Comparative study on tool life and wear resistance of titanium nitride (TiN) and aluminium chromium nitride (AlCrN) coated carbide inserts

Abstract

The objective of this study is to investigate the enhancement of tool life and wear resistance with a physical vapour deposition (PVD) process applied using aluminium chromium nitride (AlCrN) and titanium nitride (TiN) coating on carbide inserts. Flank wear experiments are carried out on a computer numerically controlled (CNC) machine under wet conditions with both the coated inserts. Effectiveness of the coating on the tool life and its resistance to flank wear are observed at various cutting parameters such as cutting speed and feed rate by following the principle of design of experiments (DOE). It is inferred that AlCrN coated carbide tools perform nearly 70% better than the TiN coated carbide tools under high cutting speed and feed rate. AlCrN coating also enhances the durability of tool for metal cutting and thereby improves tool life even under harsh cutting conditions. A response surface methodology (RSM) is utilised to arrive at the optimum value for the various parameters which are responsible for improving the wear resistance and tool life.

Keywords

Wear resistance Tool life High cutting speed Wet machining SAE 8720H steel

Introduction

Tool life plays a critical role in an estimation of the productivity level expected for specific cutting conditions in manufacturing. It becomes extremely important both economically and for good quality that a tool insert should be chosen in such a way that it wears out in a progressive manner rather than being unpredictable for its working life due to its uncertain machining capability. Wear resistant coating on cutting tools is the most significant development in cutting tool technology.1 There are two basic regions of wear in a cutting tool, which are flank and crater wear. Flank wear is generally attributed to rubbing of tool along the machined surface, thus causing adhesive and/or abrasive wear. It is also due to the prevalence of high temperatures during a machining process thereby affecting tool material properties as well as the workpiece surface. Crater wear occurs on the rake face of the tool and changes the chip/tool interface geometry as well as affecting the cutting process. The most significant factors influencing crater wear are temperature at the tool/chip interface and the chemical affinity between the tool and the work piece materials. The effect of tool wear includes increase in cutting forces; increase in cutting temperature, poor surface finish and decrease in accuracy of finished parts. However, the selection of tool should meet the criteria such as higher hardness, higher strength and longer survivals.2 It may not always be feasible owing to the following reasons:

it is not cost effective in many situations

ii.

the concerned component is so complicated in shape and design that technically it is not feasible to change the material.

To enhance the tool performance, tool inserts are provided with a surface coating. Surface coating on carbide cutting tools is to enhance the properties such as high wear resistance, lower heat generation, thermal loads, corrosion and lower cutting forces, thus enabling them to perform better at higher cutting conditions than their uncoated counterparts. Lim and Lim3 suggested that coated inserts could be employed in a more cost effective manner when the machining conditions are determined based on the wear. Coated tool inserts offer much improved resistance to wear during machining, resulting in an increase in tool life rather than uncoated ones. The mechanical properties of tool and their corresponding cutting parameters are studied4^–10 for enhancement of tool life and wear resistance. PalDey and Deevi11 have reviewed available literature for single layer and multilayer wear resistant coatings of titanium nitrate and aluminium nitrate coatings extensively. Particularly, studies12^–18 are carried out on titanium and aluminium based cutting tools. These coated materials not only help reducing the wear and increasing the tool life but also improve strength and chemical inertness, reduce friction, and make the parts more stable at high temperatures.19^–21 The researchers studied that the surface coating materials such as TiCN, TiAlN, AlTiN,22 TiAlN/CrN and TiAlCrN23 on tool performance. It is inferred that coating material on tool improves the tool life and wear etc. Further, the researchers24^–30 studied the effect of cutting parameters on tool life, wear rate, roughness and machining forces etc using RSM in dry turning conditions.

In the present study, focus is on comparison of TiN and AlCrN coated tool inserts in high speed CNC machining under wet conditions. Most of the important parameters like cutting speed and feed rate on tool life and wear are studied and their significance on tool coating was analyzed using RSM.

Experimental

Workpiece material

Turning experiments are performed using a CNC machine under wet conditions. The following cutting parameters are used: cutting speed of 300–500 m min⁻¹ and feedrates of 0·1–0·3 mm rev⁻¹. Flank tool wear evaluation is conducted on the coated tool inserts by video graphic technique. The workpiece material is SAE 8720 H steel and its specifications are listed in Table 1. The workpiece material is selected as ISO 95MnCrW1 with hardness 23 HRC.

Table 1

Workpiece specifications

Description	Hot forged steel SAE 8720H having surface Hardness 250 HB
Heat treatment	Normalisation is not done
Dimension	68 mm diameter×150 mm length

Cutting inserts and tool holder geometry

Carbide inserts are used in the turning tests done on the CNC machine under wet conditions. The cutting inserts has ISO designation of DNMG 150608 with M5 geometry. The cutting tool geometry is 0·8 mm of nose radius, 55° angle of cutting edge and 0° of back clearance angle. Two types of cutting insert such as TiN and AlCrN are used in the experiment. The inserts are mounted rigidly on a tool holder with ISO designation of PDJNR. The machine specifications are listed in Table 2. The water based emulsion derived from Coolcut Super H.W.C. 210% at 29·5°C is used as a cutting fluid.

Table 2

CNC machine specification

Machine	CNC turning centre
Machine make	ACE DESIGNER JOBBER-XL
Control	FANUC
Spindle speed/rev min⁻¹	0–3500
Z axis travel length/mm	250
X axis travel length/mm	300
Chuck diameter/mm	200
Power/kW	5·5
Swing over/mm	300
Coolant	Cool cut super H.W.C 210% at 29·5°C

Work material

The cutting performance tests were performed on SAE 8720 H low carbon steel based on AISI-SAE standard carbon steel. The work piece was hot forged but normalisation is not done and is shown in Fig. 1. Figure 1 shows both the forged raw workpiece as well as the machined piece.

Figure 1

Forged and machined workpieces

Optimisation process

Central composite design (CCD) matrix modelling of design is very economical for extracting the maximum amount of information with minimum number of experimental runs. The central composite design matrix requires a number of experimental runs evaluated using the equation where K is the number of variables and C_p is the replicate number of the central point. In the present case, this results in a single block of 13 sets of test conditions, and five central points (i.e. five experiments). The orders of the experiments are fully randomised. In this study, the CCD matrix is chosen to estimate the relationship between wear, tool life and cutting parameters such as cutting speed and feedrate. Two variables have been tested at five levels. For statistical computations, the variables are coded according to equation (1) (1) where X_i is the independent variable coded value, U_i is the independent variable real value, U₀ is the independent variable real value on the centre point, and ΔU_i is the step change value. The variables range and levels are given in Table 3. The mathematical relationship between the four variables is approximated by the second order polynomial, and terms for interaction are as given by equation (2) (2) where Y_c is the predicted wear and tool life equation parameters by the model, a₀ is equation constant, a_i, a_ii, and a_ij are regression coefficients of the model. The coefficient a_i and two interaction factors, a_ii and a_ij, have been estimated from the experimental results by applying least square method. Experiments are performed according to CCD matrix given in Table 4. The central point test is replicated five times (run nos. 1, 7, 9, 12 and 13 in Table 4), in order to have a good estimation of the experimental error.

Table 3

Variables and experimental levels*

Factors	symbol	Range and level
		−2^1/2	−1	0	+1	+2^1/2
Cutting speed/m min⁻¹	X ₁	300	329	400	471	500
Feedrate/mm rev⁻¹	X ₂	0·10	0·13	0·2	0·27	0·30

*−α = x_min, −1 = [(x_max+x_min)/2]−[(x_max−x_min) /2α], 0 = (x_max+x_min)/2, +1 = [(x_max+x_min)/2]+[(x_max−x_min) /2α], +α = x_max

Table 4

Experimental conditions and their results

Run no	Un coded		Wear side A		Wear side B		Tool life
Run no	x₁/m min⁻¹	x₂/mm rev⁻¹	TiN/mm	AlCrN/mm	TiN/mm	AlCrN/mm	TiN/h	AlCrN/h
1	400	0·20	0·30	0·18	0·22	0·15	275	456
2	471	0·27	0·40	0·26	0·29	0·23	259	381
3	329	0·13	0·25	0·09	0·18	0·11	384	536
4	400	0·10	0·25	0·09	0·16	0·09	388	506
5	329	0·27	0·29	0·16	0·23	0·14	276	428
6	300	0·20	0·21	0·10	0·18	0·13	345	522
7	400	0·20	0·30	0·18	0·22	0·15	275	456
8	471	0·13	0·33	0·19	0·19	0·16	312	465
9	400	0·20	0·30	0·18	0·22	0·15	275	456
10	400	0·30	0·36	0·23	0·31	0·19	266	361
11	500	0·20	0·43	0·23	0·26	0·25	267	395
12	400	0·20	0·30	0·18	0·22	0·15	275	456
13	400	0·20	0·30	0·18	0·22	0·15	275	456

Results and discussion

Wear measurement

Flank wear is measured using the videographic measuring system at two sides (A and B) of the inserts (Fig. 2). Image of the tool is captured using a video camera before the cutting operation. When the tool failed, again the flank surface is video graphed. Both the images are compared using an image processing software and thereby arrive at a thickness of material lost in the flank surface. Figure 2b shows the wear morphology on flank face for a TiN coated tool insert when employed with the cutting speed of 450 m min⁻¹, a feedrate of 0·25mm rev⁻¹. At this turning condition, the longest tool life of 431 numbers of components was achieved. As shown in the progressive wear picture, the TiN coating has been removed from the cutting edge, and the thermal crack is already been observed on the cutting edge of the flank face. Figure 2d shows the wear morphology on flank face for an AlCrN coated tool insert when employed with a cutting speed of 450 m min⁻¹, a feedrate of 0·25mm rev⁻¹. At this turning condition, the longest tool life of 609 numbers of components was achieved. As shown in the progressive wear picture, the AlCrN coating has been removed from the cutting edge, and the thermal crack is already been observed on the cutting edge of the flank face.

Figure 2

TiN and AlCrN coated tool inserts before and after machining operation:

As the preliminary studies shows that AlCrN coated tool carbide wears less than the TiN coated tool carbide. Moreover, these parameters are systematically studied by using DOE concepts. The regression equation obtained for TiN coated tool wear of side A that relates the operating variables for data of Table 4 is given by equation (3) (3) The statistical analysis of result is given in Table 5 for TiN coated insert of side A. The regression coefficient and p values for all the linear, quadratic and interaction effects of the parameter is given in Table 5. If the value of p is <0·05, then a variable is considered significant at 95% probability. The statistical significance of the ratio of mean square variation (due to regression and mean square residual error) is tested using analysis of variance (ANOVA). The associated p value is used to judge how much F_statistics indicates statistical significance. A p value lesser than 0·05 (i.e. α = 0·05, or 95% confidence) indicates that the model is statistically significant.

Table 5

Statistical t and p values for TiN coated insert side A (R² = 0·985)

Terms	Coefficient	t	p
Constant	0·340581	2·100	0·074
X ₁	−0·001328	−1·930	0·095
X ₂	0·028708	0·053	0·959
X ² ₁	0·000003	3·221	0·015
X ² ₂	0·631020	0·771	0·466
X ₁ X ₂	0·000503	0·465	0·656

From Table 6, it is observed that the calculated p value is <0·05. This indicates that the model is statistically significant. The F value of Table 6 indicates that equation (3) is statistically significant. This further indicates that equation (3) can be effectively used to predict tool life in various operating conditions. Estimated values and match with experimental data points, indicating a good fitness (R² value of 0·97 for TiN coated inserts of Side A). A p value less than 0·005 indicate that the model is statistically significant. Similarly, statistical analysis carried out for AlCrN coated tool for Side A, TiN and AlCrN coated tool for Side B. The statistical analysis results are given in Tables 7–12 in Appendix 1. The regression equations are given as equations (4)–(6). (4) (5) (6) The effects of the cutting speed and feedrate on tool flank wear are shown in Figs. 3 and 4 for side A of TiN and AlCrN respectively. Figures 3 and 4 reveal that the flank wear increases with the increase in both the feedrate and the cutting speed. Owing to the increase in the cutting speed, there is increased possibility of uncut material remaining on the workpiece between two successive vibration cutting cycles. The flank wear rate is directly proportional to cutting speed and feedrate. It is also confirmed by Shamoto and Moriwaki, Nath et al. and K. Katuku et al.31^–33 The flank edge is affected due to not enough clearance angle is maintained in the tool geometry. Moreover, as the cutting speed increases the possibility of interaction between the tool flank and the finished work piece behind the tool nose also increases which leads to more flank wear.34 In addition, the relative speed between the tool and the work piece in the opposite direction would increase that led to increase in the tangential force in the turning method. Thus it is inferred that tool life decreases with the increase in both the feed rate and the cutting speed.

Figure 3

Effect of cutting parameters on wear for TiN coated on Side A

Figure 4

Effect of cutting parameters on wear for AlCrN coated on Side A

Table 6

Analysis of variance for TiN coated insert Side A

Source	Degree of freedom (DF)	Sum of squares (SS)	Mean squares (MS)	F _statistics	p
Regression	5	0·040792	0·008158	70·66	0·000
Linear	2	0·039556	0·000245	2·12	0·191
Square	2	0·001211	0·000606	5·25	0·041
Interaction	1	0·000025	0·000025	0·22	0·656
Residual error	7	0·000808	0·000115	…	…
Lack-of-fit	3	0·000808	0·000269	…	…
Pure error	4	0·000000	0·000000	…	…
Total	12	0·041600	…	…	…

TiN flank wear rate is more than AlCrN, it indicates wear performance hindering due to AlCrN coating on carbide tool. AlCrN coating performs much better than TiN coatings for tool inserts. This flank wear increased at high cutting speed due to a contact area at the chip/ tool interface and caused concentration of high temperature very close to the cutting edge. Khrais and Lin35 reported that Arcona is showing better performance than the other coatings such as TiN, TiAlN, TiCN and AlTiN. It is concluded that AlCrN coated carbide tool performs approximately 70% for Side A and 23% for Side B better than TiN coated carbide tools under the high cutting speed and feedrate as well as enhance the tool life. Similar inferences are observed for B side of TiN and AlCrN and thus the results are presented in Figs. 5 and 6. In addition, Fig. 7 shows that the comparison of uncoated and coated inserts of TiN and AlCrN. It also shows that coating improves the flank wear resistance of inserts.

Figure 5

Effect of cutting parameters on wear for TiN coated on Side B

Figure 6

Effect of cutting parameters on wear for AlCrN coated on Side B

Figure 7

Comparison of uncoated and coated inserts

Enhancement of tool life

Effect of cutting parameters on tool life of TiN and AlCrN coated inserts

Statistical analyses are performed and results are given in Tables 13–16 in Appendix 2 for the regression equations obtained for tool life of TiN and AlCrN that relates the operating variables is given by Equations (7) and %(8). (7) (8) The contour plots are used for graphical representation of regression Equations (7) and %(8). Each contour plot represents an infinite number of combinations of two variables with the other variables as constant. Contour plots are easy and convenient to understand interactions between two factors and also helps us to locate their optimum levels. Coated carbide tools are tested at five different speeds, 300, 350, 400, 450 and 500 m min⁻¹ respectively. The effect of cutting speed and feedrate on tool life is presented in Figs. 8 and 9. It is observed that decreases in tool life with increasing the cutting speed and feedrate for TiN and AlCrN. The tool life for AlCrN coated tool inserts is much more than TiN at high cutting parameters. The results showed that performance of coated (AlCrN) carbide tools under wet cutting is significantly better than under dry cutting for all the selected cutting speeds.

Figure 8

Effect of cutting parameters on tool life for TiN coated

Figure 9

Effect of cutting parameters on tool life for AlCrN coated

Conclusion

In the present work, the tool performance has been evaluated in terms of tool life and flank wear as a function of cutting parameters such as cutting speed and feed rate. The flank wear resistance and tool life are studied on TiN and AlCrN coated tool inserts in computer numerically controlled machine under wet condition. It is concluded that AlCrN coated on carbide tool performs better than TiN coated on carbide tool under high cutting speed and feedrate. AlCrN coated tool inserts provide more resistance to wear during machining, leading to an increase in tool life.

Footnotes

Appendix 1

Appendix 2

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