Analysis of surface roughness and hardness in titanium alloy machining with polycrystalline diamond tool under different lubricating modes

Analysis of surface roughness and hardness in titanium alloy machining with polycrystalline diamond tool under different lubricating modes

Goutam Devaraya Revankar^I*; Raviraj Shetty^II; Shrikantha Srinivas Rao^III; Vinayak Neelakanth Gaitonde^IV

^IDepartment of Mechanical Engineering, Tontadarya college of Engineering, Gadag, Karnataka, India

^IIDepartment of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal, Karnataka, India

^IIIDepartment of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India

^IVDepartment of Industrial and Production Engineering, B. V. B. College of Engineering and Technology, Hubli, Karnataka, India

ABSTRACT

The present work deals with the investigation on machining of difficult-to-machine material titanium alloy (Ti–6Al–4V) using poly crystalline diamond (PCD) tool under different coolant strategies, namely dry, flooded and MQL. Taguchi technique has been employed and the optimization results indicated that MQL lubricating mode with cutting speed of 150 m/min, feed rate of 0.15 mm/rev, nose radius of 0.6 mm and 0.25 mm depth of cut is necessary to minimize surface roughness and dry mode with cutting speed of 150 m/min, feed rate of 0.15 mm/rev, nose radius of 0.6 mm and 0.75 mm depth of cut is necessary to maximize surface hardness. The results indicate the substantial benefit of the minimum quantity of lubrication (MQL) and justify PCD inserts to be the most functionally satisfactory commercially available cutting tool material for machining titanium alloys for better surface finish and hardness.

Keywords: titanium alloy (Ti-6Al-4V), polycrystalline diamond (PCD) insert, surface roughness, surface hardness, Taguchi optimization

Introduction

Titanium alloy (Ti-6Al-4V) has been generally used in modern manufacturing processes due to high strength-to-weight ratio at higher temperatures and superior corrosion resistance and thus finds extensive applications in aerospace, automotive, nuclear, chemical, marine and biomedical industries. Titanium alloy with low density, high specific strength, corrosion resistance and good process performance is the ideal structural material, especially for the aerospace engineering^1-3. However, titanium alloys have often been classified into difficult-to-machine materials because of low thermal conductivity. The lower modulus of elasticity of titanium leads to considerable spring back after deformation under cutting load; causing titanium parts to move away from cutting tool during machining, which leads to high dimensional inaccuracies in work pieces. The lower hardness of titanium and higher chemical reactivity leads to a tendency for galling of titanium with cutting tool and thus changing the tool geometry. The surface roughness of the workpiece is an important parameter, which influences the quality of components. The surface roughness is an estimate of technological quality of component and also indicator for evaluating the productivity of machine tools and machined parts. Desired value of surface roughness of a product is generally defined to achieve the required fatigue strength, corrosion resistance, precision fits, tribological and aesthetic requirements. Thus, measuring and characterizing the surface finish has been considered as the interpreter of machining performance.

Surface roughness prediction model in terms of cutting speed, feed rate and depth of cut using response surface methodology has been widely reported in literature^4-10. It was found that cutting speed and feed rate are the significant machining parameters affecting surface roughness, while the effect of depth of cut is found to be negligible. The use of higher cutting speed with lower feed rate produces a better surface finish, mainly due to high temperature^8,9. Azlan Mohd Zain et al.¹¹ investigated the effects of radial rake angle of tool, combined with cutting speed and feed on surface roughness. They reported that the cutting conditions should be set at highest cutting speed, lowest feed and highest radial rake angle to achieve the minimal surface roughness. Selvakumar et al.¹² used cermet inserts for finish turning of titanium alloy and observed remarkable effects of tool type and feed rate on surface roughness. Ramesh et al.¹³ conducted experiments on turning of titanium alloy (Grade-5) to study the effects of cutting parameters on surface roughness and found that the feed is the most influential factor affecting the surface roughness. Kali Dass and Chauhan¹⁴ quantified the effects of cutting speed, feed rate, depth of cut and approach angle on surface roughness and tangential force during titanium (Grade-5) alloy machining. The factorial design was utilized to obtain the best cutting conditions for minimization of surface roughness and tangential force. Ezugwu and Wang², Ribeiro et al.¹⁵, Rahman et al.¹⁶, Yang and Liu¹⁷ and Ezugwu¹⁸ identified the main problems associated with the machining of titanium and studied tool wear and the mechanisms responsible for tool failure. Ribeiro et al.¹⁵ performed turning tests on Ti–6Al–4V with conventional uncoated carbides. They observed certain coherence in the behavior of the titanium alloy in relation to variations in cutting parameters on tool wear and roughness produced in the work pieces.

Poor thermal conductivity of titanium causes concentration of extreme heat near the cutting edge, which in turn leads to speedy damage of cutting tool. The situation, thus, demands application of an inventive cooling method that would cause successful removal of heat to make implementation of higher cutting speeds viable. Dhar et al.¹⁹ investigated the role of minimum quantity of lubrication (MQL) on cutting temperature; chip formation and product quality during turning of AISI-1040 steel with uncoated carbide insert and the experimental results were compared with dry flooded machining. The MQL system has shown encouraging potentials for precision machining at low feed and high speed²⁰. The experimental research by Machado and Wallbank²⁰ indicated that the MQL enables considerable decrease in cutting temperature and dimensional inaccuracy depending upon the levels of cutting speed and feed rate. The results also showed that surface finish, chip thickness and force variation are all affected with low coolant volume when compared to flood cooling.

Gaitonde et al.²¹ performed experiments on turning of brass with K10 carbide tool to estimate the optimum amount of MQL and the most appropriate cutting speed and feed rate. Khan et al.²² compared the effects of dry, wet and MQL machining of AISI 9310 alloy steel on chip–tool interface temperature, chip formation mode, tool wear and surface roughness. Their investigation reveals that MQL machining is found to be better when compared to dry and wet machining due to substantial reduction in cutting zone temperature enabling favorable chip formation and chip–tool interaction. Vishal et al.²³ presented MQL, high pressure coolant (HPC), cryogenic cooling, compressed air cooling and use of solid lubricants/coolants techniques in turning, which resulted in reduction of friction and heat at the cutting zone, consequently leading to an improved productivity in the process. Venkata Ramana et al.²⁴ evaluated the machining performance and optimized the process parameters in turning of Ti-6Al-4V alloy using uncoated carbide tool with different coolant conditions for minimal surface roughness. Klocke et al.²⁵ reported that the machining efficiency with MQL could be enhanced when compared to dry and conventional flood machining. Ibrahim et al.²⁶ optimized the cutting parameters on surface roughness using Taguchi method in Ti-6Al-4V alloy turning with coated and uncoated cemented carbide tools under dry cutting condition and high cutting speed.

The conventional tools used for machining of titanium alloys include high speed steels and carbide tools. Due to low thermal conductivity of titanium alloys, these tools can only be used at relatively low cutting speeds. When machining at higher cutting speeds, these tools have a relatively short lifetime and hence frequent cutter regrinding is necessary. Oosthuizena et al.²⁷ found that the performance of conventional tool materials is poor during machining of Ti-6Al-4V at elevated speeds when compared to PCD tools. Nabhani²⁸ reported that the PCD tools have the lowest wear rate and produced better surface quality during titanium alloy machining when compared to the traditionally used tungsten carbide tools. Hence, PCD tool is an alternative to the traditional tungsten carbide grades for Ti-alloy machining. In order to attain higher cutting speeds for titanium alloys machining, the cutting tool should be able to suppress the heat generated in the cutting process as much as possible, while dissipating it quickly. The higher thermal conductivity of PCD could therefore perhaps allow for higher cutting speeds to be achieved²⁹. Brinksmeier and Riemer³⁰ reported that the PCD tools exhibit better tool life than the CBN and tungsten carbide during machining of Ti alloys. Ram Cherukuri et al.³¹ and Ezugwu et al.³² found a substantial improvement in tool life during machining of Ti-6Al-4V alloy with PCD tools. Mori et al.³³ also found that the PCD tools have a longer tool life, especially at higher cutting speeds when compared to cemented carbide tools. Michiko et al.³⁴ explored the possibility of improvement in cutting efficiency using PCD tool having high thermal conductivity during titanium alloy machining. Ezugwu et al.³⁵ reported that surface finish generated during machining Ti–6Al–4V with PCD tools is generally acceptable and free of physical damages such as tears, laps or cracks for all cutting conditions investigated. PCD would be the most functionally satisfactory commercial available cutting tool material for machining titanium alloys in comparison to carbide and PCBN tools²⁸. Some of the more important features and advantages of polycrystalline diamond are the higher cutting removal rates (self sharpening abrasive), uniform surface finish, more uniform particle size distribution, harder/ tougher particles, blocky shaped particles, hexagonal microcrystallites (equally hard in all directions) and surface area 300% greater than monocrystalline diamond. In view of the above, PCD tool is preferred in our investigations on Titanium alloy machining.

Taguchi design is an important tool for robust design, which offers a simple and systematic approach to optimize a design for performance, quality and cost^36,37. Taguchi developed the procedures, which apply orthogonal arrays of statistically designed experiments to obtain the best model with minimum number of experiments and thus reducing the time and cost of experimentation.

As seen from the literature, most of the researchers used the cutting conditions such as cutting speed, feed and depth of cut as input parameters. But as per authors' knowledge, no methodical research work has been reported in the literature to determine the best lubricating mode along with appropriate cutting conditions for achieving better surface quality and surface hardness using poly crystalline diamond (PCD) inserts as the cutting tool. Interestingly, tool nose radius, one of tool geometry parameters, has not been scientifically investigated, most likely due to its spontaneous effects on part surface finish. Hence, an attempt has been made in this paper to find the optimum process parameters lubricating mode, cutting speed, feed rate, nose radius and depth of cut during turning of Ti-6Al-4V alloy using PCD tool so as to minimize the surface roughness and maximize surface hardness using Taguchi method.

Taguchi Method

Taguchi procedure is used for finding the optimal levels of the control parameters to make the product or process insensitive to noise factors^36,37. Taguchi method is based on orthogonal arrays (OA); allow the simultaneous effect of numerous process parameters to be studied proficiently. The purpose of conducting an orthogonal experiment is to decide the optimum level for each of the process parameters and to establish the relative significance of individual parameter on quality characteristic^36,37.

Taguchi suggests signal to noise (S/N) ratio as the objective function for orthogonal matrix experiments. The S/N ratio is used to measure the quality characteristics and indicates the degree of predictable performance in occurrence of noise factors. Taguchi classifies the S/N ratio into smaller the better type, larger the better type and nominal the best type based on type of objective function. The analysis of means (ANOM) based on S/N ratio is used to determine the optimal levels of the control factors. The optimum level for a factor is the level that results in the highest value of S/N ratio in the experimental region. The analysis of variance (ANOVA) in Taguchi parameter design establishes the relative significance of control factors and is performed on S/N ratios to obtain the percent contribution of each of the process parameters.

Experimental Details

In the current study, five parameters, namely, lubricating mode, cutting speed, feed rate, nose radius and depth of cut were identified. The ranges for feed rate and depth of cut were selected based on the recommendations given by the insert manufacturer. The highest value of the cutting speed and the ranges of other parameters were selected after preliminary tests. Each parameter was investigated at three levels to study the non-linearity effect of the process parameters. The identified control factors and their levels are given in Table 1. 25 mm diameter bars of titanium alloy (Ti-6Al-4V) were used as work materials (Figure 1). Ti-6Al-4V work material is an (a+b) of aerospace Grade 5, the chemical composition of the work material is given in Table 2. The PCD insert and tool holder with an ISO designation of CCMT09T304 (Figure 2) was used to machine the Ti-6Al-4V work pieces; the tool geometry of PCD insert is shown in Figure 3 and the image of the PCD insert is shown in Figure 4.

According to Taguchi quality design concept, for three levels and five factors, a standard L₂₇ orthogonal array (OA) was selected as exhibited in Table 3. The turning tests were performed as per OA on 'Ace Turn mill CNC Fanuc lathe', which is equipped with 11 kW spindle power and a maximum spindle speed of 4000 rpm.

To control the temperature during cutting for better surface finish, different lubrication systems are applied. Three different types of lubricating modes used in the present study are dry, flooded and MQL. Palm oil (viscosity index of 190) having density 0.91 gm/cm³ and viscosity of 40 mm²/s at 40°C is used as lubricant in MQL lubricating mode, whereas, for flood cooling, 5% water emulsion of Vasco 1000, a commercially available water miscible, vegetable oil based cutting fluid was used. This fluid is free from phenol, chlorine and other additives. In MQL type application, the experiments were conducted using a thin-pulsed jet nozzle and controlled by a variable speed control drive. The MQL setup employed in the current investigation is shown in Figure 5. It consists of a reservoir of 2 liters capacity and a pneumatic piston pump to inject oil. A filter regulator is fitted in air line to regulate air used in the MQL set up and an oil filter cum air breather to filter oil with 149 micron. A pressure switch is used to make sure that required air pressure is coming to system. The solenoid valve is used for working of pneumatic piston pump and an air regulator to control air pressure in both the lines. The Electronic Timer B1DCA-X is a cyclic ON-OFF adjustable timer with time range from 0.6 secs to 60 mins (8 ranges) to control the frequency of oil piston pump. The discharge from the pump is at the rate of 0.40cc/stroke. The intervals between two strokes and duration of stroke can be adjusted to get the desired discharge. The nozzle is attached to a portable fixture at the machining center spindle. The flexible design allowed the injection nozzle to be located at any desired position without interfering with the tool or work piece during the machining process. The diameter of nozzle orifice is 1 mm and the delivery pressure is set at 4 kgf/cm². The direction of applying fluid nozzle in MQL system is set opposite to the feed direction. However, in the flood type application, the flood fluid was delivered through three nozzles around the tool at the rate of 8,000 ml/min. The various lubricating modes, namely, dry; MQL and flood lubrication employed for turning Ti-6Al-4V are shown in Figures 6-8 respectively.

To measure the machined work piece surface roughness, a portable surface roughness tester 'Mitutoyo, Japan Surftest SJ- 400' was used with a cut off length of 0.8 mm. The surface roughness used in this study is an arithmetic mean average surface roughness (R_a). Each trial was repeated three times and an average reading was used for the analysis.

The hardness was measured using computerized microvickers hardness testing machine (Model VM50 50PC). An average of six readings was taken at different regions along the specimen at a static load of 500 gm to obtain the value of mean hardness (H). The measured values of surface roughness and hardness are summarized in Table 3.

Results and Discussion

ANOM and ANOVA

In the present work, the objective is to minimize the surface roughness and maximize the surface hardness. Hence, "smaller the better type" category is used for surface roughness and "larger the better type" category for surface hardness have been selected. The S/N ratio associated with the objective functions for each trial of the OA is given by:

The corresponding S/N ratios for each trial of L₂₇ orthogonal array were determined using Equations 1 and 2 for surface roughness and surface hardness respectively and are presented in Table 3 which gives the combinations of experimental machining parameters and parameter levels in the L₂₇ orthogonal array (OA).A total of 27 experiments were conducted in accordance with the parameter level of each factors and observed values of surface roughness and surface hardness were noted, which were further converted to S/N ratio. Table 3 helps to find the optimal combination level of the machining parameters and the degree to which the machining parameters affect the observed values were evaluated.

The analysis of means (ANOM) based on S/N ratio³⁶ was carried out to determine the optimal levels of control factors; the results of ANOM for surface roughness and hardness are represented in Figures 9 and 10 respectively. The level of a parameter with the highest value of S/N ratio is the best combination level. The optimal parameter setting is found to be MQL lubricating mode (A2), high cutting speed of 150 m/min (B3), lowest feed rate of 0.15 mm/rev (C1), higher nose radius of 0.6mm (D3) and lowest depth of cut 0.25mm (E1) for minimum surface roughness and dry mode (A3), high cutting speed of 150 m/min (B3), lowest feed rate of 0.15 mm/rev (C1), higher nose radius of 0.6mm (D3) and highest depth of cut 0.75 mm (E3) for maximum surface hardness.

To examine the effects of control factors quantitatively, the analysis of variance (ANOVA) based on S/N ratio³⁶ has been performed. The ANOVA is accomplished by separating total variability of S/N ratio, which is measured by sum of squared deviations from total mean of S/N ratio into contributions by each of the factors and the error³⁶. The summary of ANOVA results for surface roughness and surface hardness are shown in Table 4 and Table 5 respectively. It can be seen from the ANOVA (Table 4) that the feed rate (72.32%) and cutting speed (17.49%) have major contributions, whereas lubricating mode (7.87%) has significant role in minimizing the surface roughness. On the other hand, nose radius and depth of cut have the least effects in minimizing the surface roughness. It is clear from ANOVA results of Table 5 that the lubricating mode (89.27%) and cutting speed (5.28%) are the major contributors, whereas feed rate, nose radius and depth of cut play less significant roles in maximizing the surface hardness. The validation experiments were performed at the optimal levels of the control factors and the prediction error is found to be within the 95% confidence limit; indicating the adequacy of the additivity of the proposed surface roughness and hardness models. The best combinations of the control factors for minimizing the surface roughness and maximizing the hardness along with the corresponding optimal values are presented in Table 6.

The main effect plots (Figures 9 and 10) are generated using MINITAB statistical software³⁸ for exploring the effects of control factors on surface roughness and hardness.

Analysis of surface roughness

Effect of lubricating mode

From Figure 9, it is observed that, the surface roughness is low for MQL machining when compared to dry and flooded conditions. For flooded lubricant conditions, the cutting fluid supplied at high pressure and velocity penetrates the minute particles into tool-chip and tool-work piece surfaces, causing reduction in friction and hence leading to less surface roughness. On the other hand, MQL machining provides both cooling and lubrication effectively. The cooling provides convective as well as evaporative heat transfer and hence less surface roughness is observed in MQL machining when compared to flooded lubrication²⁰. Further, in flooded condition, an effective penetration of the cutting fluid into tool-chip as well as tool- work surface is not possible along with convective heat transfer. Hence, surface roughness is more in flooded when compared to MQL condition. On the other hand, in dry machining, no cutting fluid is supplied; resulting into high friction, high tool wear and low heat transfer, which in turn leads to high surface roughness. Finally, it can be concluded that MQL machining provides better performance in reduction of surface roughness compared to dry and flooded lubricant condition. Hence, it is recommended to implement MQL machining in order to improve surface finish, reduction in quantity of lubricant, cost and environmental pollution.

Effect of cutting speed

From ANOVA analysis (Table 4), it can be seen that cutting speed has noticeable contribution (17.49%) in minimizing the surface roughness. From Figure 9, it is seen that the surface roughness of the machined component decreases with increased cutting speed. This is due to the fact that, high spindle speed is associated with the higher cutting temperature; increasing the softening of the work piece material and then reduces the cutting forces and hence leading to better surface finish. A similar result was also reported by Che-Haron and Jawaid³⁹ during machining of Ti‑6Al-4V alloy with 883 inserts under dry cutting conditions where low surface roughness was obtained with the increase in cutting speed. In addition, at higher spindle speed, the chip will break away with less material deformation at the immediate tool tip, which in turn preserves the machined surface properties leading to minimal surface roughness. However, it is believed that the spindle speed should be controlled at an optimum value, as the influence of high temperature would significantly affect the chip formation mode, cutting forces, tool life and surface roughness. The surface roughness could be improved by increasing cutting speed, though the improvement is very limited at higher cutting speed (100-150 m/min). Producing an enhanced surface finish at elevated cutting speed is eminent in metal cutting. The conventional explanations are related to built-up-edge (BUE); i.e., the formation of BUE is favored in a certain range of cutting speed. By increasing cutting speed beyond this region, BUE is eliminated and as a result, the surface finish is improved. During our current investigations on Ti-6Al-4V alloy machining, the cutting speeds are higher than those favoring BUE formation. According to Chen⁴⁰ and Bouacha et al.⁹, the deformation velocity influences the properties of the metals. Higher the velocity, less important the plastic behavior is. If the material presents less plasticity by increasing cutting speed and hence deformation velocity, the surface finish can be improved as a result of less significant lateral plastic flow and thus less additional increase in the peak-to-valley height of the machined surface roughness. In addition, at low cutting speed, grooves are developed on the tool wear face. Larger the development of the grooves, the more significant deterioration of the surface finish takes place.

Effect of feed rate

From ANOVA (Table 4), it is seen that feed rate has the major contribution (72.32%) in minimizing the surface roughness. In general, as feed rate increases, the surface roughness also increases for dry, flooded and MQL conditions. However, MQL shows reduction in surface roughness when compared to dry and flooded condition under different feed rates due to the MQL delivery pressure applied, which in turn will remove chips (debris) from the cutting zone. As can be seen from Figure 9, as the feed rate increases, the surface roughness also increases because of less available time to carry out the heat from the cutting zone, high amount material removal rate and an accumulation of chip between the tool-work piece zones.

Effect of nose radius

It is clear from the main effect plots of surface roughness based on S/N ratio (Figure 9) that the surface roughness decreases with increased tool nose radius. As tool nose radius increases, the contact length between tool and work piece increases; diminishing the height of feed marks and therefore, diminishing the surface roughness. As predicted theoretically, surface roughness decreases with increased tool nose radius. It is also noticed that departure from the theoretical prediction (R_th= f ²/8r) tend to be significant at low feed rates. This is due to ploughing actions caused by smaller uncut chip thickness. Large nose radius tools have somewhat better surface finish than small nose radius tools during the entire cutting period. The tool nose radius is very critical part of the cutting edge since it produces the finished surface, if the nose is made to a sharp point, the finish machined surface will usually be unacceptable and the life of the tool will be short.

Effect of depth of cut

It is quite evident from Figure 9 that the surface roughness increases with increased depth of cut, mainly due to increase in thermal load and vibration on the machine tool. Further, due to more contact area between tool and work piece, high friction and tool wear exist and hence leading to high surface roughness. Colafemina et al.⁴¹ conducted several experiments on Ti-6Al-4V alloy machining and established relationship between depth of cut and roughness. They recommended low depth of cut to reduce the chatter, which in turn subsequently leading to good surface finish. Our findings also closely agree with the experimental results reported in the above literature.

Analysis of hardness

During machining, the surface and immediate sub-surface of the material become harder due to work hardening. The effect of internal work hardening is determined by the temperature, time and mechanism of internal stress relaxation. The internal work hardening accumulation for heating occurs with the engagement of tool for cutting the workpiece material and accumulation for cooling occurs with the disengagement of tool from the workpiece material.

The hardness values are averaged over 7-8 indents per specimen (Figure 11). The hardness value of the surface is much higher than the bulk material hardness and it takes 210 µm deep into the bulk material for the hardness value to level. At 210 µm beneath the machined surface, the difference in hardness was very small and the hardness values approached the hardness of the base material as the depth beneath the machined surface increased. The hardness of titanium material before machining was 285 Hv and after carrying out the machining for different trials, the hardness varied between 311 Hv (minimum) to 347 Hv (maximum). Work hardening of deformed layer beneath the machined surface up to 200 mm caused higher hardness than the average hardness of the base material. The top layer of the machined surface experiences work hardening process and hence the hardness is higher than the average hardness of the work piece material. However, the material beneath the top layer is softer as a result of over-aging of titanium alloy as a result of very high cutting temperature produced at the local surface. The low thermal conductivity of titanium alloy also caused the temperature below the machined surface to be retained.

It is revealed from the investigations of Ezugwu and Tang⁴² that the combination of high compressive stresses and pressure at the cutting edge during machining contributed to the occurrence of the work hardening effect. Additionally, rapid heating and cooling may have contributed to the work hardening effect during machining⁴³. From the experimental work of Ramakrishna and Shunmugam⁴⁴_, it is seen that the depth of the work hardening layer varies depending on the type of mechanical and thermal interaction. According to Zou et al.⁴⁵ the evolution of microhardness of the machined surfaces was influenced by cutting speed, feed rate and depth of cut during turning NiCr20TiAl nickel-based alloy.

Effect of lubricating mode

From Figure 10, it is noticed that hardness is highest at the surface level for dry machining due to large amount of heat generated when compared to MQL and dry machining. During dry machining as the workpiece material is subjected to high cutting temperature and high cutting pressure, a competing process between work hardening and thermal softening takes place and affects the fundamental behavior of the workpiece material. Moreover, according to the work of Lapin et al.⁴⁶, the softening process of the sub‑surface region can be characterized by the effect of ageing on microhardness. The machined surface subjected to high cutting temperature during machining process is similar to the ageing process. From this discussion, it can be concluded that the instability or alteration of microstructure in the form of plastic deformation caused by high temperature during dry machining leads to the softening of the titanium alloy sub-surface (metallurgical alteration). However, the MQL is seen to induce lower softening at the outer layers of the ground surfaces. Generally, the hardening effect is due to high plastic flow rate combining with the heat generation at the primary shear zone³⁵. Flood lubrication increases the access of the coolant to the chip‑tool interface and contributes to reducing friction coefficient and the resistance to primary shear stress³⁵. Heat generation is decreased and consequently lower temperatures and plastic flow, resulting in lesser hardening effect as well as micro structural damage²⁹ .Softening of the machined surface implies improved ductility and yield strength of the Ti‑6Al–4V alloy, thereby improving process ability.

Effect of cutting speed

It is observed from main effect plot of Figure 10 that, as the cutting speed increases the surface hardness increases. This may most likely due to increase in the cutting force that occurs for increased cutting speeds. Also an increase in cutting speed produces an increased cutting temperature, which in turn increases the temperature on the machined surface. These changes generate a sticking friction condition between the tool-work interfaces; thus contributing to an increase in subsurface plastic flow, giving a higher hardness value. Similar observation was reported by Grzegorz et al.⁴⁷ during duplex stainless steel machining.

Effect of feed rate

From Figure 10, it is clear that the hardness value does not vary much with the feed rate. Again from ANOVA (Table 5), it is also seen that the contribution of feed rate towards hardness is almost negligible for maximizing hardness. Hence, it can be concluded the hardness value is almost independent of feed rate.

Effect of nose radius

From ANOVA Table 5, it is observed that nose radius has minor contribution effect in minimizing surface hardness. The increase in nose radius (Figure 10) has a direct effect on cutting forces; leading to a significant increase in the ploughing effect in the cutting zone. Increasing the ploughing force leads to more material flow on the machined surface thereby increasing the surface hardness. Increasing the ploughing effect leads to more material side flow on the machined surface. A large nose radius results in to generation of compressive residual stress beneath the machined surface⁴⁸.

Effect of depth of cut

It is clear from Figure 10 that, the surface hardness value increases with the increase in depth of cut because of increased cutting forces. The ANOVA analysis from Table 5 also reveals that the depth of cut is a significant parameter affecting the surface hardness.

SEM analysis

After machining, the plate form sample was produced from 'Electronica (Maxi cut)' the wire electro discharge machining (WEDM); the specimen is shown in Figure 12. The microstructure of the machined surface of titanium work-piece was obtained for each machined sample by using scanning electron microscope (SEM). The microstructure of each machined sample was obtained in order to perform a detailed study of the machined surface.

Figures 13-15 show the SEM images of Ti-6Al-4V under different lubricant strategies. Figure 13 depicts the surface generated under dry mode with cutting speed of 50 m/min, feed of 0.35 mm/rev, nose radius of 0.4 mm and depth of cut of 0.25 mm and the recorded surface roughness of 3.25 mm. The higher surface roughness is due to dry mode and high feed rate of 0.35 mm/rev. The surface roughness measured under flood lubrication with cutting speed of 150 m/min, feed of 0.35 mm/rev, nose radius of 0.6 mm and depth of cut of 0.25 mm is 2.65 mm as shown in Figure 14.This is because of high feed rate and high nose radius. Figure 15 shows the surface generated with a better surface finish of 0.89 mm under MQL condition with cutting speed of 150 m/min, feed of 0.15 mm/rev, nose radius of 0.4 mm and depth of cut of 0.5 mm. The better surface finish is attributed due to MQL condition, low feed and high cutting speed.

A large number of defects were observed on the surface during the experimental trials conducted. The SEM images of the machined surfaces show that micro-pits and re-deposited work material were the main damages to the surfaces. However no damage on the surface like tears, laps or cracks was observed when machining Ti–6Al–4V with PCD tools. Micro-structural examination of the machined surfaces revealed no plastic deformation after finish machining at the cutting conditions investigated.

Conclusions

Taguchi optimization method for titanium alloy (Ti-6Al-4V) machining with poly crystalline diamond (PCD) tool for minimizing the surface roughness and maximizing surface hardness is presented in the paper. Based on the analysis of the experimental results, the following conclusions are drawn:

• A combination of MQL lubricating mode, high cutting speed, low feed rate, high nose radius with low depth of cut is helpful for achieving the minimal surface roughness during turning of titanium alloy;

• The cutting speed (72.32%) and feed rate (17.49%) have major effects on minimizing surface roughness. The lubricating mode also plays vital role in minimizing the surface roughness;

• Reduced surface roughness is obtained for MQL machining when compared to dry and flooded conditions;

• The surface roughness decreases with increased cutting speed and nose radius, whereas the surface roughness increases with increased feed rate and depth of cut;

• Work hardening of deformed layer beneath the machined surface up to 100 µm caused higher hardness than the average hardness of the base material. However, the hardness of the subsurface at 200 mm below the machined surface was less than the average hardness recorded for the base material;

• The lubricating mode (89.27%) and cutting speed (5.28%) have key roles on maximizing the surface hardness;

• The hardness is more at the surface level in dry lubrication due to large amount of heat generated when compared to MQL and flooded lubrication;

• PCD insert was successfully used as a cutting tool material for machining titanium alloys for better surface finish.

Acknowledgements

The authors would like to thank the Government Tool Room and Training Centre, Hubli, Karnataka, India for providing the necessary facilities to carry out the turning experiments on Titanium alloy.

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Received: January 23, 2014; Revised: July 7, 2014

Publication Dates

• Publication in this collection
  15 Aug 2014
• Date of issue
  Aug 2014

History

• Accepted
  07 July 2014
• Received
  23 Jan 2014