Turning SKD 11 Hardened Steel: An Experimental Study of Surface Roughness and Material Removal Rate Using Taguchi Method

. Heat-treated steel is widely used in industrial applications due to its high strength and other desirable mechanical qualities. Grinding, which requires a lot of power and is expensive, is typically used to harden machining. In recent times, hard machining has emerged as a viable alternative to grind in select applications. In this investigation, turning operations with a carbide insert (CNMA 120408-KR3215) were carried out on SKD 11 (53 HRC) hardened steel. A total of nine machining tests were completed using the L 9 orthogonal array. Te response variables considered in this study were surface roughness (Ra) and material removal rate (MRR). Te analysis of the signal to noise ratio reveals that the optimal combination of cutting process parameters for achieving a desired surface roughness consists of a cutting speed of 119 m/min, a feed rate of 0.11mm/rev, and a depth of cut of 0.2 mm. Te contribution of each process parameter to the machining performance of the carbide tool-work piece combination is determined through the use of ANOVA. Depth of cut has the greatest impact (57.33%) to MRR, while feed rate has the highest contribution (82.15%) to Ra. Moreover, desirability function analysis (DFA) was conducted to optimize the multiple responses. DFA suggested that, to attain a satisfactory response to the output parameters, higher range of cutting speed, depth of cut, and lower range of feed rate are appreciable; therefore, the analytical fndings suggest that a cutting speed of 189m/min, feed rate of 0.11mm/rev, and a depth of cut of 0.5mm can induce a favorable Ra of 0.971 μ m and MRR of 10.248cm 3 /min. In hard machining, cutting speed has a bigger infuence on surface fnish than feed rate.


Introduction
In recent years, industrial sectors have been seeing the rise of novel materials such as alloys and super alloys due to the exceptional material qualities they possess; high-strength steel is frequently utilized in the fabrication of products for the aviation, aerospace, and high-tech equipment industries [1][2][3][4].However, the advent and extensive utilization of high-strength, heat-resistant, and difcult-to-cut materials has posed challenges to manufacturing operations [5,6].Te development of hard cutting techniques has been driven by the increasing demand for enhanced productivity, ability to respond to complicated parts, elimination of cutting fuids, achievement of excellent surface quality, and the reduction of production costs [7].Te issue of climate change has garnered signifcant interest from researchers and manufacturers worldwide, particularly in relation to the potential alternative option it ofers for many conventional fnal grinding procedures.Te machining of workpieces in these procedures involves the direct utilization of cutting edges that are geometrically specifed, with the typical hardness value falling within the range of 45-70 HRC [8,9].
Turning hardened steel presents a number of challenges, the most signifcant of which is the accomplishment of superior quality of the product.Tis may be measured in terms of dimensional accuracy, surface polish, and a high output rate and price [10,11].In addition, to guarantee a high level of precision and an excellent surface fnish on machined products, choosing the right cutting inserts is always a challenge.Numerous cutting tool advancements have made it possible to execute hard turning on hardened steels using CBN, ceramic, coated carbide, and coated ceramic inserts [12][13][14][15][16]. Due to their high melting point, excellent hardness, and resistance to wear, these inserts are able to withstand the high cutting temperature and cutting speed without failing.Polycrystalline cubic boron nitride (PCBN) and coated mixed ceramic tools have enabled industries and researchers to conduct dry turning operations for hard materials ranging from 50 to 65 HRC to achieve the required quality of machined parts [17][18][19].While these advanced cutting tools exhibit high precision cutting capabilities, the manufacturing cost has correspondingly increased [20].In the feld of hard turning, researchers are currently focused on the primary goal of reducing machining costs and enhancing productivity through the reduction of tool wear rates.As a result, there is a growing emphasis on the implementation of environmentally conscious lubrication systems in the context of hard turning [21,22].Several researchers have indicated that new cutting fuid techniques such as minimum quantity lubrication (MQL), high pressure cutting fuid application, and spray impingement cooling are considered to be more advanced compared to traditional methods like fooded cooling and dry surroundings [22][23][24][25].Te lack of lubricant/ coolant during high-speed turning of heat-treated steel results in a chip disposal issue.Tis issue leads to increased friction at the tool-chip and tool-work interfaces, thus causing faster attrition and tool wear [26].While a plentiful supply of coolant proved benefcial in reducing friction, it also resulted in increased machining costs and gave rise to certain adverse health efects for individuals [27,28].
Te development of surface roughness and material removal rate is infuenced not only by the cutting inserts but also by various uncontrollable factors [29,30].Previous studies have demonstrated a strong correlation between the cutting parameters of the machining process and both the surface fnish quality and the amount of material removed from machine workpieces [31,32].Te cutting parameters encompass several factors such as the cutting speed, feed rate, depth of cut, tool geometry, and the material properties of both the tool and the workpiece [33].Moreover, the diverse permutations of these factors might result in highly distinctive outcomes with regards to the quality of the machined surface and the pace at which the material is removed.Te outcomes are infuenced by the objective of the machining process and the specifc cutting tool employed.Nevertheless, it poses a signifcant challenge to ascertain the most ideal combination that efectively decreases the roughness value while simultaneously maximizing the rate of material removal [34,35].Asilturk and Neseli [36] conducted a study to identify the optimal machining parameters for achieving improved surface roughness in the dry turning process of AISI 304 steel.Tey utilized a coated carbide insert and employed a response surface methodology to develop a mathematical model for predicting the desired outcome.Teir fndings indicated that the feed rate was the most infuential factor in achieving the desired surface roughness.Te study conducted by Tamizharasan and Senthilkumar aimed to investigate the impact of diferent cutting tool geometries on surface roughness and material removal rate (MRR).Te researchers employed Taguchi's technique and analysis of variance (ANOVA) to assess the data [37].In another study, Kopac et al. conducted an investigation to identify the most favorable parameters for achieving the desired surface roughness during the turning process of C15 E4 steel [38].Tey achieved this by manipulating several factors, including the cutting speed, tool and workpiece material, depth of cut, and number of cuts.Additionally, they utilized coated inserts in their experimental setup.Srithar et al. examined how machining parameters impact surface quality when turning AISI D2 tool steel with a polycrystalline cubic boron nitride insert.Te workpiece was heat-treated to 64 HRC.Te study found that feed rate signifcantly impacts surface roughness.As feed rate and cutting depth rise, surface roughness increases.Cutting speed signifcantly impacts surface quality.Increasing cutting speed reduces surface roughness [39].
Te investigation of surface roughness and material removal rate in the turning process can be accomplished by employing appropriate models that establish a relationship between the process parameters and the resulting outcome [40][41][42][43].Inconel 718 dry turning characteristics were statistically modeled and optimized by Ramanujam et al. [44].Turning experiments were conducted at varying degrees of cutting parameters using Taguchi's L 9 orthogonal array to examine performance indicators such cutting force, surface roughness, and tool wear.Te efcacy of Taguchi's optimization method was demonstrated by conducting confrmation experiments on the optimal cutting settings.In another work, to determine its machinability, Dutta and Reddy turned a newly created aluminum-manganese (AM) series Mg alloy [45].In order to optimize the feed, speed, and depth of cut (DOC) of the turning process, the Taguchi method using a L 9 array has been implemented.According to the derived statistical parameters, DOC has the greatest impact on cutting force, whereas feed has the most impact on roughness.Using an experimental, modeling, and optimization approach, Kumar et al. performed the turning of JIS S45C hardened structural steel with a multilayered (TiN-TiCN-Al 2 O 3 -TiN) CVD-coated carbide insert.Tey discovered that in the machining of medium carbon low alloy steel, to improve the cutting performance of multifacetedcoated carbide tool to a greater extent [46].Nalbant et al. utilized the Taguchi technique to discover the best cutting settings for surface roughness in turning AISI 1030 carbon steel.Tree cutting parameters insert radius, feed rate, and depth of cut optimized surface roughness [47].Tey found that insert radius and feed rate are the key adjustable elements that afect surface roughness.Furthermore, Taylor et al. explored the optimization of a turning process for hardened steel by utilizing the design of experiments (DOE) approach with an orthogonal array to predict the surface roughness [48].In addition to this, they make use of the analysis of variance (ANOVA) to discover which parameters had the most signifcant impact on the turning process.

2
Advances in Materials Science and Engineering Furthermore, in 2012, Tonk and Ratol determined the parametric efects for turning EN31 alloy steel by applying Taguchi's robust design technique and found that feed rate and depth of cut afect thrust force and feed force, respectively [49].Taguchi method employs highly fractionated factorial designs in addition to orthogonal arrays.Tis makes it easier for the experimenter to study the whole experimental region of interest, and in addition to that, the Taguchi technique leads to a lower total number of runs compared to traditional methods of experiment design [50].In addition to this, it is of the utmost importance to save expenses without compromising the quality of the output.Te measurement of surface roughness, which is a key property of surface quality, is often used in the process of evaluating the quality of the surface that has been machined [51].Kumbhar and Waghmare [52] utilized the Taguchi technique to evaluate the infuence of PVD TiAlN/TiN-coated carbide inserts on tool life and surface roughness in hardened EN31 alloy steel during dry turning.Tey studied machining parameter performance using L 9 orthogonal array, signal-to-noise ratio, and ANOVA and found that feed rate greatly afects surface roughness and tool life.Utilizing advanced optimization algorithms, which assist manufacturers in making informed decisions in the presence of multiple objectives that need to be satisfed, is one way to improve the application of hard-turning technology [8,53,54].Te fndings of previous studies clearly demonstrate that the selection of machining parameters in hard turning signifcantly afects both surface roughness and material removal rate.Furthermore, the machining process of hardened steel using a conventional tool such as carbide exhibits signifcant limitations.To overcome this limitation throughout the duration of this study, the researchers conducted a series of turning experiments utilizing a normal lathe machine.Te primary objective was to ascertain the optimal cutting parameters that yield the highest quality cut while employing a carbide tool for the purpose of cutting SKD11 under dry conditions to reduce cost.SKD 11 alloy steel is classifed as a tool steel with high carbon and chromium content.Following the process of heat treatment, SKD 11 alloy steel exhibits numerous notable characteristics, including commendable wear resistance, elevated hardness, and enhanced strength.SKD 11 steel is frequently employed as a material for stamping dies, plastic molds, and cold-work dies due to its favorable mechanical qualities.Nevertheless, the material has challenges in terms of workability, particularly following heat treatment.Hence, it holds great signifcance to conduct research on the efcient cutting techniques employed for SKD 11 steel.

Workpiece Material.
In the interest of research hardened steel according to the specifcations of the Japanese standard SKD 11 (JIS-G4404) was used into a workpiece with a cylindrical shape, measuring 59 millimeters in diameter and 400 millimeters in length.A noncoated carbide tool was employed to eliminate a coating of rust and scales from the surface of the workpiece before commencing the machining process, with the objective of achieving a workpiece diameter to length ratio of 1 : 4 [43].Tables 1 and 2 provide a comprehensive breakdown of the chemical constituents comprising SKD 11, as well as its corresponding physical properties.Te initial hardness of the workpiece was measured to be 20 HRC.Subsequently, a bulk hardening and tempering procedure was employed, resulting in an increase in the workpiece's hardness to 53 ± 1 HRC.Tis method not only enhanced the toughness of the workpiece but also mitigated its brittleness [57].
Te hardening procedures were conducted within the confnes of the heat treatment laboratory at the Bangladesh Industrial and Technical Assistance Center (BITAC), as depicted in Figure 1.Te CNMA 120408-KR 3215 carbide inserts were employed as the cutting tool for conducting all of the experiments.Te photographs depicting the chosen cutting tool can be observed in Figure 2. Carbide tools are widely regarded as the most readily accessible cutting tools within the category of hard cutting tools.In recent times carbide tools are gradually being substituted by ceramic and CBN tools in high-speed cutting scenarios due to their inferior performance.However, carbide tools still possess satisfactory wear resistance and strength, making them suitable for machining SK 11 Material [58,59].In addition, carbide tools possess a high degree of accessibility and beneft from a well-established manufacturing process, resulting in a comparatively lower cost when compared to alternative cutting tools.Carbide tools are deemed to be a highly favorable selection for the present investigation.

Taguchi Approach.
Te conventional methodologies for experimental design are excessively intricate and challenging to implement.Moreover, as the quantity of machining parameters escalates, a substantial quantity of trials must be executed.Te Taguchi method is a systematic approach utilized for the purpose of experimental design.Utilizing orthogonal arrays ofers signifcant benefts in reducing the number of required tests and mitigating the infuence of uncontrollable factors.Te utilization of the Taguchi technique ofers several notable advantages.First, it leads to a notable reduction in the duration required for conducting experiments.Additionally, it results in a significant decrease in the fnancial resources expended.Lastly, it facilitates the efcient identifcation of pertinent factors within a compressed timeframe [60,61].In order to satisfy this prerequisite, Taguchi employs a conventional orthogonal array for construction purposes.Furthermore, the selection of the signal-to-noise ratio, also referred to as the S/N ratio, is the preferred quality characteristic.Te signalto-noise ratio (S/N ratio) is utilized as a quantifable measure in lieu of the standard deviation due to the inverse relationship between the mean and standard deviation.
Indeed, it is worth noting that the target mean value has the potential to undergo changes during the course of process development.Te utilization of signal-to-noise ratio principles has demonstrated potential advantages in various applications, including the enhancement of measurement accuracy and the reduction of variability to improve overall quality.Te characteristics of the signal-to-noise ratio (S/N ratio) can be categorized into three distinct groups according to the mathematical equations.

Analysis of Variance (ANOVA).
ANOVA is a signifcant statistical technique that is utilized to assess the impact of a specifc input parameter in a series of tests carried out for the machining process.Additionally, it can be employed to evaluate the outcomes of these experiments [62].Additionally, this approach can be employed to enhance the machining parameters for turning operations with the aim of achieving optimal outcomes (source).Te construction of this expression is designed in a manner that symbolically represents the concept that any function with a high number of dimensions can be broken down into a subset of terms derived from the expansion.
where p stands for the number of inputs, f 0 is a constant (bias term), and the other terms on the right-hand side represent the univariate, bivariate, trivariate, etc., functional combinations of the input parameters.When carrying out an analysis of variance (ANOVA), it is important to take into account both the degrees of freedom and each sum of squares [63].Te measurement of the error variance is of utmost signifcance in ANOVA research involving tests with known errors.Obtained data are used to estimate F value.In an experiment, the amount of variation that can be attributed to each signifcant factor or interaction is expressed as a percentage contribution.Tis percentage contribution refects the relative strength of a factor or interaction to reduce variance.A signifcant part is played both by the factors themselves and by the interactions between them.With a response surface equation, numerical optimization by desirability function is carried out for the machining responses.Te objective of the optimization method is to arrive at the optimal factor settings that result in the maximization of material removal rate and minimization of surface roughness.Te function of desirability is given by equation ( 5), where n is the total number of responses.In this situation, each factor and response are given the same amount of weight.Te factors are rated as having a 3 out of 5 importance rating.
2.3.Material Removal Rate.During turning operation, the material removal rate (MRR) refers to the volume of material that is removed per unit time.Te material is removed in the form of a ring-shaped layer at the rate of one layer for every revolution of the work piece.Material removal rate is one of the most essential variables that determine the machining process, and it is usually preferable to have a greater rate when doing operations.Te equation allows for the determination of the material removal rate in mm 3 /s.
where D 0 and D i represent the initial and fnal diameter of the workpiece in mm, respectively.L is the length of the workpiece to be turned in mm, and f and N represent the feed rate in mm/rev and spindle speed in rpm, respectively.

Experimentation
All of the turning activities are carried out on a Gap Bed Lathe Machine, which, as shown in Figure 3, is capable of reaching a maximum speed of 1600 revolutions per minute and possesses a spindle power of 7.5 kilowatts.A roughness tester of the stylus type was employed in order to assess the surface roughness of each individual run that was acquired from the experiment depicted in Figure 4. Te parameter ranges for the degree of cutting, as well as the starting values for those ranges, were chosen from the manufacturer's handbook based on what was recommended for the material that was being examined [65,66].Table 3 provides an overview of various cutting settings together with the degrees of operation that correspond to them.Te Taguchi approach and the L 9 orthogonal array were applied in order to cut down on the overall number of tests that needed to be performed.Te results of the design of experiments (DOE) that was carried out are detailed in Table 4. Tere were three diferent sets of controls utilized during the course of the tests.

Result and Discussion
Finding the ideal settings for the turning parameters (spindle speed, feed rate, and depth of cut) is this research's primary goal.Tese numbers are targeted to maximize material removal rate and provide a surface with the least amount of roughness possible.Table 5 displays the design of experiments (DOE) that was conducted, including the experimental data for the surface roughness levels and the calculated signal-to-noise ratio.Te signal-to-noise ratio (SNR) holds signifcant importance within the Taguchi technique for the analysis of experimental data because of its efcacy to provide valuable guidance in the selection of the optimal level by minimizing variation around the average value [67].Moreover, it enables an objective comparison between two sets of experimental data by evaluating the deviation of the average from the target.Based on the principles of the Taguchi Method, quite a few researchers  recommended that the signal-to-noise (S/N) ratio be maximized for this study to heighten the cutting conditions for optimal outcomes [67,68].Figure 5 plot of the S/N ratio illustrates that there is less fuctuation for changes in the depth of cut, whereas there is greater variation for changes in the cutting speed.For surface roughness, it is clearly showing that the optimal level for turning is at the frst level of cutting speed, feed rate, and depth of cut.Specifcally, this means that the cutting speed at 119 m/min, feed rate at 0.11 mm/rev, and the depth of cut at 0.2 mm are the optimum values of cutting parameters for lowest surface roughens, which is in slight contrast with the results found by Karim et al. [69].However, Figure 6 plot for the S/N ratio indicates that there is less variance for changes in cutting speed, whereas there is greater variation for changes in feed.In 2011, Akkus conducted a study on hard turning of AISI 4140 grade steed and found that feed rate is the most signifcant factor for reducing the surface roughness [70].
In addition, when it comes to MRR, the variation in the depth of cut is much more signifcant than the diferences in cutting speed and feed.Similarly, for material removal rate, the optimal level of turning are all at third level.Tat is, cutting speed at 189 m/min, feed rate at 0.2 mm/rev and depth of cut at 0.5 mm will provide the highest value of MRR.Manikanda with his colleagues performed experimental investigation on EN31 steel by using a diamond shape carbide insert and found that depth of cut is the most promising factor for maximize the MRR followed by feed rate and cutting speed [71].
S/N ratio indicates the importance of each input parameter on the desired outputs.As a higher S/N ratio indicates closer to a higher quality, a bigger S/N ratio is preferable for both the cases.Te responses for signal-to-noise ratios obtained from the two sets of data are presented in Tables 6 and 7. Tables 6 and 7 demonstrate that, feed rate ranked 1, trailed by depth of cut which is rank 2 afected the surface roughness signifcantly, whereas in accordance with the S/N ratio, depth of cut is ranked 1, followed by feed rate at rank 2 to ensure higher amount of material removal.
Te analysis of variance (ANOVA) is a statistical method employed to assess the infuence of individual parameters within a given process.Te acquired data are assessed using the Minitab-19 software and are presented in Tables 8 and 9. Te calculation of the mean square involves dividing the sum of squares by the number of degrees of freedom.Similarly, the F ratio is determined by dividing the mean square by the mean square of the experimental error.
According to the results of the analysis of variance (ANOVA) conducted on surface roughness (as presented in Table 8), the F value is determined to be 126.86.Additionally, the contribution of feed rate to the observed variance is found to be 82.15%,followed by depth of cut with 9.16%.Te parameter's F value of 8.41 suggests that the cutting speed has a relatively smaller impact on the minimal surface roughness, which can be justifed by its lower contribution percentage.Te impact of these factors has statistical signifcance with a p value less than 0.05.Tese fndings indicate that feed rate has a greater infuence on achieving low surface roughness, which can be observed in the fndings of multiple researchers [72,73].Additionally, the R 2 value of the model for surface roughness is 96.76%, suggesting a higher level of reliability and credibility for the model.Te frst test yielded a minimum surface roughness value of R a � 0.541 m.According to the data presented in Table 8, the F value of 41.02 suggests that the depth of cut is the most infuential factor in determining the material removal rate, accounting for 57.33% of the overall contribution.In  [68,69].Te R 2 value of 93.01%obtained for the MMR model provides evidence of its statistical signifcance.Te Taguchi technique lacks the capability to evaluate or ascertain the impact of individual process parameters on the overall process.Te analysis of variance (ANOVA) is a statistical method employed to assess the infuence of individual parameters within a given process.Te acquired data are assessed using the Minitab-19 software and are presented in Tables 8 and 9. Te calculation of the mean square involves dividing the sum of squares by the number of degrees of freedom.Similarly, the F ratio is determined by dividing the mean square by the mean square of the experimental error.Advances in Materials Science and Engineering Regression Eq. for MMR, MRR � −13.83 + 0.00834 Cutting Speed+48.0feed rate+22.30depth of cut.
Te experimental design lacked the inclusion of a specifc condition necessary for determining the maximum rate of material removal.Te experiment was conducted under optimal machining conditions, revealing that the maximum rate of material removal reached 18.63 cm 3 /min.In addition, the predictive value of material removal rate was calculated using regression equation (7) in order to determine the percentage of error between the actual and predicted MRR.Te analysis revealed a discrepancy of 3.37% between the observed values, with the predictive value for material removal rate estimated at 15.26 cm 3 /min.Tis range of deviation is well validated by multiple previous investigations conducted by researchers on a close to similar cutting environment [26,69].
4.1.Optimization Using DFA.Finding the independent variable conditions that result in ideal or nearly ideal values for the response variables is the goal of multi-response optimization.Te main goal of this analysis of the desirability function was to minimize surface roughness and maximize material removal rate.Table 10 displays the defned factor ranges for the primary optimization.Table 11 illustrates an overview of the optimization to achieve the primary goal of optimizing the response parameter.Te selection of the ideal factor level and the desirability of each solution in the multiresponse optimization process are therefore depicted in Table 10.Because of the greatest desirability value being 0.903, the best parameters should be set

Conclusion
Te study provides a simultaneous optimization of cutting speed, feed rate and depth of cut by incorporating Taguchi L 9 orthogonal array and ANOVA methods.Furthermore, the  Advances in Materials Science and Engineering study also presents the signifcance of each input parameter through statistical analysis.
(1) Taguchi analysis L 9 shows that the optimum level of input parameters for minimum surface roughness are as follows: 630 rpm for cutting speed, 0.11 mm/ rev for feed rate, and 0.2 mm for depth of cut and the cutting speed at 1000 rpm, feed rate at 0.20 mm/rev, and depth of cut at 0.5 mm are the optimal values for maximum MRR.On the contrary, DFA showed the optimum surface roughness and material removal rate, with a combination of 0.5 mm for the depth of cut, 189 m/min for cutting speed, and a 0.11 mm/rev for feed rate.Moreover, the highest perceived value of 0.903 could be attained amongst the 39 solutions while setting the mentioned parameters.
(2) By incorporating ANOVA analysis, contribution for each of the input parameters for both surface roughness and MRR are found as follows: For surface roughness, the relevance of feed rate (82.15%) and depth of cut (9.16%) are statistically signifcantly more important than those of spindle speed (which is demonstrated to have less of an impact on surface roughness).On the other hand, the material removal rate is afected by the depth of cut, feed rate, and spindle RPM to varying degrees (57.33%, 23.89%, and 11.79%, respectively).( 3) Validation was performed on the analysis that was established by ANOVA for surface fnish and MRR.It yielded an average error of 3.24% and 6.99%, respectively.Tis demonstrates that the model's prediction is obviously at a level that is acceptable since it has a greater R 2 value, which is the measure of how adequate the model is.

Teoretical and Practical Implications of the Research.
Tis study has multiple implications from both perspectives (theoretical and practical).Te research focuses on optimization of SKD 11 steel machining based on multiple parameters.Furthermore, the study combines the use of Taguchi L 9 and ANOVA which appears to be valid from the error calculations.Moreover, the research also gives insight on tool conditions from dry machining of SKD 11.Tese insights can work as a guideline for future researchers who are working with similar hardened materials.On another note, the fnding of this study will help manufacturing industries to improve production rate by optimizing spindle speed, feed rate and depth of cut for dry machining of hardened materials.Te proposed method will help engineers to identify the signifcant parameters with ease and optimize them accordingly which in turn will allow small to medium industries in underdeveloped countries to use hardened materials for production without the requirement of heavy equipment's such as CNC lathe or advanced manufacturing systems such as Laser systems or electrochemical systems.Te study shows an interesting outlier: Although feed rate is the most signifcant factor for minimum surface roughness, in case of MRR, depth of cut is the most important.When compared, feed rate provides 58.26% more signifcance in case of surface roughness.

Future Research Direction.
Tis study, like all others, has limitations that future researchers can attempt to overcome.For instance, since optimizing the machining parameters in response to a greater number of output performance criteria will result in improved control over the machining process.Subsequent investigations may concentrate on integrating Taguchi-PCA in order to further improve the parameters.
Researchers can acquire valuable insights into techniques to improve tool life by including more experimental trials to examine the impact of factors on diferent types of tool wear.
To gain a more thorough understanding of the machined surface and the longevity of the machined component, the study could be extended to include residual stress, microhardness, or surface texture in addition to surface roughness.More research might be conducted to investigate the sustainability aspects of the machining process by examining energy consumption and carbon footprints related to turning SKD 11 hardened steel, this could lead to the discovery of new pathways for the application of environmentally friendly machining processes.Comparative research with other widely used materials in related applications could be performed before using it for large-scale applications to determine the machinability of various materials for a given application.

Figure 6 :
Figure 6: Main efects plot for material removal rate.

Table 3 :
Selected process parameters with diferent operating levels.

Table 4 :
Experimental design using L 9 orthogonal array.

Table 5 :
Te results of the experiments with S/N ratio values.

Table 6 :
Response table for signal-to-noise ratios for surface roughness (smaller is better).

Table 7 :
Response table for signal-to-noise ratios for material removal rate (larger is better).

Table 9 :
Analysis of variance (ANOVA) for material removal rate.

Table 11 :
Summary of the values obtained from optimization.

Table 10 :
Goals and factor range for optimization of surface roughness and material removal rate.