Influence of TiB2particles on machinability and machining parameter optimization of TiB2/Al MMCs

Ruisong JIANG,Xinf CHEN,Renwei GE,Wenhu WANG,Guodong SONG

aThe Key Laboratory of Contemporary Design and Integrated Manufacturing Technology,Ministry of Education,Northwestern Polytechnical University,Xi’an 710072,China

bChina Academy of Engineering Physics,Mian’yang 621900,China

1.Introduction

Particle reinforced metal matrix composites(PRMMCs)have emerged as an important class of materials for aerospace and some other applications due to their superior properties such as higher strength to weight ratio,high elastic modulus and wear resistance.1,2Typically,PRMMCs can be prepared in two ways,which are called ex situ and in situ process respectively.In ex situ process,the reinforcements are synthesized separately and added in the matrix by a secondary process such as stir casting.The segregation of reinforcement particles and poor adhesion at the interface are normally observed in ex situ composites.3,4On the contrary,the in situ composite involves synthesis of reinforcing phases directly within the matrix,which leads to a better adhesion at interfaces and hence improves mechanical properties.5

Concurrently,most researches focus on the material preparation process6and the mechanical properties7,8of in situ particles reinforced MMCs. However, for engineering applications,adequate knowledge for machining these high performance materials is necessary.It is well known that the reinforcement particles embedded in the matrix are highly abrasive.This makes the machining of MMCs difficult,and the difficulties primarily are rapid tool wear and poor surface quality.9Since the preparation process of ex situ MMCs is much easier,SiC particles reinforced MMCs are widely used in industry practice.Hence,most studies have dealt with the machinability of SiC particles reinforced MMCs in tool wear10–12,surface integrity12–14,and chip formation.15,16

On the other hand,very little work has been done on the machining of in situ MMCs.Ding et al.17,18studied the grinding behavior of TiCp/Ti-6Al-4V MMCs(PTMCs).They found that PTMCs are more difficult to remove than Ti-6Al-4V,and low depth of cut and high workpiece speed are beneficial for a better surface quality.Further,the performance of electroplated CBN wheel and that of brazed CBN wheel were compared,and it was found that brazed CBN wheel has greater potential in high-speed grinding of PTMCs according to experimental results.Anandakrishnan and Mahamani19investigated the machinability of in situ Al-6061-TiB2MMCs.The effects of cutting parameters on tool wear,cutting force and surface roughness were analyzed.The relationship between TiB2reinforcement ratio and tool wear,surface roughness,and cutting forces were achieved.Senthil et al.20studied the machinability characteristics of homogenized Al-Cu/TiB2in situ metal matrix composites.The effects of parameters on performance measures were investigated during turning operations,and the built-up edge and chip formation were also examined.Siva et al.21developed a new in situ ceramic reinforced aluminum metal matrix composite,and the machinability of this new AMC was investigated by comparing with two other composites made with Al2O3and Al2O3-SiC.Jiang et al.22carried out experimental investigation on the machinability on TiB2/Al MMCs.Tool wear,surface quality,and chip formation were discussed.It was found that PCD tool sustained the least tool wear compared to PCBN and coated-carbide tools.Xiong et al.23,24studied the surface integrity and tool wear mechanisms of TiB2/Al MMCs.The main tool wear mechanisms are abrasion,adhesion,chipping,and peeling wear.Tool life is various from 3 to 20 min for uncoated carbide tools,and milling speed has the dominated influence.

Besides,in engineering practice,selection of cutting conditions for MMCs is the most critical job in machining operation.Palanikumar and Karthikeyan25investigated the influence of machining parameters on the surface finish obtained in turning LM23 Al/SiC particulate composites,and the optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness were determined using response surface methodology(RSM).Also,Palanikumar et al.26optimized machining condition for minimizing the surface roughness by using desirability function approach.Sahoo and Pradhan27presented the influence of process parameters like cutting speed,feed and depth of cut onflank wear and surface roughness in turning Al/SiCpmetal matrix composites using uncoated tungsten carbide insert in dry environment.The optimal parametric combination forflank wear and surface roughness was achieved through Taguchi approach.

Additionally,some researchers dedicated their effects on the optimization of cutting parameters by using soft computing.Muthukrishnan and Davim28studied the surface roughness of Al-SiC(20p)by using PCD insert under different cutting conditions.The experimental data were tested with analysis of variance(ANOVA)and artificial neural network(ANN)techniques.Ramanujam et al.29presented the detailed experimental investigation on turning Al/SiC MMCs using PCD insert.The correlation between cutting speed,feed and depth of cut to the specific power and surface finish on the work piece was established.The optimum machining parameters were obtained by Grey relational analysis.For in situ particles reinforced MMCs,Kishore et al.30studied the effect of process parameters such as cutting speed,feed rate and depth of cut on response cutting force,surface roughness,andflank wear during turning process of in situ Al6061-TiC metal matrix composite.Moreover,Kishore et al.31investigated the contribution of cutting speed,feed and depth of cut on cutting force and surface roughness of Al6061-TiC by using Taguchi L-27 orthogonal array and ANOVA.

From the above analysis,it can be found that a lot of work has been conducted on the machinability and cutting parameter optimization of ex situ SiC particles reinforced MMCs.However,different microstructures between ex situ MMCs and in situ MMCs result in different mechanical properties.Consequently,the machinability of in situ MMCs will be different from ex situ MMCs,but only a little work has been conducted on the machinability and cutting parameter optimization of in situ MMCs.In addition,machining effi-ciency is also an important index for industrial applications.

To address the problems above,this study try to achieve a better understanding of the effects of reinforced particles on machining forces,residual stress,and surface roughness with varied cutting parameters when TiB2particle reinforced MMCs specimen is machined.Moreover,based on the experimental results,a multi-objective optimization model was proposed to get optimal machining parameter combinations by considering material removal rate and surface roughness.The organization of the paper is as follows:Section 2 discusses the detailed conditions of the machining trials.In Section 3,the experimental results are presented and discussed.In Section 4,the multi-objective optimization model is established and optimized by using GA algorithm.Finally,conclusions and future work are given in Section 5.

2.Experiment

2.1.Material and specimen

The materials used in this experiment were non-reinforced 7050 aluminum and the same alloy reinforced with 6 vol%TiB2(The size of TiB2particles varies from 50 to 200 nm)particles using the mixed salts method.The nominal chemical composition(wt%)of the matrix alloy is shown in Table 1.Table 2 shows the mechanical and physical property of in situ 6%TiB2/Al MMCs,and the microstructure of this material is shown in Fig.1.The specimens were made from rectangular blocks of 7050 aluminum alloy and TiB2/Al MMCs by turning process respectively.The dimension of specimens is ?20 mm×100 mm,which are shown in Fig.2.

Table 1 Chemical composite of 7050 alloy.

Table 2 Mechanical and physical property of TiB2/Al MMCs.

2.2.Experimental setup

The experiments were carried out on a CNC turning centre,BOCHI CK7525,using a bar turning process under dry conditions.Due to the abrasive effect of the TiB2particles,a polycrystalline diamond(PCD)tool was employed in this study.The turning conditions are given in Table 3.Moreover,the cutting setup is shown in Fig.3.

2.3.Measurement

The turning force was measured online using a Kistler 9257B piezoelectric dynamometer,coupled to Kistler 5080A multichannel charge amplifier and computer data acquisition software.The illustration of cutting force measurement system is shown in Fig.4.Fris the radial force,Fcis the cutting force,andFtis the thrust force.Surface roughness was measured with a surface roughness tester(T620A)using evaluation and cut-off length of 0.8 mm.Measurement of each point was repeated twice and the average values were reported.The surface residual stress was measured using a PROTO LXRD MG2000 residual stress measurement system.

3.Experimental result analysis

3.1.Machining forces

Fig.1 Microstructure of TiB2/Al MMCs.

Fig.2 Specimens of TiB2/Al MMCs.

Table 3 Turning conditions.

The measured cutting force(Fc)and thrust force(Ft)at different cutting speeds and feed rates are presented in Figs.5 and 6.It can be seen that the cutting force for the non-reinforced 7050 aluminum alloy is smaller than that for the TiB2/Al MMCs.In Fig.5(a)and(b),the cutting and thrust forces at different cutting speeds for 7050 aluminum alloy and MMCs are presented.Cutting speed has a significant influence on the cutting and thrust forces.As the speed increases for MMCs,the forces decrease quickly while the speed is less than 50 m/min.With further increase of speed,the forces increase slightly.The main mechanisms of this can be summarized as follows:(1)with the increase of cutting speed,the friction ratio between tool and workpiece decreases;(2)the cutting temperature increases with the increase of cutting speed,which will soften the metal matrix.With the coupling effect of the two causes mentioned above,the forces decrease as the cutting speed increases from 10 m/min to 50 m/min.

Fig.6 shows the cutting and thrust forces at different feeds.It can be seen that the force of MMCs is bigger than that of 7050 aluminum alloy.The forces for both materials increase almost linearly with the increase of feed.This is because with the increase of feed rate,the material removal rate increases,which means that more energy is necessary for chip formation process.As a result,the cutting force increases.On the other hand,the increase rate of MMCs is much bigger than that of 7050 aluminum alloy.At low feeds,the forces of both materials are almost the same.Due to the existence of reinforced particles,the shear stress becomes larger while the feed rate increases for TiB2/Al MMCs.Hence,as the increase of feeds,the difference between non-reinforced alloy and MMCs becomes larger.Also,the feed has almost no influence on the thrust force of non-reinforced alloy.

Besides,the force signals from dynamometer were studied to get a deep understanding of the influence of reinforcements on force generation.The maximum forces during turning of MMCs and 7050 at speed 75 m/min,feed 40 mm/min,and depth of cut 0.6 mm are presented in Fig.7.It is noticed from Fig.7 that the particles change the force significantly.The maximum cutting force is bigger than radial force during turning of MMCs.On the contrary,the maximum cutting force is smaller than radial force during turning of 7050.This may be due to the vibration when turning of 7050.Since the stiffness of 7050 is smaller than TiB2/Al MMCs,the workpiece is easier to vibrate during the turning process.This will cause that the maximum radial force is bigger than cutting force.Meanwhile,the average radial force is smaller than cutting force when 7050 alloy is turned.Also,the deviation between cutting force and thrust force during turning of MMCs is much bigger than that of 7050,which indicates that the TiB2/Al MMCs is heterogeneous because of the reinforcements.Moreover,it can be seen that the variation of radial force is bigger than that of cutting force and thrust force while both materials are turned,and it may be because vibration occurs during the turning process.

Fig.3 Cutting setup.

Fig.4 Illustration of cutting force measurement system.

3.2.Residual stress

Fig.5 Variation of forces with speed(at f=40 mm/min and ap=0.6 mm).

Residual stress is an important parameter to evaluate the surface quality of workpiece.In most cases,compressive stress can increase the fatigue lifetime of workpiece.These stresses depend on the material of workpiece and machining process.Some researches indicated that both the mechanical and thermal effects are responsible for the generation of residual stresses on the machined surface.In this study,the transverse and longitudinal stresses of both materials at different cutting speeds and feeds were investigated.Fig.8 presents the effect of cutting speed on the residual stress in transverse and longitudinal directions.It shows that the residual stress is compressive(100–300 MPa)on machined surface of MMCs for the considered range of speeds,and the compressive stress gets larger as the cutting speed increases after 50 m/min.Besides,the residual stress is nearly neutral for non-reinforced 7050 aluminum alloy,and the influence of speed on transverse residual stress is negligible.Also,from Fig.8,it can be seen that the residual stress tendencies are similar with the machining force tendency of MMCs.This is one of the evidences that the cutting force has a great influence on the residual stress.

Fig.9 presents the effect of feed rate on residual stress for both materials.It can be seen that the feed rate has little influence on the residual stress of non-reinforced alloy,and the stresses are relatively small(-50 to 10 MPa).However,the compressive residual stress decreases with the increase of feed.After a certain feed,small decrease of residual stress is noted.

It is interesting to notice that the residual stress is quite different between TiB2/Al MMCs and 7050 alloy while the cutting forces have no significant difference.In fact,it is well known that the cutting force will result in compressive stress,and due to the effect of thermal dilation,tensile stress will be generated under high cutting temperature.For 7050 alloy,the cutting force and temperature are relatively small.In addition,the thermal dilation will rebound after the workpiece cools down.The compressive stress generated by cutting force was estimated by the tensile stress generated by thermal dilation effect.Hence,the residual stress of 7050 aluminum alloy is nearly neutral.However,due to the existence of particle reinforcement,the thermal dilation of TiB2/Al MMCs was reduced significantly under high cutting temperature.Also,the cutting force of MMCs is almost twice that of 7050.As a result,the mechanical effect plays a dominant role during the cutting process,and compressive stress was inspected(Figs.8 and 9).

Fig.6 Variation of forces with feed(at V=75 m/min and ap=0.6 mm).

Fig.7 Force signals during turning of MMCs and 7050(at V=75 m/min,f=40 mm/min,and ap=0.6 mm).

3.3.Surface roughness

Fig.10 shows the effect of cutting speed on surface roughness.It can be seen that the roughness of TiB2/Al MMCs is smaller than the roughness of non-reinforced 7050 aluminum alloy at all cutting speeds investigated.This may be because the reinforcement particles decrease the ductility of TiB2/Al MMCs and it tends to fracture during turning.Also,from Fig.10,we can see that increasing in cutting speed will improve the surface roughness.This may be due to lower sideflow of material at higher cutting speed.On the other hand,the influence of feed rate on the surface roughness is shown in Fig.11.At the same level of feed rate,the surface roughness increases with the increase of feed rate.Besides,at low feeds,the roughness of MMCs is smaller than that of non-reinforced alloy,but the reverse trend is observed at high feeds.

Also,the machined surface of non-reinforced 7050 aluminum alloy and in situ TiB2particles reinforced MMCs under the same cutting conditions are shown in Figs.12 and 13.It is noticed that the feed marks are very irregular for nonreinforced 7050 aluminum alloy due to the material softening during the cutting process.On the contrary,the feed marks are very clear on MMCs surface and the feed marks become intensive as the cutting speed increases.

The experiment results are quite different from the conclusions achieved by using ex situ SiC particles reinforced MMCs.16That may be because of the size of reinforcement particles.The size of TiB2particle is nanometer to submicrometer and has little influence on the machined surface.

Fig.8 Effect of cutting speeds on residual stress(at f=40 mm/min and ap=0.6 mm).

Fig.9 Effect of feed rate on residual stress(at V=75 m/min and ap=0.6 mm).

Fig.10 Effect of cutting speed on surface roughness(at f=40 mm/min and ap=0.6 mm).

Fig.11 Effect of feed rate on surface roughness(at V=75 m/min and ap=0.6 mm).

4.Optimization of turning parameters for surface roughness and MRR

Surface roughness is the most important parameter to evaluate the surface quality of workpiece,since irregularities in the surface may form nucleation site for cracks or corrosion.32In this section,the relationship between cutting parameters and surface roughness was studied experimentally.In order to give out a quantitative analysis between the cutting parameters and surface roughness,response surface methodology(RSM)was employed to develop the surface roughness model.RSM is one of the important techniques for determining and representing the cause-and-effect relationship between true mean responses and input control variables influencing the responses as a two-or three-dimensional hyper surface.Further,the cutting parameters were optimized with the constraints of surface roughness and material removal rate.

4.1.Development of surface roughness model

In this section,Box-Behnken designs were employed to design the experiments,since they have fewer design points than central composite designs,and can efficiently estimate the firstand second-order coefficients.Box-Behnken designs always have 3 levels per factor.The cutting speed is designed from 110 m/min to 300 m/min,the feed rate from 40 mm/min to 120 mm/min,and the depth of cut from 0.4 mm to 1.2 mm.The experimental parameters used and the corresponding responses are given in Table 4.

As indicated in previous researches26,33,a second-order quadratic model can satisfy the required precision of approximation for the true functional relationship betweenRaand cutting parameters,which can be expressed as Eq.(1).

Fig.12 Machined surface of 7050 at different speeds(at f=40 mm/min and ap=0.6 mm).

Fig.13 Machined surface of MMCs at different speeds(at f=40 mm/min and ap=0.6 mm).

whereRais the surface roughness of workpiece,terms β are the regression coefficients,xiare the values of theith cutting parameter,and ε is the experimental error of the observation.

In order to verify the conclusions of previous studies,the analysis of variance(ANOVA)was applied to study the effect of the input parameters on the surface roughness.Three different kinds of model,namely linear,2FI,and quadratic,were compared.Standard deviation(Std.Dev.),coefficient of determination(R2),adjusted coefficient of determination(Adj.R2),predicated coefficient of determination(Pred.R2),and press are summarized in Table 5.From Table 5,it can be seen that the quadratic model is the best appropriate model,so quadratic model is suggested as response surface function.Based on the data listed in Table 4,the relationship between the surface roughnessand machining parametersforsecond-order response surface model has been developed using RSM in uncoded units as follows:

To check the model adequacy,the original data which generate regression model were used for the purpose of verification.Besides,another set of check data including three surface roughness values were used in checking the precision of the RS model.The checking data are shown in Table 6.The checking data were selected from the cutting parameters’space with a good distribution.Consequently,these data can perform a good check on the accuracy of the RS model.From Table 6,it can be seen that the max error is within 10%.Therefore,the regression model is validated.

4.2.Optimization problem formulation

The surface roughness may different from parts’types and potions,which means that some other criteria must be taken into consideration while surface roughness satisfied a certain value.From a more practical perspective,material removal rate(MRR)is also an important criterion in machining operation.So,the surface roughness as well as MRR was optimized in this study.The MRR is in mm3/min,which can be calculated directly from

With the constraint of surface roughness,high MRR values can be achieved by adjusting cutting conditions with the help of an appropriate numerical optimization method.Then,the multi-objective optimization model can be formulated in the standard mathematical format as follows:

Within ranges:

Cutting speed:70 m/min≤V≤350 m/min

Feed rate:20 mm/min≤f≤100 mm/min

Table 4 Experimental results of surface roughness.

Table 5 Model summary statistics.

Table 6 Data set used for checking accuracy of RS model.

In Eq.(4),MRR is the material removal rate model as shown in Eq.(3),andRais the RS model developed in Section 4.1.In the above optimization problem definition,a better solution is also forced through the constraint definition as shown in Eq.(4c).The ranges of cutting parameters in optimization have been selected based on the recommendation of cutting handbook.

4.3.Optimization results and discussion

In this study,the two objectives are in conflict with each other.For example,on the one hand,the MRR increases with the increase of feed rate,but on the other hand,the surface roughness also increases as the feed rate increases.So,if the efforts were aimed only at reducing the surface roughness,the other objective(increasing the MRR)would never be reached.In this situation,a compromise among all objectives is necessary.Typically,the method of sum of weighted factors is commonly used to solve multi-objective problems.Usually normalized weights are used(the sum of all weights equals to 1)and only one answer is obtained in each run.

In fact,there are a set of answers,which do not have any superiority to one another,called Pareto optimums,in multiobjective problems.For example,as mentioned above,surface roughness is different from part to part.The machining process planning must be given out accordingly.To overcome this issue,Pareto-based method was employed to solve the optimization problem in this paper,which can offer a set of solutions.Then,one could be chosen by the user based on technicaloreconomicconsiderations.Consequently,the Pareto-based genetic algorithm was used to solve the optimization problem.

The GA involves some main operations such as initializing,evaluation,crossover and mutation,selection,etc.In this study,GA was implemented with the following GA parameters:population size=100;crossoverprobability=0.8;mutation probability=0.05;number of generations=300.The multi-objective optimization model was optimized by using commercial software MATLAB.The Pareto optimal solutions for all objectives are plotted and shown in Fig.14,in which each point represents a Pareto optimal solution.

From Fig.14,it can be seen that the MRR increases with the decrease of surface roughness,which indicates that the optimum values between these two objectives conflict with each other.At point I,the surface roughness is the smallest,but the MMR is relatively low,and the cutting parameters are:V=194 m/min,f=41 mm/min,andap=0.425 mm.However,at point II,the surface roughness is around 1.2 μm,the MMR is relatively high,and the cutting parameters are:V=297 m/min,f=119 mm/min,andap=0.984-mm.Forthisstudy,an optimalcompromisesolution between surface roughness and MRR can be achieved near the base points shown in Fig.14.

In order to verify the optimal results,a comparative experiment was conducted.In this experiment,a set of optimal cutting parameters as well as a set of conventional cutting parameters with the same MRR were employed to cut TiB2/Al,and the surface roughness of the specimens in this experiment was measured and compared.The results are shown in Table 7.From Table 7,we can see that with the same MRR,a better surface roughness can be achieved with the optimal cutting parameters.As revealed in previous studies,increasing in cutting speed will improve the surface roughness.This may be due to lower sideflow of material at higher cutting speed.On the other hand,the surface roughness increases with the increase of feed rate.

Fig.14 Pareto optimal solutions.

5.Conclusions and scope of future work

As a brand new material,the machinability of 6%TiB2/Al MMCs must be investigated for engineering applications.In order to identify the influence of in situ formed TiB2particles on machinability of MMCs,non-reinforced 7050 aluminum alloy was used as comparison.The influence of TiB2particles on machining force,residual stress and surface roughness was studied.A response surface model for surface roughness was generated.Further,the Pareto-based genetic algorithm was employed to optimize the multiple objectives in terms of MRR and surface roughness.The main conclusions of this study can be drawn as follows:

(1)The machining force of TiB2/Al MMCs is slightly bigger than the non-reinforced 7050 aluminum alloy.As the speed increased,the cutting and thrust force decreased rapidly for both materials.After a certain speed around 50 m/min,the machining force increased slowly.The machining force increased with the increase of feed rate.The forces of both materials are similar when the feed rate is low.But the machining force of TiB2/Al MMCs has a bigger increasing rate than that of the nonreinforced alloy.

(2)For all the conditions considered in this study,the residual stress of TiB2/Al MMCs is always compressive ranging from 100 to 300 MPa.The tendencies of residual stress of TiB2/Al MMCs are similar to those of the machining force.The residual stress of 7050 is nearly neutral and has little relationship with cutting speed and feed rate.It is important to notice that reverse results were observed in other papers.The reasons may be as follows:(1)the size of the reinforced particles;(2)the thermal softening behavior of these materials;(3)tool-particle interactions for TiB2/Al MMCs.

(3)With the increase of cutting speed,the surface roughness decreased at a high rate for both materials.After a certain cutting speed,very little further decrease is noted.At low feed rate,the surface roughness of TiB2/Al MMCs is smaller than that of 7050 aluminum alloy,but the surface roughness of TiB2/Al MMCs has a higher increase rate than that of 7050 aluminum alloy as the feed rate increased.Also,it is found that the feed marks are clear on TiB2/Al MMCs and irregular on 7050.This phenomenon is quite different from the experimental results in other papers.

(4)The response surface model of roughness developed in

this paper shows a high confidence level which was verified by a set of checking data.For the multi-objective optimization problem considering MRR and surface roughness,Pareto-based GA can be used as a powerful tool for parameter optimization.A set of Pareto solutions for surface roughness and MRR were achieved.

However,for this new material,a lot of work still needs to be done before a deep understanding of machinability is obtained for this new kind of material.The material removal mechanism,chip formation,and residual stress model will be studied in near future.

Table 7 Results of comparative experiment.

Acknowledgements

This study was co-supported by the National Natural Science Foundation of China(No.51505387),the China Postdoctoral Science Foundation funded project(No.2016M602860),and the 111 project(No.B13044).The authors would like to appreciate Prof.CHEN Dong for his inspiration and comments on this paper.

1.Cui Y,Wang LF,Ren JY.Multi-functional SiC/Al composites for aerospace applications.Chinese J Aeronaut2008;21(6):578–84.

2.Natarajan S,Naraynasamy R,Kumaresh B,Dinesh G,Kumar K,Sivapradad K.Sliding wear behavior of Al 6063/TiB2in situ compositesatelevated temperatures.MaterDes2009;30(7):2521–31.

3.Tham LM,Gupta M,Cheng L.Effect of limited matrixreinforcement interfacial reaction on enhancing the mechanical properties of aluminium-silicon carbide composites.Acta Mater2001;49(16):3243–53.

4.Irfan A,Zhang Q,Xiang M.Corrosion inhibition of SiCp/5A06 aluminum metal matrix composite by cerium conversion treatment.Chinese J Aeronaut2009;22(6):670–6.

5.Ai TT.Microstructure and mechanical properties of in-situ synthesized Al2O3/TiAl composites.Chinese J Aeronaut2008;21(6):559–64.

6.Lu L,Lai MO,Su Y,Teo HL,Feng CF.In situ TiB2reinforced Al alloy composites.Scripta Mater2001;45(9):1017–23.

7.Tayeh T,Douin J,Jouannigot S,Zakhour M,Nakhl M,Silvain J,Bobet J.Hardness and Young’s modulus behavior of Al composites renforced by nanometric TiB2elaborated by mechanosysnthesis.Mater Sci Eng A-Struct2014;591:1–8.

8.Wang M,Chen D,Chen Z,Wu Y,Wang F,Ma N,Wang H.Mechanical properties of in-situ TiB2/A356 composites.Mater Sci Eng A-Struct2014;590:246–54.

9.Pramanik A,Zhang LC,Arsecularatne JA.An FEM investigation into the behavior of metal matrix composites:tool-particle interaction during orthogonal cutting.Int J Mach Tool Manu2007;47(10):1497–506.

10.El-Gallab M,Sklad M.Machining of Al/SiC particulate metalmatrix composites Part I:tool performance.J Mater Process Tech1998;83(1–3):151–8.

11.Ding X,Liew WYH,Liu XD.Evaluation of machining performance of MMC with PCBN and PCD tools.Wear2005;259(7–12):1225–34.

12.Bhusham PK,Kumar S,Das S.Effect of machining parameters on surface roughness and tool wear for 7075 Al alloy SiC composite.Int J Adv Manuf Tech2010;50(5):459–69.

13.El-Gallab M,Sklad M.Machining of Al/SiC particulate metalmatrix composites Part II:Workpiece surface integrity.J Mater Process Tech1998;83(1–3):277–85.

14.Muthukrishnan N,Murugan M,Rao KP.Machinability issues in turning of Al-SiC(10p)metal matrix composites.J Mater Process Tech2008;39(3):211–8.

15.Joshi SS,Ramakrishnan N,Ramakrishnan P.Analysis of chip breaking during orthogonal machining of Al/SiCpcomposites.J Mater Process Tech1999;88(1–3):90–6.

16.Pramanik A,Zhang LC,Arsecularatne JA.Machining of metal matrix composites:Effect of ceramic particles on residual stress,surface roughness and chip formation.Int J Mach Tool Manu2008;48(15):1613–25.

17.Ding WF,Zhao B,Xu JH,Yang CY,Fu YC,Su HH.Grinding behavior and surface appearance of(TiCp+TiBw)/Ti-6Al-4V titanium matrix composites.ChineseJAeronaut2014;27(5):1334–42.

18.Li Z,Ding WF,Shen L,Xi XX,Fu YC.Comparative investigation on high-speed grinding of TiCp/Ti-6Al-4V particulate reinforced titanium matrix composites with single-layer electroplated and brazed CBN wheels.Chinese J Aeronaut2016;29(5):1414–24.

19.Anandakrishnan V,Mahamani A.Investigations offlank wear,cutting force,and surface roughness in the machining of Al-6061-TiB2in situ metal matrix composites produced byflux-assisted synthesis.Int J Adv Manuf Tech2011;55(1):65–73.

20.Senthil P,Selvaraj T,Sivaprasad K.Influence of turning parameters on the machinability of homogenized Al-Cu/TiB2in situ metalmatrixcomposites.IntJAdvManufTech2013;67(5):1589–96.

21.Siva SBV,Ganguly RI,Srinivasarao G,Sahoo KL.Machinability of Aluminum metal matrix composite reinforced with in-situ ceramic composite developed from mines waste colliery shale.Mater Manuf Process2013;28(10):1082–9.

22.Jiang RS,Wang WH,Song GD,Wang ZQ.Experimental investigation on machinability of in situ formed TiB2particles reinforced Al MMCs.J Manuf Processes2016;23:249–57.

23.Xiong YF,Wang WH,Jiang RS,Lin KY,Song GD.Tool wear mechanisms for milling in situ TiB2particle-reinforced Al matrix composites.Int J Adv Manuf Tech2016;86(9):3517–26.

24.Xiong YF,Wang WH,Jiang R,Lin KY,Song GD.Surface integrity of milling in-situ TiB2particle reinforced Al matrix composites.Int J Refract Met H2016;54:407–16.

25.Palanikumar K,Karthikeyan R.Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies.Mach Sci Technol2006;10(4):417–33.

26.Palanikumar K,Muthukrishnan N,Hariprasad KS.Surface roughness parameters optimization in machining A356/SiC/20pmetal matrix composites by PCD tool using response surface methodology and desirability function.Mach Sci Technol2008;12(4):529–45.

27.Sahoo AK,Pradhan S.Modeling and optimization of Al/SiCpMMC machining using Taguchi approach.Measurement2013;46(9):3064–72.

28.Muthukrishnan N,Davim JP.Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis.J Mater Process Tech2009;209(1):225–32.

29.Ramanujam R,Muthukrishnan N,Raju R.Optimization of cutting parameters for turning Al-SiC(10p)MMC using ANOVA and grey relational analysis.Int J Precis Eng Man2011;12(4):651–6.

30.Kishore DSC,Rao KP,Mahamani A.Investigation of cutting force,surface roughness andflank wear in turning of In-situ Al6061-TiC metal matrix composite.ProcMaterSci2014;6:1040–50.

31.Kishore DSC,Rao KP,Ramesh A.Optimization of machining parameters for improving cutting force and surface roughness in turning of Al6061-TiC in-situ metal matrix composites by using Taguchi method.Mater Today2015;2(4–5):3075–83.

32.Xavior MA,Adithan M.Determining the influence of cuttingfluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel.J Mater Process Tech2009;209(2):900–9.

33.Makadia AJ,Nanavati JI.Optimisation of machining parameters for turning operations based on response surface methodology.Measurement2013;46(4):1521–9.