ECDM process accuracy mainly depends on the circuit configuration of the power system with correlating other machining variables. Semi-conducting job specimen like Silicon-wafer has effective utility in micro-fluidic labs. Here, mainly circuit input variables like duty factor (0.40-0.50), pulse-on-time (20-30 mu s), voltage (40-55 V), pulse frequency (200-800 Hz) and pulse off time (35-45 mu s) effectiveness at fixed concentration 25 wt.% of (NaOH + KOH), inter-electrode gap (IEG) 40 mm and stand-off distance (SOD) 0.5 mm on machining rate as material removal rate (MRR) & (gm/h) & width of cut (WOC) (mu m) of silicon-wafer has been illustrated using WC (200 mu m) tool. The paper also analyzed the fitness of models with ANOVA for those machining outputs and optimized multi-performances for maximization of MRR and minimization of WOC. The paper also shows the convergence test and analysis of root mean square error (RMSE) and computing time for choosing the best evolutionary algorithms using response surface methodology (RSM), particle swarm optimization (PSO) and Quantum-PSO. Thermal effects and channel irregularities if any have been advocated by SEM analysis of Silicon-wafer. Maximized MR with Minimized WOC is achieved at 45 V, 25.65 mu s T-on and T-off 35 mu s, with frequency 800 Hz, 40 mm IEG and 0.50 duty factor and 25 wt.% of NaOH & KOH and best cognitive models of Quantum particle swarm optimization with neural network (QPSO-NN) is advocated and validated for this experimentation as RMSE for MRR and WOC of satisfied value 1.484% and 1.511%.

Electrochemical discharge machining (ECDM) performance enhancement is challenging when applied in the industrial field. The book chapter shows the secondorder correlating mathematical models with pulse on-time, electrolyte concentration, duty ratio, inter-electrode gap, and applied voltage for diametric over cut (DOC) and tool wear rate (TWR) when a mixture of potassium hydroxide (KOH) and sodium hydroxide (NaOH) is used as an electrolyte during blind hole generation on glass. ANOVA is used to analyze the fitness of models, as well as the effects of and process parameters. Desirability function analysis is performed in this chapter for the minimization of DOC and TWR for blind hole formation on glass and contour diagrams. Multi-objective optimization also represents the better results of micro-hole fabrication by the ECDM process. It is found that 50V/30mm IEG/20wt%/45 µs/.50 duty ratio/50Hz pulse frequency provides a minimum TWR of tungsten carbide tool and DOC of micro-hole.

Hybridizing non-traditional machining (NTM) processes, such as micro-ECDM, is a great challenge in measurement and microfabrication. To find out the solution to this problem, in the present research, the overall model is demonstrated in three steps, during the first stage, ANN is used to construct the linear model of width of cut (WOC), metal removal rate (MRR), and surface roughness (SR) from experimental data consisting of process parameters, that is, voltage (V) pulse frequency (PF), electrolyte concentration (EC), and duty ratio (DR). In the second phase, to get the best-fitted model, we applied both Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a hybrid of these two algorithms for cross-validation and validation on the train and test datasets. Based upon root mean square error, accuracy, and computational time, the proposed algorithm is more efficient than PSO and GA. Furthermore, in the final phase, the ANN model was optimized using hybrid GAPSO, which helped to determine the optimal process parameters responsible for maximum MRR, minimum WOC, and SR formation. The result shows that maximum MRR, minimum WOC, and SR were formed for the optimized value of voltage 45 volts, electrolytic concentration 30 wt%, DR 0.45, and PF 75 Hz. Moreover, hybrid GAPSO-ANN shows better convergence, accuracy, and computational time (seconds) for micro-machining characteristics analysis.

Micro-ECDM process tries to take entry in industrial field to cut various non-conducting materials like silica. In this article the parametric influences and comparative studies of various machining performances such as MRR, OC and HAZ has been addressed using electrolyte concentrations (wt.

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.

The present research work is aimed toward optimize the micro-ECDM (Electro Chemical Discharge Machining) parameters like applied voltage, electrolyte concentration, duty ratio and pulse frequency on the optimum micro-channeling performances on borosilicate glass. Micro-ECDM tests were performed on borosilicate glass in well-established ECDM set-up under different applied voltage, electrolyte concentrations, duty ratios and pulse frequencies. Several micro-ECDM performances on glass like material removal rate (MRR), surface roughness (SR) and width of cut (WOC) were investigated for every matching test condition. The signal noise (S/N) ratio and best fitted ANOVA analysis as-well-as predicted results, mean and standard deviation has been propounded during micro-channel machining process. Heat affected zone, radial overcut and morphology of debris were investigated under scanning electron microscope. The micro-ECDM parameters have been optimized by applying Taguchi Technique on the experimental results and observations. The optimum micro-channeling performances during micro-ECDM process on glass are observed at applied voltage of 50 V, 25 wt% electrolyte concentration, 0.5 duty ratio and 50 Hz pulse frequency.

MIG welding is a modern part of advanced welding process. In this article, the mathematical modeling by ANN and parametric optimization of MIG welding joining process of cast iron has been propounded using soft computing hybrid-GAPSO. The article also shows the best model using NN basis of fittest with experimental result, computational time, convergence speed and finally RMSE obtained from cross-validation. In this research, the optimal value of the input variables obtained by hybrid-HGAPSO so that output variables hardness (HA) will be maximum & surface roughness (SR) and width of breadth thickness (WOBT) will be minimum, has been illustrated. In this experimental analysis minimized RMSE of 1.15%, 1.433%, and 1.6678% for HA, WOBT and SR are accomplished respectively by HGAPSO-ANN.

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.%).

CNC milling is advanced machine tool which is most demandable due to its accuracy and precision level. In this experimental research Helix angle (HA), Axial depth of cut (AD), Radial depth of cut (RD), and cutting speed (CS) are taken as input parameter and Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR) are considered as a response variables. Overall research performed into parts, in first part ANN model used to represent the nonlinear relationship between each of the response output variables with input variables. In last phase five multi-objective Swarm Intelligence (SI) optimization techniques: Cuckoo search Optimization (CSO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC), Bat Algorithm (BA) & Simulating Annealing (SA) has been examined in order to have a better understanding of how they solve many objective challenges in order to get the best CNC milling results. A confirmatory test conducts for the validation of the best technique.

It is a challenging task to generate micro-channel on brittle materials with higher machining depth by ECDM process. The paper consists model presentation of mechanical system into nonlinear ANN where input parameters are voltage, electrolyte concentration, duty ratio & pulse frequency and mathematical models for Tool wear rate (TWR) and heat affected zone (HAZ) are established during machining performances by micro-electro chemical discharge process. Influence of process parameters like voltage, duty ratio, pulse-frequency and electrolyte concentration on surface viewer of TWR and HAZ also illustrated during fabrication of micro-fluidic channel on silica glass. In this paper best fitted model during the cross validation has been identified by accuracy, Root Mean Square Error (RMSE), computation time & minimum cost value. The paper also includes finding out optimal parametric combinations during micro channel fabrication on silica glass where both Tool wear rate & Heat affected Zone are minimized using best different Evolutionary Algorithms like Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is found that the best fitted model is PSO-ANN and multi-response optimization parametric combination is 45 V, 20 wt% electrolyte, 60 % duty ratio and 600 Hz pulse frequency.

In this work, machining of microchannel in silica glass was successfully carried out using electro chemical discharge machining (ECDM) process. The experiments were planned according to L 27 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration, pulse frequency and pulse-on-time (T ON ) as control factors. The material removal rate (MRR), overcut (OC) and tool wear rate (TWR) were considered as response characteristics. In this study the effects of control parameters on MRR, OC and TWR have been investigated. The increase in applied voltage, electrolyte concentration and pulse on time lead to the improvement in the output characteristics which is attributed to formation of heavily crowded hydrogen bubbles and further coalescence of hydrogen bubbles promotes the occurrence of sparks which resulted in higher values of MRR, OC and TWR. The multi-objective optimization of ECDM was carried out through grey relational analysis (GRA) method. Optimal combination of process parameters achieved from GRA was 45 V applied voltage, 25 wt.% electrolyte concentration, 1.5 mm SOD, 400 Hz pulse frequency and 45 μs T ON . ANOVA for GRG study revealed that the applied voltage (70.33%) was most significant factor affecting output responses followed by electrolyte concentration (11.69%), pulse frequency (4.98%) and SOD (4.13%). Furthermore, the regression equations were formulated for the optimum combination to predict the collaboration and higher-order effects of the control parameters. In addition, confirmation test was conducted for the optimal setting of process parameters and the comparison of experimental results exhibited a good agreement with predicted values. The microstructural observation of machined surface for the optimum combination was carried out.

The production of miniature parts by the electrochemical discharge micromachining process ([Formula: see text]-ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage ([Formula: see text]), pulse on time ([Formula: see text]s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO[Formula: see text]). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with [Formula: see text], [Formula: see text], and [Formula: see text] axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process.

Metal Inert Gas (MIG) welding is an advanced type of welding process where a fusion of gas welding and arc welding is used with shielding component (CO 2 ) at welding zone and brass coated stainless-steel wire is used as electrode and the feed for joining material (304 stainless steel) is automated which can mostly utilized in mines and metals industries for specific uses. This paper includes parametric influences like applied voltage (V), current (I) and gas flow rate (lit./min) on surface roughness (R z ), width of bed thickness (WOBT) and hardness. The article also consists of the development of mathematical models and analysis of variances (ANOVA) for validation to fit of experimental data and developed models. To find out the single as well as multi objective optimization for minimum surface roughness (R z ), minimum width of bed thickness (WOBT) and maximum hardness through desirability function analysis using response surface methodology (RSM) during welding of 304 stainless-steel thin plate by MIG process. This paper also validated the test results at optimal conditions of 26V, 120amp and 21 litter/min gas flow rate and achieved maximum hardness of 97, WOBT of 5.57mm and minimum surface roughness (R z ) of 7.65 m m.

Complex profile and micro-channel generation in silica glass with higher machining depth is an exigent issue to the researchers by μ-ECDM process. Fundamentals of μ-ECDM come from hybrid amalgamation effects of ECM and EDM. The intensity of sparking contributes to material erosion melting and chemical etching of the job-specimen. The paper consists of parametric influences as well as comparative analysis on machining depth (MD) and surface roughness (Ra) using NaOH and KOH at the ratio of 1:0, 3:1, 1:1, 1:3 and 0:1 with varying concentration(wt.%), applied voltage (V), pulse frequency(Hz) and duty ratio (%) in straight as well as in reverse polarity during micro-channel fabrication on silica glass. Different shapes of micro-channel like Zig-Zag, ‘Y’ shaped have been fabricated on silica glass by μ-ECDM process for utilization as a micro-fluidic device using automated spring feed and CAM-follower guided stainless steel (SS) micro-tool. The SEM analysis has been performed to identify the micro-crack and uncut debris into micro-channel. It is found that machining depth has been increased up to 1850 μm with better surface quality using mixed electrolyte of NaOH:KOH::3:1 at direct polarity and also lower TEWR is found using NaOH:KOH::1:3 as electrolyte at reverse polarity.

Micro-parts and devices are manufactured by various processes like electrochemical discharge micro-machining process (µ-ECDM) for utilizing in modern industry. In this paper an automated spring feed µ-ECDM set up with template guided CAM follower mechanism in tool holding unit has been applied for improving micro-machining performances. This paper focuses mainly on the parametric effects of applied voltage (V), pulse frequency (f), duty ratio (%) and electrolyte concentration (wt%) on different machining performances characteristics such as material removal rate, overcut, heat affected zone, surface roughness and machining depth. The paper also includes the analysis on surface roughness integrity of micro-channel cutting on silica glass (SiO2 + NaSiO3) by µ-ECDM process. Application of micro-channels has been reported for utilization as lab on chips or as micro-fluidic channel and achieved better micro-machining performances using spring feed mechanism at the parametric combination of 50 V/10wt%/200 Hz/45% duty ratio/40 mm inter electrode gap (IEG) by µ-ECDM process during micro-channel cutting.

Computer numerical control (CNC) machine has greater utility in the modern advanced industrial field. This paper deals with the parametric effects such as spindle speed (1500-2100 rpm) (N) (X1), depth of cut (DOC) (0.15-0.55 mm) (X2) and feed rate (f) (30-50 mm/min) (X3) on machining characteristics like tool wear rate (TWR) and surface roughness (Ra) during fabrication of IS-617 Aluminum miniature component by advanced CNC lathe using Tungsten-carbide tool. The article analyzes the second-order mathematical model development with co-relation of co-efficient of regression (COR) and analysis of variances (ANOVA) using desirability function analysis during the production of the miniature segment. The paper also consists of multi-criteria optimization for achieving the optimal parametric combination for minimum surface roughness and tool wear rate for this manufacturing operation. The paper also shows the fabricated micro-product of Aluminum at the optimal parametric conditions using CNC programming. It is found that spindle speed has a greater effect on the tool wear rate and depth of cut has dominating effects on surface roughness of job specimen. Desirability parametric combination for minimized surface roughness as well as tool wear rate has been found 1523 rpm/0.15mm/30mmmin-1.