Combination Of Machining Parameters Research Articles

Development of optimum performance model during spark-eroded processing of rheocasted Al6061/Cenosphere MMCs

ABSTRACT In the present study, an improved wettability casting route was used to prepare lightweight structural materials. Using the compo casting process, an Al6061 alloy was created with cenosphere particles at various weight percentages (2, 4, 6, and 8 wt%). The molten aluminium slurry incorporated hollow alumina silicate particulates. X-ray diffraction (XRD) analysis of the aluminium matrix composites (AMCs) confirmed the presence of cenospheres without any intermetallic components. Further processing of the metal matrix composites (MMCs) was conducted using electric discharge machining (EDM) with a circular copper electrode. The EDM process parameters were controlled to measure response variables such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The effects of different combinations of machining parameters – including pulse current, pulse-on time, reinforcement percentage, and flushing pressure – on these performance characteristics were observed. Experimental trials were designed using the face-centred central composite design (CCD) of the response surface methodology (RSM). Optimal process variable conditions led to significant improvements in surface finish. Additionally, an artificial neural network (ANN) model was used to determine the best conditions for EDM processing. The ANN model demonstrated a strong correlation coefficient of 0.99 between the predicted and experimental values of MRR, TWR, and SR.

Multi-Criteria Optimization of Al2O3-ZrO2(Y2O3)-based Self-lubricating Composite Cutting Tools for Increasing Shear Angle, and Wear Resistance in Machining Processes

Abstract In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt.% and CaF2 at 5Wt.% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

Optimization of Process Parameters of Wire Electric Discharge Machining Process for Machining AISI A2 Tool Steel

Investigation of the Influence of Machining Parameters and Surface Roughness on the Wettability of the Al6082 Surfaces Produced with WEDM.

Electrical Discharge Machining (EDM) is a non-conventional machining technique, capable of processing any kind of conductive material. Recently, it has been successfully utilized for producing hydrophobic characteristics in inherently hydrophilic metallic materials. In this work, Wire Electrical Discharge Machining (WEDM) was utilized for producing hydrophobic characteristics on the surface of the aluminum alloy 6082, and various parameters that can affect wettability were investigated. Adopting an orthogonal Taguchi approach, the effects of the process parameter values of peak current, pulse-on time, and gap voltage on the contact angles of the machined surfaces were investigated. After machining, all samples were observed to have obtained hydrophobic properties, reaching contact angles up to 132°. The peak current was identified as the most influential parameter regarding the contact angle, while the gap voltage was the less influential parameter. A contact angle variation of 30° was observed throughout different combinations of machining parameters. Each combination of the machining parameters resulted in a distinct surface morphology. The samples with moderate roughness values (3.4 μm > Sa > 5.7 μm) were found to be more hydrophobic than the samples with high or low values, where the contact angle was measured under 115°. In addition, the finite element modeling of the experimental setup, with parametric surfaces of uniform random and Perlin noise types of roughness, was implemented. Time dependent simulations coupling phase field and laminar flow for the modelingof the wetting of surfaces with different surface roughness characteristics showed that an increase in the Sa roughness and total wetted area can lead to an increase in the contact angle. The combination of experimental and computational results suggests that the complexity of the wettability outcomes of aluminum alloy surfaces processed with WEDM lies in the interplay between variations of the surface chemical composition, roughness, micro/nano morphology, and the surface capability of forming a composite air/water interface.

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

Now-a-days, metal matrix composites based on pure aluminium are widely used as a replacement for a wide range of applications thanks to their high strength-to-weight ratio, ductility, and thermal conductivity. Micron-sized (10−3 mm) borosilicate (mixer of Silica (SiO2) and Boron Oxide (B2O3)) glass particles were used to create a dispersion in an aluminium matrix composite (Al7075- borosilicate glass MMC) utilizing a stir casting technique while in a liquid condition. The present study examines the effect of borosilicate glass particle content (0, 3, 6, and 9 wt%) and changes in sliding velocity (0.5, 1, 1.5, and 2 m s−1) on the wear behaviour of a composite rubbing against an EN31 steel disc at a constant load of 20N and a sliding distance of 1000 m. Analysis of wear showed that the presence of borosilicate glass increased the wear rate and countered the trend shown in the coefficient of friction. In a similar vein, the sliding velocity of the counter plate rotated faster resulted in a higher coefficient of friction and a higher wear rate. A reliable mathematical model is developed to find the best combination of machining parameters for Al7075-borosilicate glass metal matrix composite (MMC). This model will consider important performance measures like surface smoothness and material removal rate. The authors will also use a sophisticated statistical method called the Taguchi L9 orthogonal array design of experiments (DOE) to understand how different machining settings affect how easily the composite can be machined. By analyzing the results from the DOE, it can optimize the machining parameters for efficient and cost-effective processing. This work can lead to manufacturing innovation in the future.

Optimization Method of Sheet Metal Laser Cutting Process Parameters under Heat Influence

To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model Tpr,t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model Tpr,t, the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments.

Investigation on the optimal ultra-precision machining of nitriding mold steel by in-situ laser assisted diamond cutting

S136 (STAVAX ESR) mold steel is an advanced material for the optical mold industry. The precisely machined surface quality is essential for guaranteeing the excellent performance of molds. Diamond cutting is a high-efficiency machining technique in optical mold manufacturing. However, the chemical wear of diamond tool is a huge challenge in precisely machining steel mold due to the reaction between atoms C and Fe. In this study, the hybrid machining technique which combined the in-situ laser assisted diamond cutting (LADC) and plasma nitriding was used to machine S136 mold steel. Furthermore, the impact of the various machining conditions on the machined workpiece surface roughness Sa was comprehensively investigated by the Taguchi method (TM) and Response surface methodology (RSM). The experimental results of material characterization indicated that a 90 μm nitride layer was generated on the workpiece surface by plasma nitriding. Analysis of variance (ANOVA) results demonstrated that the feed rate and laser power had an important impact on the machined surface roughness Sa. According to the RSM, a precise mathematical model was developed which is explored by experimental investigations. The results showed that the optimal machining parameter combination by RSM generated the better surface quality of 6.393 nm in in-situ LADC of nitriding steel. This study provides a reference for mold steel ultra-precision machining technique and parameter optimization method for surface integrity.

Parameters optimization for chatter-free milling tool path based on spindle speed variation method

Existing methods orienting for optimizing cutting parameters in milling process with spindle speed variation (SSV) only optimally selected the axial depth of cut and only considered the acceleration constraint of spindle. This article proposes a systematic optimization method to obtain the best combination of multiple machining parameters (axial and radial depths of cut) and SSV’s parameters (the ratio of variation amplitude and the ratio of variation frequency) so that the chatter-free milling tool path can be shortened. Both acceleration and jerk constraints of spindle are considered. A C3 continuous variable feedrate scheduling algorithm is used to eliminate the influence of spindle speed variation on feed per tooth, which is thus not required to treat as an optimization variable. The machining parameters of each iterative step are updated by following that the number of axial and radial cutting layers could be decreased. Discrete optimization method is combined with chatter stability analysis to solve the problem. A series of milling experiments verify that the proposed method can achieve the shortest tool path with chatter-free condition.

Orthogonal experimental study on the influence of machining parameters on flat lapping of sapphire substrate

Sapphire is extensively utilized in the optical, aerospace, and civil electronic industries due to its favorable optical, physical, and chemical characteristics. To enhance the efficiency and quality of sapphire lapping, an orthogonal experiment was conducted on a single-side sapphire substrate using a ceramic lapping plate. The study examined the impact of lapping plate material, lapping pressure, lapping speed, and abrasive particle size on the surface roughness, profile, and removal rate. By analyzing the signal-to-noise ratio and variance of machining parameters, the influence rule and influence of the weight of machining parameters on machining results were obtained. The results of the experiment demonstrate that the material removal rate of sapphire was positively affected by an increase in lapping pressure, speed, and abrasive particle size. Moreover, the removal rate of the SiC lapping plate was the highest among the experimental materials. The roughness of the sapphire surface decreased with increasing lapping pressure, speed, and abrasive particle size, while the SiC plate had the lowest surface roughness. The profile tolerance of sapphire diminished as the lapping pressure, lapping speed, and the abrasive particle size increased. Additionally, the ZrO2 lapping plate exhibited the most minor profile tolerance. The size of the abrasive particle significantly impacted the material removal rate, with a specific gravity exceeding 70%. Similarly, the lapping pressure had a significant effect on both the surface roughness and the profile tolerance. The ideal machining parameter combination comprised an abrasive particle size of 10 µm, a lapping pressure of 22 785.0 Pa, a lapping speed of 60 rpm, and a lapping plate of SiC. Under optimal machining conditions, sapphire exhibited a material removal rate of 0.65 µm/h, a surface roughness of 0.0920 µm, and a profile tolerance of 2.0915 µm after 20 min of lapping. This demonstrated that the lapping process enables highly efficient and high-quality machining of sapphire substrates.

Multi-attribute optimization of AWJM parameters on hybrid kevlar/SS wire mesh epoxy composites

ABSTRACT Fiber/metal hybrid composites are perceived as superior materials for applications in machine tools because of their enhanced machinability, lightweight nature, and vibration-damping capabilities. The application of AWJM to hybrid composites introduces challenges, including kerf convergence ratio and surface irregularity issues throughout the machining process. This study is all about the AWJM of a Kevlar fiber/SS wire mesh epoxy composite with silicon carbide filler particles and garnet as the abrasive material. The study aims to investigate the effects of varying machining conditions, such as cutting velocity, abrasive performance rate, and nozzle tip distance, and to determine the most effective machining parameters using TOPSIS technique. To achieve this, a total of 27 different experimental trials were conducted, and two key attributes, namely kerf convergence ratio and surface irregularity, were recorded. This approach empowers all-encompassing evaluation and comparison of diverse machining parameter combinations, thus expediting the selection of the most fitting parameters to attain desired machining outcomes.

Study on tool wear and optimization of machining parameters in laser-assisted fast tool servo machining of glass-ceramic

Glass-ceramic is difficult to be ultra precision machined due to its high hardness and brittleness. Laser-assisted fast tool servo machining (LAFTSM) of glass-ceramic optical free-form surface was carried out with tool wear as the characteristic value to study the machining quality of glass-ceramic. Orthogonal experiments on LAFTSM were conducted using the Taguchi method (TM). The range of tool wear reduction obtained by comparing laser-assisted machining (LAM) with fast tool servo (FTS) machining is 48.83%–64.12%. The order of contribution of each machining parameter obtained through variance analysis to the reduction of tool wear is: spindle speed > laser power > feed rate > piezoelectric frequency. The optimal combination of machining parameters that can minimize tool wear obtained through signal-to-noise ratio (S/N) analysis is: spindle speed 55 rpm, feed rate 0.01 mm/rev, piezoelectric frequency 8 Hz, laser power 75 W. Artificial neural network (ANN) and genetic algorithm (GA) were used to fit and optimize the machining parameters and experimental results in TM orthogonal experiments. The fitting values of ANN are highly consistent with the orthogonal experimental results. The optimal combination of machining parameters obtained after GA optimization analysis is: spindle speed 50 rpm, feed rate 0.015 mm/rev, piezoelectric frequency 4 Hz, laser power 75 W. Experiments were conducted using the optimal combination of machining parameters of TM and ANN, the results showed that ANN performs better than TM in predicting minimum tool wear and optimizing machining parameters. This study provides a reference for LAFTSM and the research methods of tool wear.

Tensile Test Optimization Using the Design of Experiment and Soft Computing

The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.

Expert opinion based optimziation of CNC machining parameters for Al-CNT composites using fuzzy TOPSIS method

Machinability of Al-CNT composites at optimum conditions of machining parameters saves a lot of time, improves quality of machined components at minimum possible cost of machining. In conventional methods, controlled experimentation is performed. In order to obtain best machining parameters, optimization methods are applied but they are inadequate in many situations. Hence, an attempt was made in this work to collect subjective data for response parameters such as, quality, cost, and time, from selected experts in this field for various combinations of input process parameters of CNC milling machine for the case of Al-CNT composites with high-speed steel as tool material. 27 sets of experiments were devised for various combinations of machining parameters systematically by taking cutting speed (in rpm), feed, step over ratio, and depth of cut each at 3 levels. Two experts were invited for collection of data for the response parameters and three experts were involved for giving weightages for the response parameters. Fuzzy trapezoidal membership functions were made use of, for representing fuzzy set and fuzzy Technique and TOPSIS method was applied for finding the best solution. Results revealed that best solution was obtained for a spindle speed of 3000 rpm, feed of 150 mm rev−1, step-over ratio of 0.5, and depth of cut of 0.2 mm.

PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING WHILE MACHINING AN Al6061 ALLOY USING CBN INSERTS WITH DIFFERENT CUTTING-EDGE GEOMETRIES

The present work aims to investigate the effect of cutting-tool edge geometry on cutting force and surface finish while machining an Al6061 alloy under different conditions. A series of experiments was performed with a custom-fabricated cutting insert of a chamfered edge to observe the effect of feed rate and depth of cut on the cutting forces and surface finish. The results showed that varying the cutting-edge geometry has a significant effect on controlling the cutting forces. Also, as the feed and depth of cut were reduced (at high cutting speeds), the surface roughness was observed to reduce with the geometry effect. Furthermore, in the present work validation of the experimental results were also performed based on a multi-criteria decision-making method called Grey Relational Analysis (GRA). The weighted GRA predicted the optimal combination of machining parameters for two different cutting-tool inserts. Finally, the obtained optimal results were compared with the predicted and experimental values in terms of weighted GRG. The result shows that there was no significant improvement while using the standard cutting tool, whereas a net improvement of 16.9 % was observed while using the chamfered cutting tool for machining the Al 6061 alloy.

Experimental investigation on cutting force and machining parameters optimization in in-situ laser-assisted machining of glass–ceramic

Glass-ceramic is a typical hard and brittle material that is difficult to use for ultra precision machining. In order to improve the machining quality of glass–ceramic, orthogonal experiments on in-situ laser-assisted machining (LAM) of glass–ceramic were carried out with cutting force as the characteristic value. The maximum reduction in the resultant force of cutting force in in-situ LAM was 56.13%. The contribution order of each machining parameter to reducing cutting force is: spindle speed > laser power > feed speed > cutting depth. Response surface methodology (RSM) was used to conduct experiments and establish a regression model for predicting cutting force. The influence law of four machining parameters on cutting force was studied through three-dimensional response surface. The optimal combination of machining parameters to minimize cutting force obtained through RSM optimization is: spindle speed 400.51 rpm, feed speed 0.03 mm/rev, cutting depth 18.91 μm, laser power 74.78 W. The RSM experiments were trained, predicted, and optimized using artificial neural network (ANN) and genetic algorithms (GA). The fitting values of the ANN model are highly consistent with the experimental values of RSM. The optimal machining parameter combination obtained through GA optimization is: spindle speed 400 rpm, feed speed 0.01 mm/rev, cutting depth 20 μm, laser power 75 W. Experiments were conducted using the optimal machining parameters of RSM and ANN respectively, and the results showed that ANN is more ideal for optimizing machining parameters and predicting minimum cutting force. This study provides guidance and reference for glass–ceramic in-situ LAM and parameter optimization method for cutting force.

Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm

Surface finish and temperature rise are the crucial machining outcomes since it determines the quality of the machining and the tool life. During machining operations, choosing optimal machining parameters is critical since it affects the machining outcome. In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. Cutting depth, cutting speed, and feed rate are the machining variables that are used in the process of optimization. From the results, it shows that the minimum temperature rise is 243.333 ℃ with a surface roughness of 1.975 μm during machining of 20 hardness. It also observed that the hardness of the material significantly affects the surface roughness and temperature rise. The outcome shows that as the hardness of the material is increasing the temperature is increasing while the surface roughness is decreasing. This research also revealed that using a MOGA to optimize multi-objective replies produces positive outcomes.

Effects of Machining Parameters of C45 Steel Applying Vegetable Lubricant with Minimum Quantity Cooling Lubrication (MQCL)

One of the most significant performance indicators for measuring the machinability of materials is tool wear and surface roughness. Choosing the best combination of cutting parameters can help reduce production costs, which is what the manufacturing industry is interested in. At the same time, industries are always looking for an alternative to conventional flood cooling since its use creates an environmental burden and health concerns for the operators. Therefore, vegetable oil-based minimum quantity cooling lubrication (MQCL) is considered a cutting environment. Sunflower oil is utilized as base fluid in MQCL and applied to the cutting zone through a nozzle. The turning experiments are conducted on C45 material which is widely used in various industrial applications, including numerous automotive components. Since flood cooling is widely utilized in machining C45, it is the present-day need to assess alternative cooling and lubricating approaches to avoid the adverse effects of flood cooling. The Taguchi method was used in the present work to minimize surface roughness and tool wear. L9 orthogonal array was constructed, and experiments were performed on C45 steel using coated carbide cutting tools. The statistical approach is utilized to evaluate the effect of cutting parameters on output responses. The optimal cutting settings for cutting speed, feed, and depth of cut to minimize surface roughness are 100 m/min, 0.18 mm/rev, 0.150 mm, and 80 m/min, 0.18 mm/rev, and 0.150 mm for tool wear. According to the findings, cutting speed, feed rate, and depth of cut varied surface roughness by 1.9%, 78.3%, and 14.04%, and tool wear by around 43.8%, 37.9%, and 6.3%, respectively. The outcomes can be useful to metal-cutting industries to identify the combination of machining parameters with vegetable oil-based MQCL.

Effect of Copper Percentage in Copper Tungsten Electrode on Ti6Al4V by Electrical Discharge Machining

The aim of this study was to identify the optimal parameters for electrical discharge machining of Ti6Al4V titanium alloy using copper tungsten electrodes with tungsten compositions of 70% and 80%, respectively. The experiment involved varying current, pulse on time, and duty factor were determined using the Taguchi experimental method L-18 orthogonal arrays. The level of importance of each machining parameter on material removal rate (MRR) was determined using analysis of variance (ANOVA), and the optimal machining parameter combination was obtained using the analysis of signal-to-noise (S/N) ratio. The results showed that a Cu30/W70% electrode performed the best in terms of MRR, electrode wear, and surface roughness. The optimal machining conditions for achieving maximum MRR were found to be a current of 30 A, a pulse on time of 50 μs, and a duty factor of 40%.

Construct the Optimum Process Model for Transistor Gaskets with Six-Sigma DMAIC

The improvement process of six-sigma DMAIC refers to the process that the industry performs to enhance process quality via the following five procedures: (1) define, (2) measure, (3) analyze, (4) improve, and (5) control. It is a common tool used in the industry to ameliorate and enhance process quality. In addition, the process capability index is a tool that the industry most frequently uses to measure process quality. Though many studies have discussed six-sigma methods, none of the specific theoretical models could be provided as a reference for the above five DMAIC improvement steps, making it difficult to control the effect. In order to solve this problem, this paper developed the multi-characteristic process capability analysis chart, MPCAC, using the process capability index and applied the method of statistical inference as a tool promoting define, measure, and analyze in the improvement process of six-sigma DMAIC. Additionally, the testing of the orthogonal table in the Taguchi method can efficiently assist process engineers in finding the optimum combination of machining parameters to improve or boost the process quality level of the quality characteristics by reducing the number and the cost of experiments. Therefore, this paper then adopted the testing of the orthogonal table in the Taguchi method as a tool for process improvement in the fourth step. Finally, according to the optimal combination of machining parameters improved in the fourth step, a standard operating procedure for transistor gaskets was established as a tool for process control in the fifth step.

Sustainable Dry Machining of Stainless Steel with Microwave-Treated Tungsten Carbide Cutting Tools.

This paper presents a research investigation conducted on the turning of stainless steel 316 material under a dry environment using microwave-treated cutting tool inserts. Plain tungsten carbide WC tool inserts were exposed to microwave treatment for enhancement of their performance characteristics. It was found that a 20-min microwave treatment resulted in the best tool hardness and metallurgical characteristics. These tool inserts have been used to machine SS 316 material following the Taguchi L9 design of experimental techniques. A total of eighteen experiments have been conducted by varying three main machining parameters, i.e., cutting speed, feed rate, and depth of cut, at three levels per parameter. It has been found that tool flank wear increased with all three parameters and surface roughness decreased. At the longest dept of cut, surface roughness increased. An abrasion wear mechanism was found on the tool flank face at a high machining speed and adhesion at low speed. Chips with a helical shape and low serrations have been investigated. Turning SS 316 at optimum machining parameters of 170 m/min cutting speed, 0.2 mm/rev feed rate, and 1 mm depth of cut, as obtained by the multiperformance optimization technique grey relational analysis, resulted in the best values of all machinability indicators: 242.21 µm tool flank wear, 3.81 µm mean roughness depth, and 34,000 mm3/min material removal rate, at a single parameter setting. In terms of research achievements, the percentage reduction in surface roughness is approximately 30% and represents an almost ten-fold improvement in the material removal rate. The combination of machining parameters of 70 m/min cutting speed, 0.1 mm/rev feed rate, and 0.5 mm depth of cut is optimum for the lowest value of tool flank wear when considered for single parameter optimization.