The use of titanium based alloys in aerospace and biomedical applications make them an attractive choice for research in micro-machining. In this research, low speed micro-milling is used to analyze machinability of Ti-6Al-4V alloy as low speed machining setup is not expensive and it can be carried out on conventional machine tools already available at most machining setups. Parameters like feed per tooth, cutting speed and depth of cut are selected as machining variables and their effect on burr formation is analyzed through statistical technique analysis of variance to determine key process variables. Results show that feed per tooth is the most dominant factor in burr formation (81 % contribution ratio). The effect of depth of cut was found to be negligible. It was also observed that micro-milling at optimum process parameters showed minimum burr formation. In terms of burr formation, as compared to high speed machining setup, better results were achieved at low speed machining setup by varying machining parameters.
Schematic of macro and micro-milling process (adapted from Schaller et al., 1999).
Micro-machining is a process used for manufacturing miniaturized parts and features (Jaffery et al., 2016). Demand for highly precise and accurate micro components is rising day by day in the industries such as biomedical, aerospace, automotive and electronics (Chae et al., 2006; Jaffery et al., 2016; Kuram and Ozcelik, 2013). This miniaturization technology will provide micro systems to improve health care, quality of life and economic growth in such applications as micro channels for shape memory alloy “stents”, sub-miniature actuators and sensors, fluidic graphite channels for fuel cell applications, lab-on-chips and medical devices (Chae et al., 2006; Jaffery et al., 2016). There are a few unconventional machining processes which could be used to produce micro scale components such as focused ion beam cutting (Kuram and Ozcelik, 2013), micro-laser machining, micro-forming and micro electro-discharge machining (micro EDM) (Ucun et al., 2013) but these machining processes have limitations either because of producing two dimensional (2-D) microstructures or high costs of manufacturing/setup cost (Kuram and Ozcelik, 2013; Ucun et al., 2013). Other than these machining processes, micro-milling is another non-traditional machining technique which is capable of fabricating miniaturized three dimensional (3-D) complex parts/features (Ali et al., 2012; Özel et al., 2011; Thepsonthi and Özel, 2013). Although micro-milling is considered better than other non-traditional machining operations in terms of high material removal rate, process flexibility, low set up cost and producing complex shapes but it is also accompanied with some problems such as burr formation, low surface quality, unpredictable tool breakage and rapid tool wear (Özel et al., 2011; Thepsonthi and Özel, 2013). Therefore, many factors such as tool vibration (Jaffery et al., 2016; Thepsonthi and Özel, 2012), subsurface plastic deformation (Jaffery et al., 2016) and material microstructure (Thepsonthi and Özel, 2012) which are not taken into consideration at macro-level become significant at micro level (Thepsonthi and Özel, 2013) so, it becomes difficult to obtain desired results in micro-milling than that of macro-milling (Thepsonthi and Özel, 2012, 2013). Micro-milling becomes much more difficult when hard to cut materials are used for manufacturing such as Ti-6Al-4V titanium alloy (Thepsonthi and Özel, 2013). Ti-6Al-4V is known by the name “workhorse” of the titanium industry as it accounts for more than 45 % of actual titanium usage (Jaffery et al., 2016). It is widely used in biomedical (medical implants) (Thepsonthi and Özel, 2012), aerospace (turbine blades, aerospace fasteners) (Thepsonthi and Özel, 2012) and automotive (connecting rods, engine and exhaust valves) (Wagner and Schauerte, 2007) and marine industry (Ezugwu and Wang, 1997) due to its high strength to weight ratio (Bajpai et al., 2013; Ezugwu and Wang, 1997; Jaffery et al., 2016; Kim et al., 2014; Mhamdi et al., 2012; Thepsonthi and Özel, 2012), property to withstand high temperature (Ezugwu and Wang, 1997; Mhamdi et al., 2012), biocompatibility (Jaffery et al., 2016; Kim et al., 2014; Thepsonthi and Özel, 2012) and corrosion resistance (Ezugwu and Wang, 1997; Kim et al., 2014; Mhamdi et al., 2012; Thepsonthi and Özel, 2012). On the other side there are properties which make it difficult to cut material such as low thermal conductivity (Ezugwu and Wang, 1997; Kim et al., 2014; Mhamdi et al., 2012; Thepsonthi and Özel, 2012), high chemical reactivity (Ezugwu and Wang, 1997; Mhamdi et al., 2012) and low elastic modulus (Ezugwu and Wang, 1997). Syed Husain Imran Jaffery observed that tool wear increases during the machining of Ti-6Al-4V alloy due to high chemical reactivity of titanium with cutting tool material and low thermal conductivity which leads to tool fracture as most of the heat generated goes into the cutting tool (Jaffery and Mativenga, 2009).
Micro-milling seems to be a scale down machining operation of macro-milling
but there are differences between these processes. In micro-milling, diameter
of cutting tool (
In macro-milling feed per tooth (
Figure 2 shows there are four situations that take place during the process
of micro-milling. When depth of cut (
Micro-milling tool workpiece interaction (adapted from Jaffery et al., 2016).
Mechanical machining is always accompanied by burr formation either it is macro-machining or micro-machining. Deburring in macro-machining is easy as compared to micro-machining. In micro components, deburring may destroy delicate micro features as well as it can damage the workpiece. Moreover, cost of deburring process is very high as it requires complex assembly operation (Kim et al., 2014; Mian et al., 2010). Therefore, it is undesirable to use deburring process to remove burr and the recommended approach would be to select the appropriate machining parameters and tool geometry to reduce burr formation (Thepsonthi and Özel, 2012).
Syed H Imran Jaffery statistically analyzed the effect of feed rate (below and above cutting tool edge radius), cutting speed and depth of cut on burr formation in micro-machining of Ti-6Al-4V and found that feed rate was the most contributing factor causing burr formation and residual effects were more significant when feed rate was selected below edge radius (Jaffery et al., 2016). Tuǧrul Özel investigated the effect of cBN coated tools on burr formation and surface roughness in micro-milling of Ti-6Al-4V by varying process parameters and found that increasing feed rate reduced burr formation and cBN coated tools showed less burr formation than uncoated tools (Özel et al., 2011). Vivek Bajpai performed micro-milling experiments on Ti-6Al-4V alloy and observed that size of side exit burr was larger among all other burr types. It was also found that increase in chip load, cutting speed and depth of cut results in better surface finish (Bajpai et al., 2013). Aamer J. Mian conducted micro-milling experiments on AISI 1005 steel and AISI 1045 steel and determined that best surface finish was achieved when feed rate was just close to cutting tool edge radius (Mian et al., 2010). Gandjar Kiswanto studied the effect of machining parameters on surface roughness and burr formation in the micro-milling of Aluminum alloy 1100 and found that surface roughness increased by increasing feed rate and also concluded that in order to reduce the burr formation up milling cutting strategy should be used as down milling cutting strategy produces bigger and wavier burrs (Kiswanto et al., 2015). Kiha Lee investigated burr formation in micro-milling of aluminum and copper and found that for micro-milling tool, exit and entrance burr was bigger in size than macro milling, considering burr size to chip load ratio (Lee and Dornfeld, 2002). Kiha Lee also explained that tool wear relates with burr height and it is proportional to feed rate. Size effect also plays an important role in burr formation and with the increase in edge radius of the cutting tool larger burrs are produced (Lee and Dornfeld, 2005). Aamer Jalil Mian conducted micro-milling experiment on NiTi alloy and found that feed rate to undeformed minimum chip thickness is the most critical factor in reducing burr root thickness. At high values of chip load, cutting starts earlier as compared to low undeformed chip thickness, as a consequence of this, ploughing effect becomes less pronounced (Mian et al., 2011b). Ravi Lekkala analyzed the burr formation in micro end milling of Aluminum and Steel using experimental and theoretical techniques and reported that either in down milling or up milling, burr height increases with the decrease in number of flutes and cutting tool diameter and burrs formed in the case of stainless steel were larger than aluminum (Lekkala et al., 2011). Sinan Filiz investigated micro-machinability of copper 101 and found that feed rate is directly proportional to surface roughness and inversely proportional to burr formation and cutting speed is directly proportional to burr formation and at higher feed rates the process is dominated by shearing which results in decrease in burr formation as compared to ploughing (Filiz et al., 2007). Fengzhou Fang suggested that after defining cutting and other machine parameters; tool sharpness and undeformed chip thickness are the most critical factors to determine burr height (Fang and Liu, 2004).
The review of literature shows that burr formation in micro-machining of different materials has been studied extensively and most of the research done on micro-milling of Ti-6Al-4V alloy is in the region of high speed machining (10 000–90 0000 rpm) (Bajpai et al., 2013; Chen et al., 2012; Jaffery et al., 2016; Jaffery and Mativenga, 2009; Kim et al., 2014; Özel et al., 2011; Thepsonthi and Özel, 2012, 2013). However, no research has been reported on micro-milling of Ti-6Al-4V alloy below 10 000 rpm. The reason for this is that high speed machining setup provides better surface finish, high material removal rate and reduced cutting forces (Bajpai et al., 2013; Jaffery et al., 2016; Jaffery and Mativenga, 2009; Thepsonthi and Özel, 2012, 2013). On the other hand, low speed machining setup is not expensive and can be carried out on conventional machine tools already available at most machining setups. Moreover, researchers have reported that in high speed micro-milling of Ti-6Al-4V, burr formation is more effected by change in feed per tooth rather than cutting speed (Jaffery et al., 2016; Özel et al., 2011; Thepsonthi and Özel, 2012, 2013). Therefore, using carbide tools for micro-milling of Ti-6Al-4V, this study aims to find out effect of critical machining parameters namely, feed rate, depth of cut and low/conventional cutting speed below 10 000 rpm (Bajpai et al., 2013) on burr formation and use statistical technique to find out best combination of key process variables (KPVs) to minimize burr formation.
The material selected for experimentation is Ti-6Al-4V alloy. It is an
alpha-beta (
The chemical composition, mechanical properties and physical properties are given in the Tables 1, 2 and 3 respectively.
Chemical composition of Ti-6Al-4V (wt %).
Mechanical properties of Ti-6Al-4V.
Physical properties of Ti-6Al-4V.
The tests were conducted using a FANUC MV-1060 conventional speed machining
center. Figure 3 shows experimental setup. Relative motion between micro
end-milling tool and workpiece was controlled by FANUC 0i-MC motion
controller. The tools used during micro-milling experiments of Ti-6Al-4V
were ultrafine tungsten carbide tools (North Carbide Tools). The average
edge radius of micro tools was found to be 4.0
Experimental Setup (1) Spindle; (2) Micro end mill; (3) Workpiece; (4) Machining vise; (5) Magnified view of tool and workpiece.
Experimental Conditions.
Micro-milling experiments were designed based on Taguchi's Design of
Experiments. The array selected was L9 orthogonal array with three factors
and three levels. Three independent factors of feed per tooth, cutting speed
and axial depth of cut were considered. Syed H. Imran Jaffery in his research
found that residual effects were more significant when feed per tooth was
selected below tool edge radius (Jaffery et al., 2016). Therefore, to
minimize the effect of residual effects feed rate was selected above tool
edge radius between 8 and 12
Process Parameters.
The spindle revolutions per minute (
In micro-milling different types of burrs such as top burr, exit burr, entrance burr and bottom burr are formed depending on direction of cutting and tool-workpiece interaction (Aurich et al., 2009; Kiswanto et al., 2015). Exit burr and side burr is the burr which remains attach to the surface machined by minor cutting edge of the tool and major cutting edge of the tool respectively (Aurich et al., 2009). Top burr is the burr which remains attach to the top surface of workpiece (Aurich et al., 2009). During micro-milling process, chips generated move upward along the rake face of the cutting tool and the material that is in contact with the chips and workpiece pulls apart under large tensile stress. A part of this deformed material remains attach to the top surface and not taken away with the chips as a result of this top burrs are initiated (Chen et al., 2012). Figure 4 illustrates how top burr width is measured and it can be described as horizontal length of burr from groove wall (Thepsonthi and Özel, 2012). Table 6 shows results in the form of Taguchi orthogonal L9 array. Figure 5 shows burr formation on down milling and up milling side during micro-milling and it can be seen that size of burr is larger at down milling side. This is due to the fact that velocity of localized cutting edges on the side of up milling side would always be greater than on the side of down milling, therefore causing larger burrs to form on down milling side as also observed by Syed H Imran Jaffery (Jaffery et al., 2016). To account for worst case scenario down milling side burrs were taken into consideration. Maximum value of top burr width was measured for each run and to measure that scanning electron microscope (SEM) was utilized.
Taguchi orthogonal array (L9 array) for micro-milling of Ti-6Al-4V.
Top burr width measurement (adapted from Thepsonthi and Özel, 2012).
Burr formation at down milling and up milling side during
micro-milling of Ti-6Al-4V (
Main effects plots for top burr width with respect to machining parameters (feed, speed and depth of cut).
ANOVA table for significance and contribution ratio of parameters (feed, speed and depth of cut).
After obtaining results of top burr width using SEM, ANOVA was used for
statistical analysis of results. ANOVA is a statistical technique used to
assess the significance of process parameters on output responses. This was
performed by computing the sequential sum of squares (
ANOVA was carried out to measure impact of parameters on burr width. From Table 7 it can be observed that feed per tooth is the most significant parameter contributing 81 % causing top burr formation and cutting speed is another parameter that is significant with contribution ratio of 6 % which shows that it has almost 93 % less impact on top burr as compared to feed per tooth. Each point in the main effects plot (see Fig. 6) shows mean or average top burr width for a particular level of feed rate, cutting speed and depth of cut. The CRs are comparable with a previous research in which similar analysis was conducted on Ti-6Al-4V alloy at high speed machining setup (Jaffery et al., 2016). From Fig. 6, it can be seen that top burr width decreases with the increase in feed per tooth. Researchers have reported similar outcomes from high speed micro-milling of Ti-6Al-4V alloy and found inverse relationship between feed rate and burr formation and concluded that feed per tooth is the most significant parameter influencing top burr formation (Jaffery et al., 2016; Kim et al., 2014; Thepsonthi and Özel, 2013). It is due to the fact that at lower feed per tooth, ploughing effect is more pronounced and material deforms plastically to form larger burrs (Kim et al., 2014).
Main effect plot between
Main effects plot between
The ratio of feed rate to cutting tool edge radius (
Comparing Fig. 7a and b, the region of
Main effects plots (see Fig. 6) for cutting speed and depth of cut also show
a downward trend which means increase in cutting speed and depth of cut
reduces the burr width. These result are comparable with the findings of
research where similar analysis and experiments were performed on Ti-6Al-4V
opting high speed machining (Bajpai et al., 2013; Jaffery et al., 2016; Kim
et al., 2014; Thepsonthi and Özel, 2013). Increase in cutting speed is
accompanied by decrease in cutting force (Jaffery and Mativenga, 2009; Pathak
et al., 2013) and as cutting speed decreases contact time of tool with the
chip is reduced.as a result less heat is transferred into the chip and more
heat is transferred into the tool tip (Rosemar et al., 2013). Temperature in
the shear zone is expected to rise at higher cutting speeds but the friction
between the tool rake face and chip is reduced thereby reducing the welding
phenomenon between chip and workpiece and heat generation at the tool chip
interface (Prasad, 2009) as a result reduction in burr formation takes place.
While it is also possible to minimize burr width by further increasing
Minimum top burr width (
Maximum top burr width (
Experimental results at optimum conditions.
From main effects plot it can be seen that setting feed per tooth, cutting speed and depth of cut at high levels yielded minimum top burr width. So, a validation test was carried out for burr width selecting optimal levels of parameters. A summary of machining parameters and experimental results corresponding with best and worst operating conditions is given in the Table 8.
It is evident from presented results that optimum conditions yield best results as compared to the initial results reported in Table 6. Figure 9 shows negligible burr at optimum conditions and Fig. 10 shows maximum burr.
Identifying KPVs is very important to enhance product quality and at the
same time productivity which ultimately leads to less manufacturing costs.
The impact of machining parameters in micro-milling is not as same as in
macro-machining. This is due to fact that feed rate is almost of the same
order as edge radius of the cutting tool. In this paper, KPVs (feed per
tooth, cutting speed and depth of cut) were varied to study their impact on
burr formation in detail. ANOVA technique was applied on measured outputs to
investigate the main effect of machining parameters on burr formation.
Machining at optimum conditions gave best outcome in the form of minimum
burr on the edges of slot. It is clearly evident that reduction in burr formation in micro-milling of
Ti-6Al-4V can be effectively achieved by using low speed machining setup at
optimal conditions instead opting for expensive high speed machining setup. At 95 % confidence level, feed rate was found to be the most significant
factor contributing towards burr formation with contribution ratio of
81 %. Reduction in burr formation can be achieved by selecting feed per tooth in
the range of 1–3 times of edge radius either it is low speed or high speed
machining setup. Cutting speed is another control factor affecting burr formation during
micro-milling process and its contribution was found to be 6 % which is
75 % less than feed rate contribution. The impact of depth of cut was negligible (1 % contribution ratio). The contribution of residual effects was found to be 12 %. This is due to
many noise factors i.e. subsurface plastic deformation, tool vibrations,
chatter, material separation and strain hardening.
No data sets were used in this article.
GUR, SHIJ and MK designed experiments, GUR performed experiments supervised by LA on the CNC machine, AK and SIB reviewed drafted paper, suggested amendments and guidance during revisions.
The authors declare that they have no conflict of interest.Edited by: Bahman Azarhoushang Reviewed by: two anonymous referees