Parameters For Material Removal Rate Research Articles

Optimization of µ-WEDM Parameters for MRR and SR on Ti-6Al-4V

Micro EDM is unconventional metal removing technique that is effective in machining hard-to-cut conductive materials. It has a big potential in modifying surfaces of metallic bone implants for better biocompatibility by providing proper surface topography to ease cell adhesion. However, it is still important to study machining performance. This paper investigates material removal rate (MRR) and surface roughness (SR) of micro WEDM on Ti-6Al-4V alloy. Three level Taguchi’s design was implemented to observe the effect of capacitance and gap voltage. Moreover, analysis of variance (ANOVA) and grey relation analysis (GRA) allowed to investigate contribution of each parameter and find their best combination for multiple output optimization. Results showed that highest MRR of 1.72*10-2 mm3/s can be achieved at 10 nF and 90 V values, while smallest SR of 0.309 µm can be achieved at 1nF and 90 V. In addition, the contribution and significance of capacitance on MRR and SR was considerably higher than the effect of gap voltage. Lastly, the optimal parameters for multiple output performance were calculated at 10 nF and 90 V values.

Optimization of PMEDM process parameters for B4C and B4C+SiC reinforced AA7075 composites

MaterialsWith sufficient electrical conductivity can be successfully processed by applying the electrical discharge machining (EDM) method; however, due to the presence of non-conductive particles in composites, which have been produced by adding ceramics particles, problems such as unstable machining, low material removal rate, and high tool wear are observed during the EDM. This study employed powder-mixed electrical discharge machining (PMEDM) by adding electrically conductive nano-size graphite powder into the dielectric liquid to minimize these problems. Moreover, the machinability of AA7075/ B4C and AA7075/ B4C+SiC composites was evaluated using the Taguchi method. The experimental study used L18 orthogonal array (OA) (21 ×32). ANOVA was employed to obtain significant parameters and percent contributions of variable parameters on the material removal rate (MRR). Reinforcement ratio, current and sintering time applied to the workpiece were chosen as variable parameters. The optimum parameters for MRR were obtained at A1B3C3 (reinforcement ratio= 10%, current= 8 A, sintering time=150 min). According to S/N ratio graphs, increasing the reinforcement ratio leads to a decreased MRR. On the contrary, when the applied current increases, MRR increases. Additionally, analysis results show that the discharge current is the most important parameter affecting MRR. In the morphological examinations, it was understood that the amounts of B4C and SiC particles in the composite structure affect the quality of the machined surfaces. It was determined that the surface quality deteriorated with the increase in the amount of SiC and B4C in the composite structure and the increase in the discharge current.

Investigation and optimization of wire EDM process parameters for Al 5454 alloy machining

In the current research, the use of a wire electric discharge machine (WEDM) to machine aluminium 5454 alloy work material is studied. A zinc-coated brass wire with a diameter of 0.25 mm is used as the tool material, and deionized water is used as the dielectric fluid. In this work, different parameters like pulse on time (Ton), current, power, wire feed rate, wire tension, and flushing pressure are varied for analysis of the performance of material removal rate (MRR) and surface roughness (SR). Taguchi L16 is used to design the experiments. Multi-response optimization is done by the multi-response signal-to-noise ratio (MRSN) method to obtain the best combination of parameters. To find the significance of parameters, the Analysis of Variance (ANOVA) technique has been used. From the experimental investigation, it was found that current is the most influential parameter and flushing pressure is the least influential parameter for MRR and SR. The optimal level of input parameters obtained using the optimization method for material removal rate and surface roughness are Peak current = 15A, Pulse on time = 32 s, Wire tension = 15 N, Wire feed = 165 mm/min, Power = 25w, flush pressure = 7 bar. The confirmation experiments are conducted to verify the results obtained by the MSRN method. Optimal WEDM settings for machining Al 5454 alloy samples enhanced improvements in material removal rate and surface finish.

Parametric optimization and prediction of MRR and surface roughness of titanium mixed EDM for Inconel 718 using RSM and fuzzy logic

Powder mixed electric discharge machining (PMEDM) was developed to improve the machining rate and surface quality of conventional EDM. In this study, PMEDM was performed on Inconel 718 by adding titanium particles to the dielectric fluid. The input parameters selected for the experiment were powder concentration, pulse current, gap voltage, pulse on time and pulse off time and the effects of the input parameters were investigated on the material removal rate (MRR) and the surface roughness of the sample. Response surface methodology (RSM) was used to construct the design of experiment. Analysis of variance (ANOVA) was carried out to find out the significant input parameters for each response. Further, Mamdani based fuzzy logic was developed using the experimental data and used to predict the responses in optimized condition. The multi-objective optimization was performed using the RSM desirability function approach to maximize the MRR and minimize the surface roughness. From ANOVA, it is observed that powder concentration, pulse current, gap voltage and pulse on time are the significant input parameters for MRR and surface roughness. The fuzzy result is compared with the experimental data and found the accuracy of prediction is 89.21 % for MRR and 91.23 % for surface roughness. The optimized setting of input parameters is obtained as powder concentration = 8 g/l, pulse current = 9.5 A, gap voltage = 60 V, pulse on time = 150 µs and pulse off time = 20 µs and the corresponding optimum value of MRR and surface roughness are 16.623 mm 3 /min and 3.71 µm respectively. The SEM result indicates that the width of the surface crack is found more in the optimized sample compared to the sample having the least surface roughness. From the EDX analysis, it is found that a very small amount of copper and titanium particles were included on the surface during the machining.

Experimental Investigation of Material Removal Rate Parameters in ECM for Aluminum Hybrid Matrix Composites Using the RSM Technique

In the present work, the preparation of AA-6082/ZrSiO4/TiC hybrid composite is studied along with an analysis of the effects of electrochemical machining parameters such as feed rate of electrode (FE), voltage (VO), electrolyte concentration (EL), and electrolyte discharge (ED) rate on the output responses of the material removal rate (MRR) and surface roughness (SR) for Al hybrid composites. The experiments are carried out based on the Taguchi L16 orthogonal array and the important process parameters are found for MRR and SR. Each parameter contains four different levels that are FE (0.10, 0.15, 0.20, and 0.25 mm/min), VO (10, 15, 20, and 25 V), EL (15, 20, 25, and 30 g/lit), and ED (1.5, 2, 2.5, and 3 lit/min).The optimization software, namely, Minitab-17 version helps to find the contribution of each parameter on MRR and SR. The ANOVA result reveals that the feed rate of electrode is the highest contributing parameter, trailed by the electrolyte discharge rate and other process parameters for MRR and SR. A linear model of regression and interaction plots is also included to show the relationship between the parameters. From the observational results, the highest MRR (0.00953 mg/min) is attained by the parameter combination level of the feed rate of electrode of 0.20 mm/min, voltage of 25 V, electrolyte concentration of 20 g/lit, and electrolyte discharge rate of 1.5 g/, whereas the lowest MRR is found at FE of 0.10 mm/min, VO of 10 V EL-15 g/lit and ED of 2.5 g/litre. For SR, the maximum and minimum are recognized at FE2-VO4-EL3-ED2 (0.15 mm/min, 25 V, 25 g/lit, and 2 lit/min) and FE1-VO1-EL1-ED1 (0.10 mm/min, 10 V, 15 g/lit, and 1.5 lit/min), respectively. Finally, the increment of MRR and SR values is mostly dependent on the feed rate of the electrode.

Evaluating machining performance of acrylonitrile-butadiene-styrene (ABS) based electrical discharge machining (EDM) electrodes fabricated by fused deposition modelling (FDM) followed by a novel metallization method

In the present study machining performance of a novel ABS P400 based EDM electrode is experimentally investigated. Three EDM parameters that is, current ( I), pulse on time ( Ton) and pulse off time ( Toff), each at three levels, are considered, and mild steel is machined using these electrodes according to the response surface methodology (RSM) based face-centred central composite design (FCCCD). Machining performances such as material removal rate ( MRR), tool wear rate ( TWR) and surface roughness ( SR) are measured. Experimental results are analysed using analysis of variance (ANOVA), response graphs and 3D surface plots. Current is found to be the dominating parameter for MRR, TWR and SR. The optimal combination of EDM parameter for multi-performance is determined using teaching-learning based optimization (TLBO) algorithm. The effectiveness of the new electrodes is established by comparing their machining performances with the already developed electrode at the same machining parameter setting. MRR resulting from the new electrode and also its TWR are found to be in good agreement with those of already produced electrodes. However, SR of the machined surface using new electrodes is found to be relatively higher.

Optimization Of Machining Parameters of CNC Milling Operation for Material Removal Rate and Surface Roughness on En-24 Steel Using Taguchi Method

The significance of quality and productivity in the manufacturing industry cannot be overstated, as they directly impact profitability. To remain competitive and keep pace with advancements in technology, manufacturing industries must continuously improve their processes to enhance the quality and productivity of their products. One technology that has contributed to such improvements is CNC milling machines, which have been used in this study. For this study, the high-strength, ductile, and wear-resistant steel alloy EN24 was selected for milling. Taguchi L9 orthogonal array proposal of experiment was used to select cutting parameters, and a total of nine milling operations were conducted. Rate of material removal and surface roughness were calculated for all the nine experiments. Signal-to-noise (S/N) ratios and mean values were used to identify the influencing cutting parameter for material removal rate and surface roughness. ANOVA technique was employed to calculate the optimal cutting parameters for achieving better material removal rate and surface roughness. To analyze the parameters that influence MRR and surface roughness, a comparison was made between the S/N ratios and initial readings using ANOVA technique. Overall, this study demonstrated the importance of selecting appropriate cutting parameters for achieving optimal MRR and surface roughness of CNC milling procedures, which can lead to improvements in quality and productivity in the manufacturing industry.

Performance of Powder mixed Drilling EDM on Biomedical Ti-6Al-4V Alloy

Titanium alloys have gained a lot of attention in biomedical due to their novel properties. In fact, their low elastic modulus and good biocompatibility have promising potential in load bearing bone replacement implants. However, a big part of successful implantation relies on proper surface treatment, which can enhance its performance. Electro discharge machining (EDM) is one of the surface treatment techniques that can be beneficial for proper surface forming. For that reason, this study investigates the machining conditions of EDM on Ti-6Al-4V titanium alloy. In details, this paper evaluates the effect of machining parameters of pulse current, time-on, time-off, gap voltage and powder on material removal rate (MRR) and overcut. Moreover, an experiment set based on L9(34) Taguchi’s orthogonal array was conducted and the analysis of variance was observed. In order to investigate the optimum machining condition, grey relation analysis (GRA) and analysis of variance (ANOVA) were used. In conclusion, the best performance parameters for MRR, overcut and multiple characteristics of the orthogonal array were observed and predicted improvements are calculated in this paper.

Multi-objective parametric optimization on the EDM machining of hybrid SiCp/Grp/aluminum nanocomposites using Non-dominating Sorting Genetic Algorithm (NSGA-II): Fabrication and microstructural characterizations

AbstractIn this study, different input parameters for electric discharge machining (EDM) are examined in order to revise the distinctiveness of EDM for machining aluminum-based hybrid metal matrix composites (MMCs). The versatility of hybrid aluminum MMCs makes them very popular and sought after in the automotive, aerospace, marine, and space industries. In this article, an optimized process parameter setting for hybrid MCCs machining with an EDM machine is determined that have silicon carbide (SiCp) and graphite (Grp) particles added as reinforcement materials in varying amounts (Al–0.7Fe–0.6Si–0.375Cr–0.25Zn/10 wt%SiC/3 wt%Gr–MMC, Al–0.7Fe–0.6Si–0.375Cr–0.25Zn/15 wt%SiC/5 wt%Gr–MMC, and Al–0.7Fe–0.6Si–0.373Cr–0.25Zn/20 wt%SiC/8 wt%Gr–MMC). The stir casting method was used to prepare these hybrid aluminum MMCs (3 samples). A study of surface roughness (SR) and material removal rate (MRR) was conducted to examine the effects of dominant parameters. An experiment is planned using a central composite rotatable design (CCRD) of response surface methodology (RSM). It is possible to predict MRR and SR with 95% degree of accuracy by utilizing the quadratic model. Non-dominating Sorting Genetic Algorithm-II was employed to solve “mathematical models” for multi-objective optimization of output response characteristics. The scanning electron microscope (SEM) images of the tool and workpiece materials show that the recast layer has been formed on the tool face and the surface of the machined work-piece. Based on the results, it was determined that an optimal value of MRR (2.97 g·min−1) was obtained at 90 µs, 30 µs, 7.0 V, and 14 A asPon,Poff, gap voltage, and peak current, respectively. As a result of the findings, the SR is reciprocally proportional toPon, and the SR is commensurate withPoff. It was determined that the optimal value of SR (2.41 µm) could be attained at 30 µs, 52 µs, 6.0 V, and 12 A as thePon,Poff, gap voltage, and peak current, respectively. For an optimal set of response variables,Poncan be specified as 30 µs,Poffas 30 µs, gap voltage as 6 V, and peak current as 14 A as process parameters for MRR and SR. The SEM images of the tool material and the workpiece material clearly demonstrate a recast layer formed on the tool face and the machined surface of the workpiece. The optical microscopy analysis reveals a uniform distribution of SiCpand Grpparticles in the Al–0.7Fe–0.6Si–0.375Cr–0.25Zn matrix. In addition to recast layers and machined surfaces, EDS analysis reveals the deposition of tool material on the surface of the workpiece. The composites fabricated may replace materials in many of these applications where “friction” is a significant factor.

Enhancing EDM performance characteristics of Inconel 625 superalloy using response surface methodology and ANFIS integrated approach

The objective of the present work is to find out the optimal level as well as the influence of the electrical discharge machining (EDM) process on material removal rate (MRR), surface roughness (SR), and tool wear rate (TWR). The machining is carried out on Inconel 625 superalloy by using a copper tool electrode in a kerosene submerged medium. The optimum level of input parameters such as current, pulse on time (Ton), pulse off time (Toff), and gap voltage are calculated to achieve maximum MRR and minimum SR and TWR. L27 orthogonal design is used to perform the experiments and optimum levels of MRR, SR and TWR have been obtained by the response surface methodology (RSM) and adaptive network-based fuzzy inference system (ANFIS) approach. Analysis of variance (ANOVA) is used to identify the significant and non-significant parameters. For implementing, RSM methodology, the Design Expert 13 software was used for the optimization and modelling of the experimental data. MATLAB R20b software was utilized for developing the ANFIS model. It is found that the pulse on time (Ton) and pulse off time (Toff) are the significant parameters for MRR and SR and more wire wear increases as the value of pulse on time (Ton) increases. The obtained quadratic RSM model is found significant for MRR, SR, and TWR. Similarly, ANFIS proved to be accurate for the optimization of output responses with model accuracy percentages noted as 95.55% for MRR, 90.35% for TWR, and 97.82% for SR. The obtained results show that the predicted values and experimental values are very well suited for the proposed objective. The obtained data can be utilized for the EDM industry during the machining of Inconel alloy.

Discriminant Analysis-based Parametric Study of an Electrical Discharge Machining Process

This paper deals with the application of discriminant analysis in an electrical discharge machining (EDM) process to determine the comparative contribution of each of its input parameters on the measured responses. It also identifies the most significant EDM process parameters influencing those responses. For this process, voltage, current, pulse-on time and pulse-off time are considered as the input parameters, whereas, material removal rate, electrode wear rate and surface roughness are the responses. Based on the past and simulated experimental data, both simultaneous and step-wise estimations are carried out for each of the three responses showing the relationships between the EDM process parameters and the considered responses. It is observed that in both these estimations, pulse-off time, current and pulse-on time respectively evolve out as the most significant parameters for material removal rate, electrode wear rate and surface roughness. Step-wise estimation identifies voltage as the least significant input parameter for all these responses. The developed discriminant functions, which can also help in predicting the responses, are finally cross-validated.

Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni55.8Ti Shape Memory Alloy.

In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi’s L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm3/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions.

A study on machining performance of wire electric discharge grinding (WEDG) process during machining of tungsten alloy micro-tools

In this experimental study, tungsten alloy micro-tools of high aspect ratio are machined through wire electric discharge grinding (WEDG) method using the step machining technique on a hybrid micro-electro-discharge machine to achieve high productivity and finish. Tungsten alloy micro-tools are widely used in the fabrication of miniature products. The precise and economical machining of the tungsten material with an optimum level of accuracy and surface properties is a challenge because of its exceptional characteristics like hot hardness, high strength, low wear, and corrosion rate. The material removal rate (MRR) and surface roughness (Ra) responses are studied during machining of micro-tools, selecting the variability of applied voltage, circuit capacitance, and spindle speed during machining for a constant-aspect-ratio micro-tool. Both voltage and capacitance are found to be influencing parameters for MRR and surface roughness. Higher voltage and capacitance values tend to increase MRR and roughness values on the machined micro-tool. Further, the parametric conditions are also identified for higher MRRs and lower surface roughness (Ra) values after employing the hybrid Taguchi design of experimentation methodology. Surface topography of the machined surface studied by energy-dispersive spectroscopy (EDS) reveals the presence of pyrolytic carbon and oxygen elements. Field emission scanning electron microscopy (FESEM) images show a little waviness across the machined surface. A mathematical model is developed to evaluate the correlation among governing parameters for machining response and validated by confirmation experiments. The machining procedure can be further developed to fabricate more predominant fluted micro-tools for difficult-to-machine tungsten material.

Investigations and optimization of rotary tool micro-USM process for fabrication of microchannels

This article reports on investigations on process parameters of rotary tool micro-USM during machining of glass. Abrasive size, tool rotation speed, concentration, and power rating were the selected process parameters investigated in this study. The material removal rate (MRR) was chosen as response characteristics. Optimal parametric combination for maximum MRR was also determined by adopting Taguchi’s methodology in this investigation. The results obtained from analysis of variance revealed that tool rotation speed followed by abrasive size and concentration with a percentage contribution of 46.37%, 31.78% and 5.55% respectively were the significant parameters for MRR. The MRR increased with increasing the tool rotation speed and concentration and decreasing the abrasive mesh size. The maximum MRR was obtained at lower level of abrasive mesh size and higher level of rotation speed and concentration of abrasive slurry.

Machinability of SUPERNI-800 during PMEDM using the Taguchi method

Abstract EDM is the most popular machining method used in industry to machine hard metals and alloys and many researchers try to explore the applications of EDM process. They are trying to optimize the machining characteristics of difficult to machine materials from the literature reviewed, it is observed that considerable research work has been done on various aspects of electrical discharge machining and abrasive EDM of Inconel 718 and Titanium-based alloy. But less literature is available in the study EDM of Superni-800 is a versatile alloy that is widely used for nuclear industries, petrochemical industries, furnace equipment, etc. There is a need to investigate the machining of this material with different powders by varying different machining parameters such. In the current test study, blind holes were machined using BM-processed copper-titanium electrodes and a conventional copper electrode during the discharge of the Superni-800. The test design and variation analysis were implemented to understand the effects, contribution, importance, and optimal mechanical configuration, i.e. polarity, peak current, of the process investigation., Separation voltage in electrode type, pulse time, and material removal rate (MRR) and wear rate (WR). Significant parameters affecting the decisiveness of the machine were explored and the optimal size of the combination of machine parameters for MRR and WR was determined. A K-D powder metal has been found to give higher MRR and WR with a processed electrode, resulting in better mechanical performance than the Superni-800. The results were verified by a confirmatory test.

Parametric optimization of wire electro discharge machining of Inconel 718 using Taguchi’s methodology

Abstract In this demanding world of advanced technologies, the Wire Electro Discharge Machining (WEDM) has evolved as one of the promising methods for efficient machining of advanced materials (super alloys, ceramics and composites). WEDM, an important advanced machining process meets the demands of precise complex geometry and effective stock removal. The work material chosen for the present study is Nickel based age-hardenable austenitic super alloy Inconel 718, having high strength, better oxidation-corrosion resistance, and high temperature resistance. Inconel 718 finds widespread application in aerospace, marine, automobile industries, nuclear power, and oilfield applications. The present study deals with the experimental analysis and parametric optimization of slitting Inconel 718 with brass wire using WEDM. The objective of the proposed work is to optimize the process parameters namely Pulse-on time (TON), Open Circuit Voltage (OV), Wire Tension (WT) and Wire Feed Rate (WFR). The required experiments were conducted using Taguchi’s Orthogonal Array [L9 (34)] to predict optimal parametric settings for the responses namely Surface Roughness (SR) and Material Removal Rate (MRR). The results of ANOVA show that TON has highest influence on MRR followed by OV, WFR and WT. TON has highest influence on SR followed by WFR, WT and OV. When OV or TON increases then MRR also increases. When TON or OV or WFR decreases then SR also decreases. The optimal parameter for MRR is OV-60 V, TON-0.75 µs, WFR-1 m/min, WT-1700 g and for SR is OV-50 V, TON-0.25 µs, WFR-1 m/min and WT-1100 g.

Effect of process parameters on centre lathe of EN8 steel in turning process

Good surface finish and better material removal rate are desired for the proper functioning of produced parts. It was seen that the desired surface roughness and material removal rate were not obtained consistently in turning of EN 8 steel applications. These higher values of surface roughness results in rework and increasing cost, hence the main objective is optimization of surface roughness and material removal rate. A general optimization of surface roughness and material removal rate are necessary for the most of manufacturing industry. The surface quality and material removal rate are influenced by cutting speed, feed rate and depth of cut and many other parameters. In this experimental work optimum process parameters and the effect of machining parameters like spindle speed, feed and depth of cut on material removal rate and surface roughness are investigated. An L9 orthogonal array (mixed level design), analysis of variance (ANOVA) and the signal to noise (S/N) ratio are used in this study. Mixed levels of machining parameters are used and experiments are done on conventional lathe machine. EN8 steel material is used for manufacturing of shafts, studs, keys, general purpose axles etc. The most significant parameters for material removal rate are depth of cut, speed and least significant factor for MRR is Feed. For surface roughness speed, depth of cut are the most significant parameters and least significant factor is feed rate.

Investigations on morphology and material removal rate of various MMCs using CO2 laser technique

The efficient machining of metal matrix composite becomes important factor with respect to the high strength and hardness properties. The presented research work observes the influence of CO2 laser processing parameters on various metal matrix composite materials in terms of material removal rate (MRR). A comparison has been explored between various composite materials such as silicon carbide (SiC), aluminum oxide (Al2O3), and zirconium oxide (ZrO2) metal matrix composites for MRR. This results in novel research in context of MRR that examines the microstructure of machined work material. In the present study, to determine MRR various weighing evaluations and topography of machined surface were studied. The results indicated that the focal position, cutting speed and reinforced particles are the significant parameters for MRR. The morphological and metallurgical changes such as formation of recast layer have been analyzed using scanning electron microscope. The chemical composition of machined work materials and formation of new compound have been identified using EDS/XRD technique.

Multi-objective parametric optimization for high surface quality and process efficiency in micro-grinding

In this study, for the selection of maximum material removal rate and minimum surface roughness [Formula: see text] in micro-grinding of aluminum alloy through multi-response optimization, two optimization approaches are proposed based on statistical analysis and genetic algorithm. The statistical analysis–based approach applies response surface methodology according to the analysis of variance to propose a mathematical model for [Formula: see text]. In addition, the individual desirability of material removal rate, [Formula: see text], and the global desirability function are calculated, and the inverse analysis is conducted to locate input setting giving maximum desirability function. The genetic algorithm–based approach uses the improved multi-objective particle swarm optimization with the experimental data trained by support vector machine. To demonstrate that the material microstructure is a significant parameter for material removal rate and [Formula: see text], the models with and without Taylor factor consideration are developed and compared. The optimized results achieved from both response surface methodology and improved multi-objective particle swarm optimization demonstrate that the consideration of Taylor factor can significantly improve the optimization process to achieve the maximum material removal rate and minimum [Formula: see text].

Optimization of Turning Parameters for Material Removal Rate of EN-36 Alloy Steel by Box Behnken Method under RSM

Optimization of Turning Parameters for Material Removal Rate of EN-36 Alloy Steel by Box Behnken Method under RSM