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Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method.

For machining a component, it is important to understand the characteristics of work material in order to choose the appropriate cutting tool and to fix a set of machining parameters to achieve optimum output. Analytical models of machining processes require complete understanding of process mechanism and hence are difficult to be developed. Once developed, these models are useful in parametric optimization, process simulation, operation and process planning, process parameter selection, parametric analysis, process performance prediction, verification of the experimental results, and improving the process performance by implementing/incorporating the theoretical findings.

Neural network models associated with artificial intelligence are known as artificial neural networks (ANNs) which are simple mathematical models in the form of defining a function. This work presents the details of the experiments carried out for data acquisition, method of building the ANN models and their validation. These models can be used for predicting the output for a chosen set of input variables or for a specific desired output, finding the set of input variables to be chosen.

This work resulted in developing models for the turning process for Inconel 718 alloy in a scientific manner. It also enables further scope of identifying the optimized set of turning parameters for Inconel 718 material using the newly developed coated carbide tools, achieving quality surface and productivity.

Electrical Discharge Machining (EDM) is a thermal energy based non-traditional shaping process for shaping of hard and brittle electrically conductive materials, but it suffers with low machinability and recast layer formations. The combination of grinding with EDM means enhancement in machining capability, but the process becomes highly complex. Therefore, the assortment of control factors for optimum results is greatly challenging for the industries. The objective of present study is to optimize the control factors such as current, pulse on-time, pulse off-time, wheel RPM and abrasive grit number (GN) to optimize the material removal rate (MRR) and average surface roughness (Ra) for Grinding Aided-EDM process. For this purpose, the simultaneous application of soft computing methods such as Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been employed. The results demonstrate that combination of ANN with GA effectively predicts the data and provides optimal results with adequate percentage errors in MRR and Ra positively.

High Carbon High Chromium (or AISI D2) Steels, owing to the fine surface finish they produce upon grinding, find lot of applications in die casting. Machining parameters affect the surface finish significantly during the grinding operation. In this context, this work puts an effort to arrive at the optimum machining parameters relating to fine surface finish with minimum cutting force. The material removal caused by the abrasive grinding wheel makes the process a very complex and nonlinear machining operation. In many situations, traditional optimization techniques fail to provide realistic optimum conditions because of the associated complexity. In order to overcome this issue, particle swarm optimization (PSO) coupled with artificial neural network (ANN) is applied in this research work for parameter optimization with the objective of achieving minimum surface roughness and cutting force. The machining parameters selected for the investigation were table speed, cross feed and depth of cut and the responses were surface roughness and cutting force. ANNs, inspired from biological neural networks, are well capable of providing patterns, which are too complex in behavior. The ANN model developed was used as the fitness function for PSO to complete the optimization. Optimization was also carried out using conventional response surface methodology-genetic algorithm (RSM-GA) approach in which regression equation developed with RSM was considered as the fitness function for GA. Confirmatory experiments were conducted and the comparison showed that PSO coupled with ANN is a reliable tool for complex optimization problems.

The machining of metal matrix composites (MMCs) creates an extra challenge as compared to that of metals and alloys due to their hardness owing to the abrasive reinforcement particles. This paper presents the study on end milling of Al-4032/3% SiC composite considering the cutting speed (CS), feed rate (FR) and depth of cut (DOC) as the process parameters. Surface finish and material removal rate (MRR) have been taken as the response parameters. The Al-4032-based AMC has been prepared using stir casting process. Taguchi’s $L_{27}$ orthogonal array (OA) has been used for experimental trials. The optimum setting of the parameters has been obtained using TGRA. The resulting surface roughness (SR; $R_a$) occurs in the range of 1.18–3.97$\mu$m, with the minimum value corresponding to the CS of 110m/min, FR of 0.05mm/tooth and DOC of 1.2mm. Bayesian regularization (BR), scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms have been employed for training, validating and testing the 3–$n$–1 and 3–$n$–2 ANN architectures ($1\le n\le 20$). Minimum root-mean-square error (RMSE) has been taken as the standard for evaluating the model. Neural network toolbox of MATLAB “R2019A” has been used for prediction of the response.

Duplex turning (DT) is a novel concept of metal cutting where two tools are employed to cut the objects in lieu of single tool. It shows many benefits over conventional turning in terms of superior dynamic balancing, lower cutting forces and tool wears, better surface finish, reduction in vibration with additional support for workpiece. It is a complex method and the resulting experimental analysis becomes difficult and expensive. In such conditions, modeling techniques show their potential for parametric study, prediction of data for optimization and selection of optimal condition. Generally, soft computing-based Artificial Neural Network (ANN) is applied for modeling and prediction for complicated processes while Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows their potential for optimization of complex problems over Genetic Algorithm. Therefore, ANN and NSGA-II techniques are employed for modeling and optimization of DT process to minimize the surface roughness and cutting forces (primary and secondary). Finally, results reflect that ANN efficiently predicts the responses at different input combinations within training data set with absolute percentage errors as 2.55% for roughness, while 3.05% and 3.14% for cutting forces (primary and secondary), respectively. In the same way, optimized results also found within the range of acceptability with percentage errors as 2.57% for roughness, while 3.25% and 3.15% for primary and secondary forces, respectively.

In this paper, a strategy has been set for minimizing the corner error in the modification of pulse and non-pulse parameters. Taguchi’s philosophy has been used to design the experiment by varying process parameters (i.e. Spark-on Time (STon), Wire Tension (WT), Servo-Voltage (Sv), Discharge Current (DC) and Wire-speed (Sw)), to explore the machining outcomes. The response characteristics have been measured in terms of cutting speed (Cs), Corner Error (CD) and surface Roughness (RA) using Topas plus X wire of $\varphi$ 0.25mm diameter. The machining performance characteristics were analyzed using main effect plots and analysis of variance (ANOVA). Furthermore, a soft computing-based hybrid optimization technique (Artifical Neural Network (ANN)-based Multi-Objective Grey Wolf Optimizer (MOGWO)) has been utilized to search the multi-optimum parameter setting for superior machining results. The most significant parameter observed is DC for Cs, which is determined to be 34.81%. Moreover, WT found 26.29% and 34.10% for CD and RA, respectively. The confirmation test shows that the maximum absolute percentage errors are observed as 3.89%, 6.3% and 9.7% for Cs, CD and RA, respectively. The proposed hybrid technique can generate superior solutions compared to the existing algorithms. Notably, the outcomes obtained on new instances exhibit potential, purposeful, and efficacy.

Higher accuracy and meticulousness are highly demandable in modern industrial field during micro-machining performances by electrochemical discharge machining (ECDM) process. The paper deals with the experimental fuzzy logic control (FLC) analysis as well as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) analysis during micro-channel fabrication on silica glass. A comparative analysis of FLC, ANN as well as ANFIS has been performed and experimental error prediction has been propounded to estimate minimum error possibilities such as mean absolute error (MAE), root mean square error (RMSE) and regression value (R). Computational time training dataset, minimum sample size and prediction time are also illustrated in this paper. In this paper, ANN, FLC and ANFIS models are analyzed for tool wear rate (TWR) and heat-affected zone (HAZ). 3D Rule Viewer and regression analysis as well as validation of test results and characteristics graph between performances with number of epochs of ANN model also are included in this paper for TWR and HAZ. Influence of process parameters like voltage, duty ratio, pulse frequency and electrolyte concentration on Surface Viewer of TWR and HAZ is also illustrated. It is found that ANFIS has great effectiveness of prediction of error during micro-ECDM process.

An experimental investigation was conducted to evaluate the machinability of a titanium alloy (Ti6Al4V) using copper (Cu), tungsten carbide (WC), and graphite (C) tools. Voltage (V), capacitance (pF), pulse-on time (T_on), and pulse-off time (T_off) were considered as the input machining parameters, whereas the material removal rate (MRR) and tool wear rate (TWR) were considered as the output machining parameters. A Taguchi L${}_{16}$ orthogonal array and gray relational analysis (GRA) were utilized to design and optimize the machining parameters for both responses. Artificial neural network (ANN) analysis was performed to predict the experimental outcomes. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to assess the surface morphology and determine the elemental composition of the machined surface. The results indicated that the optimum machining conditions for the copper tool were 150 V, 1000 pF, 15$\mu$s (T_on), and 15$\mu$s (T_off). However, the optimal machining conditions for the WC were 200 V, 100 pF, 25 $\mu$s (T_on), and 10$\mu$s (T_off), and the optimal conditions for the C were 200 V, 1000 pF, 20$\mu$s (T_on), and 25$\mu$s (T_off), respectively. The highest MRR achieved using the WC tool was 9.4510 mg/s, whereas the TWR of the Cu, WC, and C tools were 1.1039 mg/s, 1.0307 mg/s, and 1.2796 mg/s, respectively. The results showed that machining with the graphite tool had a higher TWR than machining with the Cu and WC tools.

Surface roughness prediction based solely on cutting parameters provides a quantified value regardless of tool condition, making it suitable for initial parameter selection. However, to achieve accurate surface roughness prediction, the current tool condition must be incorporated into the model. This can be accomplished by utilizing vibration or cutting force signals. In this study, we develop a hybrid response surface methodology-artificial neural network (RSM-ANN) model for predicting surface roughness by combining cutting parameters and vibration data. Four different RSM models were developed, and the best-performing model was selected as input for the hybrid RSM-ANN model. A comparison was made between the hybrid model, a basic ANN model with four inputs (three cutting parameters and one mean vibration in the $Z$-direction), and other ANN models with all possible combinations of these four variables. The hybrid model demonstrated the highest accuracy with the mean square error of 0.00332 with the highest coefficient of regression value of 0.99802 when compared to the other models.

Production engineering focuses on designing, optimizing, and managing manufacturing processes to produce goods efficiently. Turning is a machining process where a cutting tool removes material from a rotating workpiece to create cylindrical shapes. Key parameters include cutting speed, feed rate, and depth of cut. Surface roughness is a key challenge in turning, impacting product quality. Achieving the desired finish is crucial for tight tolerance and performance. Engineers use optimization techniques to minimize roughness. Advancements in tool materials and technology help address roughness challenges for improved efficiency in turning. Predictive models for surface roughness are vital for optimizing machining processes, ensuring quality, and enhancing performance. They guide decision-making, improve efficiency, and drive innovation in manufacturing. In this paper, 25 machine learning models have been used and optimized to accurately predict the surface roughness of the turning process of five previous studies on Titanium alloy. Resulting in the best performance with the lowest MSE was for the artificial neural network with 3 hidden layers: the first has 5 neurons, the second has 10 neurons and the last one has 5 neurons. The MSEs are 0.053072, 0.555763, 0.059667, 0.051867, and 0.554829 for the five studies, respectively.

Nowadays, hybridization of different algorithms for the optimization of non-conventional machining processes tries to accomplish better results. The paper consists of experimental evolutionary-particle Swarm Optimization (PSO), Quantum-PSO and Gaussian Quantum Particle Swarm Optimization (G-QPSO)-based ANN modeling and comparative investigation on performances such as material removal rate (MRR), machining depth (MD), roughness of surface and overcut (OC) for machining of silica by ECDM process using mixed electrolyte. The paper also shows the co-efficient of NN models for different machining criteria and G-QPSO and also the comparative study of MD, roughness (SR), overcut (OC) as well as MRR using different algorithms and convergence test for fitness of experimental results also propounded to achieve cross-validation of models and multi-response optimal results for micro-machining of Silica by ECDM using PSO, QPSO and GQPSO. It is found that Gaussian Quantum Particle Swarm Optimization (G-QPSO)-ANN is more efficient for ECDM and achieves optimal results at 55-volt, pulse on time 52.3 s, inter-electrode gap (IEG) 30 mm, duty ratio 0.475 and electrolytic concentration 30 (wt.%).

BP neural network can achieve arbitrary nonlinear mapping of the input to the output, so it is extensively adopted in intelligent control, image recognition, hydrological predicting and water-resource quantity evaluation, etc., has stronger features of mapping, classification, functional fitting. This paper chooses the water quality of Lanzhou section of Yellow river as example by use of BP model to forecast the water quality. It is well verified that BP network model can reach the purposes of early warning and predicting.