Narrow Results

Plasma spraying is a prospective method frequently employed because of its increased effectiveness in ceramic coatings. Optimizing process parameters is necessary to maximize the coating performance. This study employed response surface methodology (RSM) to investigate the impact of process variables, including current, powder feed rate, and standoff distance, on the porosity and corrosive wear loss of Cr₃C₂$+$8YSZ composite coating. Experiments were done using the central composite design method, and quadratic regression models were created for the responses based on the completed trials. All parameters are observed to be most significant as the obtained p-value is under the threshold of 0.05 as per the analysis of variance (ANOVA) calculations. The optimal plasma spray process parameters were determined to be 500 A of current, a powder feed rate of 46gm/min, and a standoff distance of 3 inches for the expected lowest corrosive wear loss of 0.000025mm/year and a reduced area percentage porosity value of 0.93.

This paper aims to conduct an economic and environmental machining procedure (dry condition) during face milling of the Polyoxymethylene Co-polymer. In this context, an experimental study based on Taguchi’s design was conducted to develop empirical models using Response Surface Methodology (RSM) on one hand and Support Vector Machine (SVM) for regression on the other hand. The ANalysis of VAriance (ANOVA) is conducted to determine the contribution and the significance of each cutting parameter on the surface quality and productivity (MRR). The obtained models are being compared to determine the most efficient approach. The last part is to find the optimum cutting combination by using Genetic Algorithm (GA) optimization based on SVM models, whether to minimize surface roughness Ra, or for composite objective to improve the quality and to increase the productivity. The results show that feed per tooth (fz) is the most affecting parameter on Ra followed by depth of cut (ap) and then the cutting velocity (Vc). SVM was more robust than RSM with less deviation error.

The aim of this study is to highlight the importance of optimizing machining parameters to improve the performance and surface integrity of Inconel-825 superalloy using the Electrical Discharge Turning (EDT) process, an important configuration of Electrical Discharge Machining (EDM). The study uses a Face-Centered Central Composite Design (FCCCD) to conduct experiments and applies the Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) to optimize input parameters. Various factors like Gap Current (Ig), pulse on time (Ton), rotational speed (N), and Magnetic field assistance (B) are adjusted at different levels, while outcomes such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), and Surface Roughness (Ra) are measured. Analysis of Variance (ANOVA) is used to understand the impact of each input factor on the outcomes. The results demonstrate that both RSM and MOGA provide accurate predictions closely aligned with experimental results, with MOGA showing a slight advantage in predicting tool wear and surface roughness. Specifically, the RSM solution achieved a desirability of 0.693 with parameters Ig at 8 A, Ton at 48.082$\mu$s, speed at 1399.988RPM, and magnetic field at 0.3T, achieving MRR of 5.182mg/min, TWR of 3.138mg/min, OC of 166.716$\mu$m, and Ra of 2.047$\mu$m. The MOGA solution featured parameters Ig at 8.045 A, Ton at 48.557$\mu$s, speed at 1360.3 RPM, and magnetic field at 0.3T, yielding an MRR of 5.169mg/min, TWR of 2.983mg/min, OC of 170.037$\mu$m, and Ra of 2.060$\mu$m. SEM analysis confirmed improved surface quality under optimized conditions, while XRD analysis showed significant grain refinement and increased dislocation density.

Electrical discharge machining (EDM) was performed on a copper electrode and the implications of changing factors such as current, pulse on time, pulse-off time, spark gap voltage and speed have been investigated in this research. The process used an intermetallic MoSi₂–SiC ceramic composite. Multiple performance characteristics, including material removal rate, electrode degradation rate and surface roughness, run out, radial overcut, circularity, cylindricity and perpendicularity were considered when optimizing these parameters using the Taguchi-based ${\text{L}}_{25}$Orthogonal Array with Design of Experiments (DoE). For this purpose, techniques such as analysis of variance (ANOVA), Response Surface Methodologies (RSM) and graphical analysis were utilized. The outcomes revealed that geometric tolerances were decreased as a consequence of significant improvements in the rates of material removal, tool degradation, form tolerance, and orientation tolerance. The optimal machining settings for producing high-quality holes and electrodes in the conductive MoSi₂–SiC composite were determined by scanning electron microscopy (SEM) testing the anomalies of the machined composite for various holes of trials. The experimental results suggest that the precision, presence of microvoids and surface roughness of the copper electrode can be enhanced through selecting an appropriate optimal combination.

In this investigation, Ti6Al4V was used as the base material for the shot peening process. Three major influencing parameters such as peening time, peening distance, and peening pressure were examined. The substrate was shot peened with stainless steel shot, with an average diameter of 0.6mm. The process parameters were optimized using the statistical tool Response Surface Methodology. A three-factor, five-level composite design matrix was employed to minimize the number of trial runs. The effect of shot peening parameters on hardness, surface roughness, and coefficient of friction was optimized. The adequacy of the model was checked using an analysis of variance. From the test results, it was observed that the peening performed with a shot peening time of 20s, a peening distance of 100mm, and a peening pressure of 3 bar resulted in a higher hardness of 433 VHN, a surface roughness of 5.8, and a coefficient of friction of 0.22. This may be attributed to the optimal residual compressive strength achieved through the shot peening process.

30-pair AlAs/GaAs distributed Bragg reflector (DBR), which has 1030 nm center reflectivity, is studied extensively by means of High Resolution X-ray Diffraction (HR-XRD) and reflectivity measurements. Theta/2-Theta measurements and dynamical simulations have been done for (002), (004) and (006) planes to determine strain and thickness of AlAs and GaAs layers in the DBR stack. Reciprocal space mappings (RSMs) are measured for same planes and also for (224) plane to find out tilt and relaxation of the DBR stack. Relaxation is not observed and it is confirmed with symmetric in-plane (400) Theta/2-Theta and RSM measurements. This is a first study in the literature according to the best of our knowledge. Finally, we have shown sensitivity of high angle diffraction planes to disorders in crystal. Angle-dependent reflectivity simulations have been also done and compared with measurements. 99.99% reflectivity is obtained with 99.5 nm stop bandwidth and 482.7 nm penetration depth.

The porthole die extrusion process of profiled cross-section hollow aluminum alloy is influenced by numerous factors, which brings inconvenience to the process design. In this paper, 7075 aluminum alloy is taken as an example, the fitting model of the ultimate load is analyzed by variance and regression analysis using response surface method (RSM). The influences of extrusion speed, friction factor and initial temperature on the change of extruded ultimate load are investigated systematically, and the important influence factors (initial temperature $>$ friction factor $>$ extrusion speed) to the load are determined eventually. By comparison, the error between the ultimate load model obtained after fitting and the calculated value is only 2.4%, further verifying the reliability of this model. The optimal objective is to minimize the ultimate load, then the optimum technological parameters are obtained by optimizing the process, where the initial temperature, the extrusion speed and the friction factor are 430${}^{\circ }$C, 2.28mm/s and 0.31, respectively. The results provide a theoretical basis for the scientific design of the porthole die extrusion process of profiled cross-section hollow aluminum alloy.

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.

Due to its vast industrial applications and thermal engineering, the investigation of the inconsistent heat and mass transfer that drives the flow of squeezing viscous nanofluids between two plates is an interesting topic. In this case study, we have investigated the heat transfer analysis of unsteady viscous nanofluid between two parallel plates by using Response Surface Methodology (RSM). The partial differential equations illustrating the flow model are converted to nonlinear ordinary differential equations by suggesting similarity transformations. The resulting dimensionless and nonlinear ODEs of temperature functions and velocity are solved using the well-known numerical technique bvp4c by transforming the problem into initial value problem from boundary value problem. The results found are consistent with this numerical solution. These numerical values are then used to optimize the different input parameters and to design an experimental model. RSM is used to develop an experimental model for skin friction coefficient. ANOVA tables are generated for input parameters and then sensitivity analysis is performed for output response. Further, the impacts of different parameters on temperature profiles and velocity are graphically explored. The results are compared with the results solved by HPM. The results concurred with this numerical solution. These findings considered much be useful in the application of polymer processing, power transmission, compression, temporary loading of mechanical parts, food processing, cooling water, gravity machinery, modeling of plastic transport in vivo, chemical processing instruments, and demolition due to freezing.

The Ti-6Al-4V alloy is known as materials with high hardness and good mechanical properties. However, the machining process of this material faces many difficulties due to unfavorable machining conditions. When the machining parameters are not selected in accordance with the Ti-6Al-4V alloy, the surface quality of workpieces does not meet technical requirements. Therefore, the appropriate machining parameters are needed to upgrade the surface roughness of workpieces in high-speed machining (HSM) conditions. The main cutting forces Py and Pz, and surface roughness Rz during turning of Ti-6Al-4V alloy in HSM were investigated and evaluated in this study. The response surface method (RSM) was used to conduct experiments and establish the models of Rz, Py, and Pz under various cutting conditions of feed rate (S), tool nose radius (R), and approach angle ($\phi$). As a result, the S value has the largest impact on the Pz. Moreover, the machining parameters such as S, R, and $\phi$ have a great influence on the Py. The magnitude of Rz is mainly affected by R value. Based on the optimal parameters, the average value of the force Pz has decreased from 238N to 169N, corresponding reduction of about 29%. The value of the force Py has reduced from 184N to 118N, corresponding reduction of about 36%. In addition, the surface roughness of the workpiece has been improved significantly from Rz of 5.6 $\mu$m to 2.7 $\mu$m under optimal parameters condition. The surface roughness of the workpiece after processing in high speed machining was reduced of 52% compared with cutting under non-optimal parameters condition. The best surface quality and minimum cutting force could be achieved under optimal processing parameters in high speed cutting method.

Cenosphere fly ash particles are incorporated into AA6061 alloys with different concentrations ranging from 0wt.% to 10wt.% using a modified semi-solid metal processing technique. X-ray diffraction patterns were recorded to analyze the morphology of the aluminum-based metal matrix composites (AMCs). The major diffraction peaks of Al, SiO₂, Al₂O₃ and Fe₂O₃ are distinctly identified which revealed the presence of cenosphere particles and their integrity within the matrix is preserved. The high-resolution optical micrograph identifies the homogeneous distribution and uniform dispersion of the particles. Machinability of the prepared AMCs was investigated by electro discharge machining (EDM) using response surface methodology (RSM). Face-centered CCD of RSM was considered to design the number of experimental runs required. ANOVA was used to explore the influence of selected process parameters and their interactions on the performance characteristics of the systems by developing a second-order quadratic mathematical model for all the responses. Pulse on-time and pulse current were observed to be the most influencing independent variables of EDM system that affect the selected performance measures during spark erosion process. Finally, desirability function approach was employed to optimize the parameters. The optimal processing condition was identified as follows: pulse current: 6 A, pulse on-time: 1010$\mu$s, percentage of reinforcement: 2% and flushing pressure: 0.2 MPa. Very small percentages of deviation have been observed while comparing with the experimental results obtained for MRR (8.6%), TWR (10.3%) and SR (2.18%).

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.

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.

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.

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.

In the present scenario, electrochemical arc machining (ECAM) (hybrid of electric discharge erosion and electrochemical dissolution) is an evolving procedure for difficulty in machining the materials due to constraints of existing processes. This research aims to investigate the machinability of Ni${}_{55.7}$Ti alloy through electrochemical arc drilling using molybdenum electrode. Electrolyte concentration (ethanol with ethylene glycol and sodium chloride), supply voltage, and tool rotation are considered as the variable factors to evaluate the ECAM performance characteristics in drilling blind hole operation concerning overcut (OC), tool wear rate (TWR) and materials removal rate (MRR). Consequently, response surface methodology is implemented for predictive modeling of various performance characteristics. Finally, multi-objective optimization through desirability function approach (DFA) has produced a set of optimal parameters to improve the productivity along with the accuracy, which is the prime requirement for the industrial applicability of the ECAM process. Results demonstrated that supply voltage is the influential key factor for improvement of machining rate. Scanning electron microscope (SEM) photographs revealed the development of heat affected zone (HAZ), white layer, melted droplet, craters, re-solidified material, ridge-rich surface and voids as well as cavities around the end-boundary surfaces of a blind hole. Composition analysis through energy dispersive spectroscopy (EDS) indicated the oxygen content on the machined surface because electrolyte breakdown causes oxidation to take place at elevated temperatures across the machining zone. Moreover, carbide precipitation like TiC was found in the melting zone of the drilled hole, as revealed by X-ray diffraction (XRD) analyses, which has the affinity to reduce the SMA properties in HAZ.

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.

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.

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.

In the plastic industry for mold making, pocket milling is applied. The surface finish of the mold affects the quality of the plastic product, especially for toys. This can be achieved by minimising the surface roughness of the mold. To get a good quality product with a better production rate, the selection of the best combination of parameters in pocket milling is necessary. Multi-response optimisation can be applied for selecting such parameters which are suited for fulfilling the objective. In this study, one of the toy mold designs is selected as a pocket profile on which, two tool trajectories, viz Follow Periphery (FP) and Zigzag (ZZ), are applied for generation of pocket by varying Speed (S), Feed (F) and Step Over (SO). Box–Behnken Response Surface Methodology is applied to find the experimental runs. Two conflicting objectives minimising Surface Roughness (SR) and maximising Material Removal Rate (MRR) are obtained by applying Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithm (MOGA). Conformational experiments were conducted for the random set of Pareto results obtained from MOGA for both the tool trajectories to validate the model. From the analysis, it is observed that the FP tool path strategy is well suited to generate the pocket to get minimum SR and maximum MRR as the error percentage between the predicted and test results observed is 0.8085% for SR and 0.9236% for MRR.