resulted 0.89 before tuning and 0.94 after tuning of the model, which is higher than the polynomial regressor and support vector regressor.

Wire cut EDM is a quite regularly used machining process in mechanical and electronic industries. This research has attempted to machine aluminum alloy for which experimental design was prepared using Box-Behnken design. Different combinational options of pulse-on time (P₁), pulse-off time (P₂), servo wire feed (WF), and current (I) were investigated and surface roughness after machining was observed. Collected 27 datasets were further used in Adaptive Neuro Fuzzy Inference System (ANFIS) to produce about 500 datasets. These 500 datasets are approximated data derived from experimental datasets, known as synthetic data. Data model was further developed and used in Gorilla Troops Optimizer (GTO) to locate the optimum machining parameters. With the excellent three search operators: move towards other gorillas, migrate towards unknown places, and migrate towards known places, GTO has produced the lowest surface roughness value of 0.500953μm when the machining parameters of pulse-on time, pulse-off time, wire feed, and current values were set as 121μs, 52μs, 3m/min, and 166A, respectively. To ensure the accuracy of the synthetic data-based model and optimality, verification and validation were conducted. Wilcoxon signed rank test was conducted for the pairwise comparison of GTO with each of its competing algorithms at the significance level of σ=0.05. Friedman test was conducted to calculate the average ranking of each algorithm and to detect the global differences between all compared algorithms. Outperforming performance by GTO algorithm in machining of the selected material is found.

The aim of the present study is to investigate the influence of Nickel–Hexagonal Boron Nitride (Ni-hBN) nanocomposite coatings, deposited using the pulse reverse current electrodeposition technique. This experimental study focuses on assessing the tribological and corrosion properties of the produced coatings on the SS304 substrate. The microhardness of the as-deposited (AD) sample and heat-treated (HT) sample were 49% and 83.8% higher compared to the control sample. The HT sample exhibited a grain size which was approximately 9.7% larger than the AD sample owing to the expansion–contraction mechanism of grains during heat treatment and sudden quenching. Surface roughness reduced after coating, where the Ni-hBN-coated sample measured a roughness of 0.43 µm compared to 0.48 µm for the bare surface. The average coefficient of friction for the AD sample was 42.4% lower than the bare surface owing to the self-lubricating properties of nano hBN. In particular, the corrosion rate of the AD sample was found to be 0.062 mm/year, which was lower than values reported in other studies. As such, findings from the present study can be particularly beneficial for applications in the automotive and aerospace industries, where enhanced wear resistance, reduced friction, and superior corrosion protection are critical for components such as engine parts, gears, bearings and shafts.

This research is done to determine the optimum parameters to drill polytetrafluoroethylene (PTFE) and to investigate the effect of two-tier modeling for enhanced response in optimization. RSM model was done with L₂₇ experimental design, considering speed (N), feed (f), and tool point angle (Ɵ). RSM data were further trained and tested using the Adaptive Neuro-Fuzzy Inference System (ANFIS), and β coefficient values were restructured to form revised RSM model. Both nonrevised RSM model and revised RSM model were used in Genetic Algorithm to locate the minimum surface roughness. ANFIS revised RSM model deviates from the experimental results by 2.6 and 2.86 for dry and wet condition; meanwhile, nonrevised RSM model deviates by 4.76 and 4.94, respectively. The research concludes that two-tier modeling using RSM and ANFIS is better. Spindle speed of 1656rpm, feed rate of 0.05mm/min, and point angle of 100° are the optimum conditions to drill PTFE material where the best surface quality of 0.68μm at wet drilling can be achieved.