This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm’s hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R2 = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R2 = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

The correlation between the mechanical properties of Fillers/Epoxy composites and their tribological behavior was investigated. Tensile, hardness, wear, and friction tests were conducted for Neat Epoxy (NE), Graphite/Epoxy composites (GE), and Data Palm Fiber/Epoxy with or without Graphite composites (GFE and FE). The correlation was made between the tensile strength, the modulus of elasticity, elongation at the break, and the hardness, as an individual or a combined factor, with the specific wear rate (SWR) and coefficient of friction (COF) of composites. In general, graphite as an additive to polymeric composite has had an eclectic effect on mechanical properties, whereas it has led to a positive effect on tribological properties, whilst date palm fibers (DPFs), as reinforcement for polymeric composite, promoted a mechanical performance with a slight improvement to the tribological performance. Statistically, this study reveals that there is no strong confirmation of any marked correlation between the mechanical and the specific wear rate of filler/Epoxy composites. There is, however, a remarkable correlation between the mechanical properties and the friction coefficient of filler/Epoxy composites.

Recently, recycled thermoplastic polymers become an alternative resource for manufacturing industrial products. However, they have low mechanical properties compared to the thermosets. In this paper, an attempt has been made to enhance the mechanical properties of recycled high density polyethylene (HDPE) with chopped strand mat (CSM) glass fibres as a synthetic reinforcement and with short oil palm fibres as a biodegradable (natural) reinforcement. The effects of volume fraction of both synthetic and natural fibres on tensile, compression, hardness, and flexural properties of the HDPE were investigated. The failure mechanism of the composite was studied with the aid of optical microscopy. Tensile properties of the HDPE composites are greatly affected by the weight fraction of both the synthetic and the natural fibres. The higher strength of the composites was exhibited when at higher weight fraction of both natural and syntactic fibres which was about 50 MPa. Date palm fibre showed good interfacial adhesion to the HDPE despite the untreated condition used. On the other hand, treatment of the fibres is recommended for higher tensile performance of the composites.

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Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R2 = 0.95 that closely matches the actual surface roughness experimental values.

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.