Machinability Investigation and Optimization of Process Parameters in Cryogenic Assisted Sustainable Turning ofAISI?L6 Tool Steel

，，，，，，4*

1.College of Mechanical and Electrical Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，P.R.China；

2.National Key Laboratory of Science and Technology on Helicopter Transmission，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，P.R.China；

3.Key Laboratory of High Efficiency and Clean Mechanical Manufacture，Ministry of Education，School of Mechanical Engineering，Shandong University，Jinan 250061，P.R.China；

4.National Demonstration Center for Experimental Mechanical Engineering Education，School of Mechanical Engineering，Shandong University，Jinan 250061，P.R.China

（Received 17 March 2020；revised 13 April 2020；accepted 18 May 2020）

Abstract: The application of cutting fluid is significantly increased in the machining sector to improve productivity.However，the inherent characteristics of cutting fluids on ecology，environment，and society shift the interest of researchers to work on environmentally friendly cooling conditions such as cryogenic cooling. Here，the effect of cutting speed and feed rate on the machining performance of the AISI?L6 tool steel is investigated under cryogenic cooling conditions. Then，the L9 Taguchi based grey relational analysis（GRA）is conducted to investigate the essential machining indices such as cutting energy，surface roughness，tool wear，and material removal rate（MRR）.The results indicate that the cutting speed of 160 m/min and feed rate of 0.16 mm/r are the optimum parameters that significantly improves the machining performance of AISI?L6 tool steel.

Key words：sustainable manufacturing；cryogenic machining；hardened steel；energy consumption；tool life

0 Introduction

Tool steel is famous for manufacturing cutting tools，hot dies，chisels，hammers，etc. The other application of this material involves the various man?ufacturing operations like deep drawing，wire draw?ing，and die casting，respectively［1］. In service，most tools are subjected to high and rapidly applied loads. Therefore，they must have the resistance to softening at elevated temperature，wear，deforma?tion，and breakage［1?2］. The increased percentage of nickel，along with chromium and vanadium，en?hances the hardenability of this material. These char?acteristics make it difficult to machine and reduce the tool life. Due to the long production times，the manufacturing costs are increased［3］. Most research?ers were focused on machinability of materials，where cutting simulation was regarded as an impor?tant approach［4］. Recently，various cooling condi?tions have been applied to improve machining perfor?mance. The cutting fluids increase the efficiency of the cutting process. In addition，they provide better service quality and increase tool life. The cutting flu?ids have certain disadvantages as well. The conven?tional cutting fluids are a source of environmental pollution and health hazards. The recycling of these cutting fluids is also difficult and expensive［5］. Much research has been done on alternative strategies for cooling and lubrication in machining processes. Krol?czyk et al.［6］have reviewed various cooling tech?niques from an ecological point of view. Five tech?niques were reviewed in detail during the research，namely dry cutting，minimum quantity lubrication（MQL），cryogenic cooling，high?pressure cooling，and biodegradable oils. According to the research，dry cutting is the best method as it eradicates the cutting fluids and is safe for workers.

Gupta et al.［7］have discussed the machinability of the Inconel?800 superalloy under sustainable cool?ing conditions for the turning process. They have compared the machining results of the near dry ma?chining （NDM） technique with dry machining.MQL process is used in this research as the NDM technique. During the research，it was discovered that NDM reduced cutting forces by 4% to 9% as compared to the dry machining environment. This may occur due to the reduction in friction as a result of the reinforcement of lubrication provided by the cutting fluids.

The surface roughness was reduced by 3% to 10% under NDM conditions due to a reduction in gradient temperature in the cutting area，reduction of friction due to enhanced lubrication and preven?tion of early damage of the tooltip. Tool wear was reduced by 4% to 11% in NDM as compared to dry machining. This is because cutting fluid in combina?tion with compressed air has almost abolished the crater on the rake surface. NDM generates small fragments of chips，whereas dry machining pro?duced some unbroken，very long continuous chips.Moreover，NDM produced dry chips，which are a favorable result for cleaner production. The applica?tion of liquid nitrogen （LN2），commonly called cryogenic cooling，is a lubrication technique that is under focus from a sustainable manufacturing point of view. It improves machinability by decreasing the cutting zone temperature. After being sprayed on the cutting zone，liquid nitrogen evaporates into the environment. Therefore，it has no health hazards and disposal problems，unlike conventional cool?ants［8］. Islam et al.［9］have investigated machining performance of hardened EN 24 steel under cryogen?ic cooling in comparison with dry and flood cooling conditions. During the study，it was discovered that the surface roughness was reduced under cryogenic cooling as compared to other conditions. This is due to the preservation of tool sharpness and better chip removal process. Mia et al.［10］have studied the influ?ence of single and double cryogenic jets on machin?ability characteristics in turning of Ti?6Al?4V alloy.During the study，it was concluded with a single cryogenic jet，there was a reduction in specific cut?ting energy，chip?tool interface temperature，and surface roughness by 8%，5%，and 8%，respec?tively. Mia［11］investigated the machining parameters under cryogenic cooling conditions. The study con?cluded that cryogenic cooling reduced cutting force，specific cutting energy，and improved surface finish as compared to dry cutting and conventional cutting oil applications. In another work of Mia et al.［12］，the benefits in terms of cutting forces and surface roughness of cryogenic cooling were investigated during turning of Ti?6Al?4V alloy. Kursuncu et al.［13］discussed the effect of cryogenic treatment in the machining of Inconel 718 alloy. Koneshlou et al.［14］have studied the effect of cryogenic treatment on microstructure，mechanical，and wear behaviors of AISI H13 tool steel. During the study，they ob?served that cryogenically treated samples of the aforementioned tool?steel showed less wear as com?pared to untreated ones. Huang et al.［15］discussed the microstructure of cryogenic treated M2 tool steel. Revuru et al.［16］have reviewed the perfor?mance of cutting fluids in the machining of titanium alloys. According to the review，cryogenic cooling is an effective alternative to conventional cutting flu?ids and MQL. It reduces cutting forces，helps to in?crease tool life，reduces friction，and improves the surface quality of the finished products. Moreover，the cryogenic cooling process results in active chip breaking and smaller chip contact length.

Several statistical techniques are used to design the experiments such as response surface methodolo?gy （RSM） based central composite design（CCD）［17］，Box?Behnken design（BBD），artificial neural network（ANN）［18］，and factorial method（full?factorial，half?factorial）［19］，grey relational analysis （GRA），and Taguchi method［20］，etc.Among several techniques applied for the design of experiments，Taguchi has got attention due to ease in application，having the capability to provide a minimum combination of experiments accommodat?ing several parameters having different levels of pa?rameters. Also，GRA has the capability to find the optimal combination of parameters having multiple performance measures.

Much focus in recent times has been on sustain?able development through cooling techniques that can achieve near dry machining［21］. However，not much research work can be found on the optimiza?tion of cutting parameters for tool steels from the sustainability point of view. Even less research work can be identified for low alloy L series tool steels.Patel et al.［22］have performed analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel.During their research，they found a linear relation?ship of surface roughness with cutting speed，feed，and nose radius. High surface quality was achieved for low feed value，high cutting speed，and large nose radius. Pathak et al.［23］have optimized cutting parameters in dry turning of AISI A2 tool steel for surface roughness and cutting force components.The research concluded that optimum results are ob?tained at minimum values of cutting speed，feed，and depth of cut.

From the literature，it was identified that the machining sector is searching for a sustainable and environmentally friendly cooling system to cope up with the low productivity，surface quality，and ener?gy consumption type challenges. Cryogenic LN2is reported as efficient for the machining of hard?to?cut AISI?L6 steel. Also，literature depicts that optimi?zation techniques to determine the optimum level of parameters are useful to achieve the actual experi?mental conditions. In this study，an attempt has been made to machine AISI?L6 tool steel under cryogenic LN2. Also，Taguchi based GRA was used for multi?response optimization to get the opti?mal experimental conditions. The controllable pa?rameters are cutting speed and feed rate，whereas performance measures are surface roughness，tool wear，energy consumption，and material removal rate.

1 Experimental Setup

Tool steel AISI?L6 is being taken as a work?piece material having hardness about 58 HRC. The chemical，physical，and mechanical properties of workpiece material are provided in Tables 1，2.

The length of the workpiece was 100 mm，and the diameter was 50 mm. All the turning tests were performed by using CK 4060CNC turning machine having a maximum spindle speed of 12 000 r/min and equipped with an 8 kW drive motor. The un?coated carbide tool number YG?8 and CNMG tool holder assembly were used. The tool geometry was as follows：the rake angle was -1°，and the tool clearance angle was 10°—12°［24］. The new tool was used for every cut.

Table 1 Chemical composition of AISI?L6 tool steel

T

able 2 Physical and mechanical properties of AISI?L6 tool steel

The machining process chosen for our research was turning，and the cooling environment was cryo?genic cooling. The tests were performed with three values of cutting speed，i.e.，100，130，and 160 m/min，three different levels of feed rate，which were 0.08，0.12 and 0.16 mm/r and a constant depth of cut equal to 1 mm. The range of cutting conditions was selected from the recommendations of the manufacturer’s handbook. In this study，Taguchi’s L9 orthogonal array（OA）is used from Minitab.The orthogonal array used has nine rows and three columns. The rows represent the number of tests，whereas the columns indicate the process parame?ters along with levels. The first，second，and third columns in this paper represent cutting speed，feed rate，and cooling conditions，respectively.

2 Measurement of Responses

Three parameters were measured，which are surface roughness，cutting energy，and tool wear.Surface roughness is a significant quality indicator of the machining process. In the current research，the surface roughness values were measured at different locations through Mehr Perthometer SJ?410 on the workpiece，and then meant the value of the surface roughness was calculated. This method helped to in?crease the accuracy of the results. Cutting energy is another important parameter that is related to the machining efficiency of the product. Cutting energy in the present work was measured through PPC cur?rent clamps. The MRR is another critical parameter in machining，which needs to be maximized to achieve the optimum results. It is the product of cut?ting speed，feed rate，and depth of cut. The tool wear is a crucial machining parameter that directly affects production time and the overall cost of the product. The tool wear is decided based upon three criteria：（1） Average width of flank wear（VBavg），（2）maximum flank wear（VBmax），and（3）notch wear at the depth?of?cut?line（VB?notch or VNmax）［5］. In this study，maximum flank wear（VBmax）is only considered as the parameter of wear. The measurements were carried out on workpiece material until the experimental process was completed，and finally，an average value of tool wear was calculated.

3 Results and Discussion

This section describes the surface roughness，cutting energy，MRR，and tool wear. The subse?quent analysis is presented in the sections below.The summary of the experimental results is present?ed in Table 3.

Table 3 Taguchi L9 array depicting the effect of parameters on performance measures

3.1 Taguchi method

Taguchi developed a statistical approach to de?sign the experiments［25］. This technique has accumu?lated the design of experiments from different world statisticians and different fields to mold it according to the requirements in the manufacturing field. Al?so，it was made easy for the practitioners to apply，considering fewer experimental designs，providing a clear understanding of performance measures varia?tion at different levels of parameters，accommodat?ing the economic burden of all the combination in?creases the number of experiments.Under the manu?facturing domains，following design are applied：

（1）Design any process/product and robust by considering the real conditions.

（2） Design any process/product and robust considering each component variation on target.

（3）Decrease the alterations across target val?ue.

During system design，engineering and scientif?ic knowledge is applied to produce a prototype de?sign. It includes product design，process design，and optimization stage. At the process design stage，optimization of the range of parameters to improve their effect on performance measures under optimal values. Besides，it is assumed that the optimal range of process parameters are insensitive to envi?ronmental conditions or noise factor. Classical full factorial design［26］provides a large number of experi?ments，while Taguchi uses an orthogonal array to design the smallest number of experiments consider?ing the entire parameter space.After deciding the ap?propriate orthogonal array，the Taguchi loss func?tion is defined to determine the deviation of perfor?mance measure from the desired value.The orthogo?nal array has the flexibility to accommodate multiple process parameters，with each having different pa?rameter levels. The loss function is simply trans?formed to signal to noise ratio（S/N?ratio）.

For the analysis of parameters，S/N ratio is calculated according to the desirability/performance characteristic of the performance measure，such as lower?the?better， nominal?the?better， and high?er?the?better. Regardless of the performance charac?teristic，the larger S/N ratio corresponds to better performance. Therefore，the highest S/N ratio is a desirable level in the analysis. Furthermore，analy?sis of variance（ANOA）is done to evaluate which parameter has contributed to what percentage. Al?so，mathematical models for each specific perfor?mance measure can be determined considering all the parameters simultaneously. The 3D surface plots can be achieved to calculate the simultaneous response of two parameters on the performance mea?sure.

The effect of cutting speed and feed rate on sur?face roughness，cutting energy，MRR，and tool wear are presented，respectively.

3.2 Surface roughness

The surface roughness of the machined surface is one of the most important parameters in the ma?chinability of materials. The effect of machining pa?rameters on surface roughness is evaluated using a portable spectrometer，as shown in Fig.1.

Fig.1 Surface roughness during the turning process

Fig.2 3D response surface of feed and cutting speed on sur?face roughness

From Fig.2，it can be seen that surface rough?ness is inversely proportional to the cutting speed.So，by increasing the cutting speed from 100 m/min to 160 m/min. There is a decrease in surface rough?ness，resulting in better surface quality at higher cut?ting speeds. The straight?line slope shows that the relation between these two factors is linear. This can be attributed to the fact that at lower cutting speeds，the tool has a high amount of wear owing to an earlier wear cycle. Secondly，at lower cutting speeds，BUE and BUL areas on the tool’s cut?ting?edge form a protrusion in the cutting directly from the tool to the workpiece，these coating layers of materials result in changes to the depth of cut and tool geometry. As a result，the irregular geometry contacts with the material being machined，causing surface roughness to increase. On the other hand，the feed rate is directly proportional to the surface roughness. By increasing the feed rate from 0.08 mm/r to 0.16 mm/r，the surface roughness is increased. For all three levels of cutting speed，the increment in surface roughness from 0.08 mm/r to 0.12 mm/r is small as compared to the increment from 0.12 mm/r to 0.16 mm/r. The increase in sur?face roughness with the increase in feed rates can be attributed to the fact that the increase in feed rate caused an increase in feed force and volumes of ma?terial removed［26］. Sarikaya et al.［26］have also evalu?ated cutting parameters and cooling/lubrication methods in turning of Haynes 25 superalloy.Accord?ing to their results，under dry，there was a decrease in surface roughness with an increase in cutting speed from 15 m/min to 45 m/min，and a slight in?crease in surface roughness from 45 m/min to 60 m/min，under dry，wet cooling and MQL conditions.Sarikaya et al.［27］have analyzed machining parame?ters in CNC turning under MQL conditions for AI?SI 1050 steel by using the Taguchi design and re?sponse surface methodology（RSM）method. They have performed cutting experiments under dry，wet cooling and MQL conditions. They have calculated surface roughness at cutting speeds from 80 m/min to 200 m/min and have also observed the best sur?face quality at the highest level of cutting speed.The results of the literature support current research result that surface roughness is inversely proportion?al to cutting speed.

Both of these factors increase surface rough?ness. Kumar et al.［28］have verified the effect of feed rates on surface roughness for five materials for the CNC turning process. The feed rate range for their experiments was from 0.05 mm/r to 0.15 mm/r.The value of spindle speed was kept in the range from 339 r/min to 980 r/min. In most materials，surface roughness increased with increase and espe?cially at high levels of spindle speeds and in the range of feed rates from 0.1 mm/r to 0.15 mm/r.Therefore，the study concluded that high surface quality could be achieved at high spindle speeds and low feed rates. Bashir et al.［3］have studied the effect of feed rate on surface roughness for surface milling.According to their research，surface roughness was found to increase with an increase in feed rate from 22 m/min to 68 m/min under dry and MQL condi?tions，and the best surface quality was achieved at 22 m/min. The established results are in accordance with current research results concerning the relation?ship between surface roughness and feed rate.

3.3 Cutting energy

From Fig.3，it can be observed that cutting en?ergy is directly proportional to the cutting speed.The cutting energy increases with the increase in cutting speed and feed rate. At all levels of feed rate，the relation between cutting speed and cutting energy is almost linear，the increments in cutting en?ergy from 100 m/min to 130 m/min and from 130 m/min to 160 m/min are almost the same.From Fig.3，it can be observed that cutting energy is directly proportional to the cutting speed and feed rate. The relationships of cutting speed and feed rate with cutting energy are almost linear. At higher cut?ting speeds and feed rates，the axis motor needs to move faster. This results in an increase in cutting en?ergy［10］.

Fig.3 3D surface of feed and cutting speed on cutting energy

3.4 Material removal rate（MRR）

MRR is an output perimeter that is derived from cutting speed and feed rate. The mathematical relation between MRR and cutting parameters is provided in Eq.（1）.

whereVcis the cutting speed，fthe feed rate，andapthe the depth of cut. Theoretically，MRR is directly proportional to cutting speed，feed rate，and depth of cut，and the relationship is linear. In this paper，the depth of cut is constant，so the focus of this work is on the relationship of MRR with cutting speed and feed rate. The MRR is calculated from Eq.（1）for all experiments. Fig.4 shows that MRR is directly proportional to the cutting speed and feed rate. It is found to increase linearly with the increase of cutting speeds at all levels of feed rate，and vice versa.

Fig.4 3D response surface of feed and cutting speed on MRR

According to the researches published by Mia et al.［29］and Gadekula et al.［30］，MRR was found to be directly proportional to cutting speed and feed rate. The results of the literature on the effect of cut?ting speed and feed rate on MRR support the cur?rent research work.

3.5 Tool life

From Fig.5，it can be inferred that tool wear is inversely proportional to the cutting speed and feed rate. By increasing the cutting speed and feed rate，tool wear is decreased， and vice versa. The straight?line slope in both cases indicates that the re?lation of tool wear with cutting speed and feed rate is inversely linear. Tool wear here was flank wear，as shown in Fig.5.

Fig.5 Flank wear patterns during the turning process

Fig.6 shows that flank wear is directly propor?tional to the cutting speed and feed rate. The straight?line slope in both cases indicates that the re?lationship of tool wear with cutting speed and feed rate is linear. This can be attributed to thermal soft?ening of tool material at higher cutting speeds and feed rates［11］. However，the effect of feed rate is more pronounced than the effect of cutting speed on tool wear.

Fig.6 3D response surface of feed and cutting speed on flank wear

4 Grey Relation Analysis

When the performance measures are deter?mined in different units owing to different attri?butes，it can influence the contribution of some per?formance measure. It will occur if some perfor?mance measures have substantial values，and some have the least. Besides，their goals（maximum，minimum），and direction are different，thereby leading to wrong interpretation or analysis. There?fore，it is essential to consider all the performance measures to a comparable sequence in terms of nor?malization. This process is named grey relational analysis（GRA）. It analyzes a complicated uncer?tainty in several performance measures and optimiz?es it towards the target objective.

The response optimization is generally used for optimization problems. However，this method can?not be used when dealing with problems involving optimization of more than one parameter. For such problems，Taguchi based grey relational analysis（GRA）is used. The output parameters in this paper are surface roughness，cutting energy，MRR，and tool wear. The purpose of the GRA is to optimize all parameters，which means minimizing surface roughness，cutting energy and tool wear，and maxi?mizing MRR. Taguchi based GRA is basically a sta?tistical method，which includes a relational analysis of the uncertainty in the system model and lacks of information. It involves a correlation analysis of se?quences with uncertainty and discrete data. It deter?mines the degree of approximation through grey re?lational grade（GRG）［3］.In this work，GRG was de?termined in the first step after the normalization of the experimental results. By normalization， we mean that we make the range of each of the parame?ters from 0 to 1. This is known as grey relational generating. Three criteria are used in GRA，which are“larger?the?better”“smaller?the?better”，and“nominal?the?best”. In the current research，we aim to maximize the value of MRR. So，we use the“larger?the?better”criterion for MRR，which is rep?resented by Eq.（2）.

The aim of this paper is to minimize the value of cutting energy，surface roughness，and tool wear. Here，we use the“smaller the better”criteri?on for these factors，which are governed by

wherexi（p）is the value after grey relational genera?tion，max（（p））and min（（p））are the largest and the smallest values of（p）for thepth re?sponse，respectively. For better response，values of normalized results should be as large as possible with the best possible value equal to one. The grey relational coefficient（GRC）denoted by?i（p），pro?vides the relation between desirable and real experi?mental normalized data.GRC is defined by

whereΔ0i（p）is the difference between the absolute values ofx0（p）andxi（p）whereasΔminandΔmaxare the minimum and the maximum values of the abso?lute differences between all comparing sequences，respectively.ζis the distinguishing or identification coefficient. It helps to minimize the effect ofΔmax；when its value is too large or too small，its value lies between 0 and 1. It also enhances the difference significance of GRC. Most researchers take its value equal to 0.5，so in our work，the value ofζis taken as 0.5. The GRC for all responses were calculated using Eq.（5）. GRG is a weighted sum of the GRC and is calculated by

wherenis the number of performance characteris?tics，here，nis 4. The higher value of GRG indi?cates that the corresponding process parameter com?bination is closer to the optimal. Table 4 provides a summary of GRC and GRG measurements. In Ta?ble 4，the ninth experiment gives the highest value of GRG，which means that it has the best multi?per?formance characteristics，i.e.，optimum values of all output parameters among all experiments.

Table 4 GRC and GRG for each experimental run

4.1 ANOVA for GRG

Analysis of variance（ANOVA）is a statistical method used to examine the interactions of all the control factors under consideration［3］. We use ANO?VA to identify the significant factors. ANOVA is with a 95% confidence level and a 5% significance level. The ANOVA analysis results are shown in Table 5. The results show that the model is signifi?cant. Also，cutting speed（A），feed rate（B），the combination ofAandBdenoted byAB，A2，andB2are all significant factors and have a severe impact on GRG values. This is because the correspondingPvalues for all these factors are lower than 0.05.The value ofR?square is 0.996 9，and the adjusted value ofR?square is 0.991 8. Both these values are very close to each other，which shows the reliability of data.

Source Model Degree of freedom ABA B A2 B2 F?value 22.650 030 15.911 83 46.566 510 27.436 270 16.900 73 6.434 825 p?value prob > F 0.013 8 0.028 2 0.006 4 0.013 5 0.026 1 0.084 9 Residual Cor total Sum of squares 0.029 415 0.004 133 0.012 095 0.007 126 0.004 390 0.001 671 0.000 779 0.030 194 51111138 Mean square 0.005 883 0.004 133 0.012 095 0.007 126 0.004 390 0.001 671 0.000 260

4.2 Calculation of GRC and GRG

After using the equations，GRC and GRG are calculated and depicted in Table 5.

The 3?D plot for GRG in ANOVA is present?ed in Fig.7. It can be deduced that GRG values ini?tially decrease with an increase in cutting speed，but eventually，the relationship becomes directly propor?tional at higher cutting speeds. So，GRG decreases with increasing cutting speed from 100 m/min to 130 m/min and then increases with an increase in cutting speed from 130 m/min to 160 m/min，at?taining a maximum value of 160 m/ min. On the other hand，GRG is directly proportional to feed rate，which means that by increasing the feed rate，GRG is increased and vice versa. The relation be?tween GRG and feed rate exponentially results in very high GRG values at large values of feed rates.

Fig.7 3?D plot for grey relational grade

4.3 Confirmation experiments

At the last stage of research，confirmation ex?periments of the control factors are performed at op?timal and random levels to verify the accuracy of op?timized results and find the improvement in overall output value. These experiments are conducted three times. The estimated GRG can be calculated by

whereγestimatedis the GRG，which predicts the opti?mal machining parameters，γmthe total mean GRG，γithe average GRG at the optimal level，andothe number of design parameters that significantly affect the quality characteristics. Table 6 compares the estimated GRG calculated by Eq.（6）with the

experimental value. In Table 6，the estimated GRG value is 0.710 6，and the experimental GRG value is 0.711 2. The close difference between these two values validates both the expected and experimental results. The improvement in GRG from initial fac?tor combination（V3?F2）to optimal factor combina?tion（V3?F3）was 0.113 9，thus showing a percent?age improvement of 19.07%.

Table 6 Results of the confirmation experiment

Several researchers have performed multi-re?sponse optimization for machining parameters through GRA［5，31?34］. In a nutshell，we can say that the performance parameters， which are surface roughness，cutting energy，MRR，and tool wear，are significantly optimized through Taguchi based GRA. It is evident from literature results that Tagu?chi based GRA helps to improve the optimization process. The current study also provided an im?provement in GRG，as described in the optimization results. It can，therefore，be safely concluded that literature results verify the findings of current re?search regarding GRA.

5 Conclusions

The cutting parameters are optimized by Tagu?chi based GRA with the multiple?performance out?puts.The conclusions are summarized as follows：

（1）It is concluded from ANOVA results that both cutting speed and feed rate are significant fac?tors affecting the cutting performance.

（2） According to the ANOVA results for GRG，the percentage contribution of flow rate and cutting speed are 46.57% and 15.91%，respective?ly. Therefore，the flow rate is the most significant control factor on GRG for minimization of surface roughness，cutting energy and tool wear，and maxi?mizing MRR.

（3）Taguchi based GRA directly combines the multiple responses into a single performance charac?teristic known as GRG. The GRG results depict that the best combination values are cutting speed of 160 m/min and feed rate of 0.16 mm/ r.

（4）The improvement in GRG from initial fac?tor combination（V3?F2）to optimal factor combina?tion（V3?F3）is 0.113 9，thus showing a percentage improvement of 19.07%.