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In this paper, A356/B₄C composites were fabricated using the friction stir processing (FSP) method. The process’s input parameters, including rotational and transverse speed, were optimized using the response surface methodology (RSM). Three factors and three levels with nine experimental runs made up the design of the experiments. An analysis of variance (ANOVA) was employed to determine whether the constructed model was adequate at a 95% confidence level. This study found that transverse speed was the most critical variable affecting the composites’ silicon (Si) particle size, UTS, and force. The findings demonstrate that the Si particle size of the parent material and the dispersion quality of B₄C particles in the aluminum matrix are considerably influenced by the FSP factors, such as rotating speed and transverse speed. Second, tests for tensile strength were conducted to examine the composites’ mechanical properties. Then, using a specially designed fixture to measure force during the process, the forces on the tool, which play a decisive role in determining the tool’s life, were measured in different input parameters. The findings demonstrate that FSP transforms the mechanism of the fracture from brittle to extremely ductile in composites from the as-received metal.

The present work is aimed at optimizing the cutting rate (CR), surface roughness (Ra) and dimensional deviation (DD) in wire electrical discharge machining (WEDM) of EN-31 steel considering various input parameters such as pulse-on-time, pulse-off-time, wire tension, spark gap set voltage and servo feed. A face centered central composite design of response surface methodology (RSM) has been adopted to develop the empirical model for the responses. It is often desired to obtain a single parameter setting that can decrease Ra and DD and increase CR simultaneously. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying all the objectives in any one solution. The optimum search of the machining parameter values for maximization of CR and minimization of Ra and DD are formulated as a multi-objective, multi-variable, nonlinear optimization problem using genetic algorithm weighted sum method to evaluate the performance.

Equal channel angular pressing (ECAP) processed materials have higher grain refinement and strength, and they exhibit more surface roughness when they are machined. This enhancement in the properties highly influences the surface roughness and material removal rate of the materials. The commercial pure aluminum has a wide variety of applications when it is enhanced with high strength properties. In this paper, the machinability of commercially pure aluminum processed through ECAP is investigated in turning operations. Different ECAP processes are carried out to study the microstructural characterization and mechanical properties of the material. The material removal rate and surface roughness are tested by performing the turning operation in the CNC lathe with chemical vapor deposited carbide tool such that the feed rate, spindle speed and depth of cut are considered as the machining variables. To create a hypothesis for the experimentation, the empirical models are developed for the objective functions using the statistical technique response surface methodology (RSM) such that the response models are the objective functions and the model variables are the machining parameters. The response models are verified for the adequacy through ANOVA and $p$-test, and also verified for the closeness with the experimental results. Artificial neural network (ANN)-based empirical equations are also developed for the objective functions using the RSM design matrix and the results of both the RSM and ANN are compared for the suitability.

Traveling Wire Electro-Chemical Spark Machining (TW-ECSM) process is a new innovative thermal erosion-based machining process suitable for cutting electrically nonconductive materials using tool electrode in the form of wire. This article attempts experimental modeling of TW-ECSM process using a hybrid methodology comprising Taguchi methodology (TM) and response surface methodology (RSM). The experiments were carried out on borosilicate glass using L${}_{27}$ orthogonal array (OA) considering the input parameters like applied voltage, pulse on-time, pulse off-time, electrolyte concentration and wire feed velocity along with process performances such as material removal rate (MRR), surface roughness (R${}_{\text{a}})$ and kerf width (K${}_{\mathit{\text{w}}})$. The interaction influence of input parameters on process performances was also discussed. Further, multi-objective optimization (MOO) of response performances of TW-ECSM process is executed using a coupled approach of grey relational analysis (GRA) and principal component analysis (PCA). The optimal process parameter setting illustrates the improvement of MRR by 171%, diminution of R_a and K${}_{\mathit{\text{w}}}$ by 27% and 8% against the initial parameter settings. Moreover, irregular cutting of kerf width and surface characteristics were also scrutinized using scanning electron microscope (SEM).

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.

Drilling, which constitutes one third of the machining operations, is widely used in many areas of the manufacturing industry. Various difficulties are encountered in the drilling process since the chip is formed in a closed limited chip flows. These difficulties directly affect the output parameters such as energy consumption, surface quality, and cutting force. Therefore, it is necessary to determine the ideal processing parameters to achieve the best performance. However, experimental research on machining processes requires both a long time and a high cost. For these reasons, machining outputs can be estimated by conducting drilling simulations with the finite element method. In this study, the finite element method is used in order to investigate the influence of different cutting parameters and different helix angles on the power and thrust force of Ti–6Al–4V (grade 5) alloy that is commonly used in the aviation industry. The study selected three different cutting speeds, feed rates, and helix angles as the cutting parameters. The experimental design was made according to the response surface method (RSM) Box–Behnken design in the Design-Expert program. Drilling simulations were performed using the ThirdWave AdvantEdge^TM software. The lowest thrust force measured is 1241.39 N at 40° helix angle, 2000-rpm revolution rate, and 0.05-mm/rev feed rate, while the lowest power consumed is 765.025 W at 30° helix angle, 1500-rpm revolution rate, and 0.05-mm/rev feed rate. As a result, it was determined that the most effective parameter for power and thrust force was the feed rate.

The present contribution describes an application of a hybrid approach using response surface methodology (RSM) and particle swarm optimization (PSO) for optimizing the machining parameters in electric discharge machining (EDM) of compo casted Al6061/ cenosphere AMCs. Compo casting processing route was employed to prepare the AMCs. Each experimentation in EDM was performed under different machining conditions of Peak Current (Amp), Pulse on time ($\mu$s), and Flushing Pressure (bar). Performance characteristics such as material removal rates (MRRs), surface roughness (SR), and electrode wear rates (EWRs) were evaluated. A Taguchi L27 orthogonal array was considered to plan the experimentation and RSM was applied to model the inter relationship between the input process parameters and responses. A mathematic model has been developed to provide a fitness function to PSO by unifying the multiple responses. Finally, PSO was used to predict the optimal settings of the processing condition for the multi-performance optimization of the EDM operation. The experimental observations confirm the feasibility of the strategy and are in good accordance with the predicted value over a wide range of processing conditions employed in the process.

Electrolysis is a method used for producing copper (Cu) nanoparticles at faster rate and at low cost in ambient conditions. The property of Cu nanoparticles prepared by electrolysis depends on their process parameters. The influence of selected process parameters such as copper sulfate (CuSo₄) concentration, electrode gap and electrode potential difference on particle size was investigated. To optimize these parameters response surface methodology (RSM) was used. Cu nanoparticles prepared by electrolysis were characterized by using X-ray diffraction (XRD) and scanning electron microscope (SEM). After reviewing the results of analysis of variance (ANOVA), mathematical equation was created and optimized parameters for producing Cu nanoparticles were determined. The results confirm that the average size of Cu particle at the optimum condition was found to be 17nm and they are hexagonal in shape.

The manufactured products that rely on molten Acrylonitrile Butadiene Styrene (ABS) via Tederic machine (450t) suffer from some types of defects such as black dots, shrinkage, and bubbles, which weaken the competitiveness. Therefore, this work proposes the Jidoka recruit’s network system (JRNS) to set optimal operating parameters to reduce the processes’ output variations and cut down on total process wastage autonomously. The suggested JRNS consists of two sequential stages. The first stage in front propagation relies on response surface methodology (RSM) in classifying the significant operating parameters from 15 candidates experimentally (DOE) to identify the most crucial one. The second stage predicts the processes’ deviation by integrating two meta-heuristic methods called harmony search (HS) and weighted superposition attraction (WSPA) in the backpropagation to automatically reset the operating parameters to keep the product within the standard specification to avoid 11 defect chances. JRNS is innovative and has been used to upgrade autonomous control for enhancing the Tederic machine (450t) process to reduce 70.98% of molding defects. The JRNS (RSM, $\mathrm{\text{WSPA}}+\mathrm{\text{HS}}$) increases the operation efficiency from 94% to 100% and reduces the defects per million opportunity to the six sigma scale.

Shape memory alloys (SMAs) are an excellent material for producing components for a wide range of industrial applications, such as orthopedic implacers, micro-equipment, actuators, fittings, and screening components, as well as military equipment, aerospace components, bio-medical equipment, and fabrication requirements. Despite its remarkable qualities, the production of SMAs is a problem for investigators all over the globe. The purpose of this research is to evaluate the effects of altering the $T_{\mathrm{\text{on}}}$, $T_{\mathrm{\text{off}}}$, $I_{\mathrm{\text{p}}}$, and GV while processing copper-based SMA in an electrical discharge machining process on the material removal rate (MRR) and surface roughness (SR). The major runs were designed using a central composite design. SEM was also utilized to examine the micro-structure of EDM-processed electrode tools and work samples. SEM scans indicated the presence of debris, micro-cracks, craters, and a newly formed recast layer on the electrode tool and workpiece surface. High $I_{\mathrm{\text{p}}}$ and prolonged $T_{\mathrm{\text{on}}}$ provide huge spark energy simply at the work sample-tool contact, resulting in debris production. The experimental results reveal that the least and highest MRR values are 10.333 and 185.067mm³/min, respectively, while the minimum and maximum SR values are 3.07 and 7.15$\mu$m. The desirability technique, teacher learning based optimization (TLBO), and the Jaya algorithm were also utilized to optimize the studied solutions (i.e. MRR and SR) on a single and multi-objective basis. The best MRR and SR were determined using the desirability approach, the Jaya Algorithm, and the TLBO to be 152.788mm³/min and 4.764$\mu$m; 240.0256mm³/min and 1.637$\mu$m; and 240.0257mm³/min and 1.6367$\mu$m.

Precision micro-component fabrication demands suitable manufacturing processes that ensure making of parts with good form and finish. Mechanical micro milling represents a flexible and powerful process that exhibits enhanced capability to create micro features. Bulk metallic glass (BMG) represents a young class of amorphous alloy material with superior mechanical and physical properties and finds appreciable micro scale applications like biomedical devices and implants, micro parts for sport items and various other micro- components. In the present work, an attempt has been made to analyze the influence of the cutting parameters like spindle speed, feed per tooth and axial depth of cut on the machinability of BMG, in mechanical micro-milling process. The micro-milling process performances have been evaluated concerning to cutting forces and surface roughness generated, by making full slots on the workpiece with solid carbide end mill cutters. The paper presents micro-machining results for bulk metallic glass machined with commercial micro-milling tool at low cutting velocity regime. Response surface methodology (RSM) has been employed for process modeling and subsequent analysis to study the influence of the combination of cutting parameters on responses within the selected domain of cutting parameters. It has been found that the effect of axial depth of cut on the cutting force components is remarkably significant. Cutting force components increases with the increase in axial depth of cut and decreases with increase in spindle speed. At low feed rate, cutting force in the feed direction (F_x, i.e., cutting force along x-direction) increases with a decrease in feed rate. This increase of force could be due to the possible ploughing effect. A similar pattern of variation has been observed with cutting force component in cross-feed direction (F_y) also. It has been found that effect of feed per tooth on the roughness parameter R_a is remarkably significant. Surface roughness increases with feed per tooth. Axial depth of cut does not contribute much to the surface roughness. Surface roughness decrease with the increase of spindle speed.

In bone-drilling operations, undesirable temperature rises are experienced due to high-contact friction. These increases in temperature can damage bone and soft tissues from time to time. When the temperature exceeds 47^∘C, osteonecrosis occurs. This article presents a new method for both the selection of optimum drilling parameters and the mathematical temperature model ($T$^∘C). In this study, the optimum parameter values for bone-drilling operations were found using gray relational analysis, and a mathematical model was created based on the temperature parameters using the response surface method. The accuracy of the developed analytical model has been proven by ANOVA. As a result, it has been revealed that the value of spindle speed is the most effective factor in bone-drilling operations and that the developed analytical model and experimental measurements are in harmony.

In this investigation, the method of minimum quantity lubrication (MQL) is utilized in the procedure of finishing milling of AISI H11 steel instead of the conventional lubrication method. The variants are three three-level experimental cutting parameters, including $V_c$-cutting speed, $f_z$-feed per tooth, and $a_p$-depth of cut. The responses are production rate (MRR, in mm³/min), cutting force ($F_c$, in N), and surface roughness ($R_a$, in $\mu$m). The purpose of this study is to generate the mathematical regression models for the responses ($R_a$, $F_c$, and MRR), and solve the multi-objective optimization problem to estimate the appropriate input parameters respecting the defined criteria for $F_c$, $R_a$, and MRR. Experimental research was conducted with an experimental matrix designed by Box–Behnken Design (BBD). The experimental runs were executed on a 5-axis CNC machine tool, model DMU50. The desirability function (DF) method is used to resolve the problem of multi-attribute optimization. The results show that the optimum process variables include $V_c=210$ m/min, $f_z=0.059$ mm/tooth, $a_p=0.196$ mm, corresponding to $R_a=1.819\mu$m, $F_c=152,326$ N, and $\mathrm{\text{MRR}}=1299.177$ mm³/min.

Inconel-625 is a high-performance nickel-based superalloy which offers exceptional properties such as extensive resistance to corrosion, high strength-to-weight ratio, hardness, and impressive heat tolerance. Machining precise holes with required dimensional accuracy is challenging in Inconel-625 using conventional drilling processes. The investigation aims to improve the quality characteristics of hole machined on Inconel-625 by using the abrasive aqua jet drilling (AAJD) process. The influence of jet pressure ($J_P$), table feed ($T_F$), mass flow rate ($M_{\text{FR}}$) and gap distance ($G_D$) on the erosion rate ($E_R$), surface roughness ($R_a$), circularity error (CI_error) and striation zone ($S_{ZN}$) are investigated. The weighted principal component analysis (WPCA)-based response surface methodology (WPC-RSM) is employed to analyze and optimize process parameters. The optimal parameter settings ($J_P$-300 MPa, $G_D$-1.5 mm, $T_F$-64 mm/min, $M_{\text{FR}}$-0.55 kg/min) are observed to produce substantial improvement in response. Comparing initial and optimal conditions, the surface roughness ($R_a$) is decreased by 10.15% from 3.25 $\mu$m to 2.92 $\mu$m. The CI_error and $S_{ZN}$ are also reduced by 38.02% and 12.74%, respectively. The erosion rate ($E_R$) is improved by 8.79% with the optimal settings. $J_P$ is found to be the most influential parameter, followed by $M_{\text{FR}}$. Scanning electron microscopy (SEM) pictures and 3D roughness plots are used in the surface topography analysis.

Electrical discharge machining (EDM) is a non-conventional process utilized for machining electrically conductive materials which cannot be machined by conventional processes. This paper is focused on modeling and optimization in electrical discharge machining (EDM) of UNS T30407 steel. The variables such as pulse on time (Ton), pulse off time (Toff), and current will influence the material removal rate (MRR) and surface roughness (Ra) which were taken as response variables. Experimental data were collected as per the central composite design (CCD) matrix of response surface methodology (RSM) approach and mathematical models have been developed for both the response variables. The analysis of variance (ANOVA) was employed to determine the significant machining parameters and optimize the input parameters for both the responses (MRR and Ra). The statistical adequacy of the models is tested with the help of ANOVA. The optimum parametric combination for maximum MRR and smaller Ra (highest surface finish) was found. The surface characteristics of the machined surface were investigated by a scanning electron microscope (SEM).