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Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools
j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y 2 0 9 ( 2 0 0 9 ) 511–519
journal homepage: www.elsevier.com/locate/jmatprotec
Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools K. Palanikumar a,∗ , J. Paulo Davim b a
Department of Mechanical & Production Engineering, Sathyabama University, Chennai 600119, Tamilnadu, India Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193 Aveiro, Portugal b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Glass fibre-reinforced plastics (GFRP) composite materials are used in many different engi-
Received 12 April 2007
neering fields. The need for machining of GFRP composites has not been eliminated fully.
Received in revised form
The tool wear reduction is an important aspect during machining. In the present work,
4 February 2008
an attempt has been made to assess the factors influencing tool wear on the machining
Accepted 13 February 2008
of GFRP composites. Experimental design concept has been used for experimentation. The machining experiments are carried out on lathe using two levels of factors. The factors considered are cutting speed, fibre orientation angle, depth of cut and feed rate. A procedure
Keywords:
has been developed to assess and optimize the chosen factors to attain minimum tool wear
Optimization
by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interac-
Glass fibre-reinforced plastics
tion graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that cutting
(GFRP) composites
speed is a factor, which has greater influence on tool flank wear, followed by feed rate. Also
Tool wear
the determined optimal conditions really reduce the tool flank wear on the machining of
Response table
GFRP composites within the ranges of parameters studied.
Analysis of variance (ANOVA)
© 2008 Elsevier B.V. All rights reserved.
Normal probability plot
1.
Introduction
Today fibre-reinforced plastics (FRPs) have an important place in the field of engineering materials. They are increasingly being used for varieties of engineering applications from automotive to aircraft components. They have attractive properties than other materials such as high strength to weight ratio, high fracture toughness, and excellent corrosion and thermal resistances (Strategic, 2002). Because of the different applications, the machining of FRP cannot be eliminated. The machining of FRPs are necessary to obtain near-nett shape and for precision fits. The first unique material removal character-
∗
istics in orthogonal cutting of FRPs was presented by Koplev et al. (1983). They have discussed the formation of the chips and the quality of machined surface in cutting of unidirectional carbon fibre-reinforced plastics. They have measured the cutting forces parallel and perpendicular to the cutting direction and discussed the formation of chips and wear of the tool. Sakuma and Seto (1983) conducted face-turning tests. They have measured cutting resistance and surface roughness for analyzing the machinability and tool wear in cutting of glass fibre-reinforced plastics. They have studied how fibre orientation influences both the quality of the machined surfaces and tool wear. Bhatnagar et al. (1995) has studied the machinabil-
Corresponding author. Tel.: +91 44 22280226. E-mail addresses: palanikumar [email protected] (K. Palanikumar), [email protected] (J.P. Davim). 0924-0136/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2008.02.020
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ity of unidirectional carbon fiber reinforced plastic laminates with different fibre orientation. They have presented a model for predicting the cutting forces and the dependence of cutting direction on machinability requirements. The effect of tool wear on cutting forces in the orthogonal cutting of unidirectional glass fibre-reinforced plastics has been studied in depth by Caprino et al. (1996). They have found a strict correlation between the flank wear and vertical force. The machining of fibre-reinforced composite materials differs from normal metal cutting. The behaviour of composites is heterogeneous and depends upon the fibre and matrix properties, orientation of fibres, bond strength between fibre and matrix, and the type of weave. Therefore, the machining of these materials requires special consideration about the wear resistance properties of the tool materials and the cutting tool geometry (Tondon et al., 1989). Davim and Mata (2004a) have studied the influence of cutting parameters on surface roughness in turning glass fibre-reinforced plastics using statistical analysis. The objective has been focused on obtaining the contribution percentages of the cutting parameters (cutting velocity and feed rate) on the surface roughness on the workpiece. Subsequently, these authors conducted the new optimization study of surface roughness in turning GFRP tubes manufactured by filament winding and hand lay-up, using polycrystalline diamond cutting tools. The objective has been in establishing the optimal cutting parameters to obtain a certain surface roughness in the GFRP workpieces, using multiple analysis regression (Davim and Mata, 2004b). When GFRP composites are machined, the glass fibres break-up by rupturing and shearing, thus producing rapid tool wear. Tool wear is an important response in machining process. The tool integrity plays an important role in the form of the machined surface of the workpiece and the control of the cutting quality. Unlike the machining of traditional materials, problems are encountered during the machining of FRP’s. The typical problems encountered are (Santhanakrishnan et al., 1988, 1989): 1. Material delamination: These problems encountered when the resin/filler materials do not back up the fibre materials adequately. 2. Thermal damage: Differential expansion and thermal conductivity between the fibres and filler materials result in thermal damage. 3. Machining tough fibres: Machining of tough fibres results in a fuzzy surface, damaging the cutting tool considerably. The studies on machining GFRP composites indicates that reduction of tool wear has been found very difficult and further research is required to have an effective control. (Caprino et al., 1996; Santhanakrishnan et al., 1988, 1989; Palanikumar, 2004; Palanikumar et al., 2004, 2007). In the present work, experimental design scheme in William and Cox (1957) is used for the experimentation and ANOVA is used for analysis. This experimental scheme is almost similar to the L16 orthogonal array. A procedure has been introduced to assess and optimize the chosen factors to attain minimum tool wear by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of variance (ANOVA) technique.
2.
Scheme of investigation
In order to achieve the desired tool wear on the GFRP composite workpiece, the present investigation has been planned in the following steps: (i) identifying the important factors, which influence the tool wear on the machining of GFRP composites; (ii) finding the upper and lower limits of the factors identified; (iii) developing the experimental design matrix using design of experiments; (iv) conducting the experiments as per the design matrix; (v) assessing the factors and its effects using response table and effect graph; (vi) assessing the real or chance effect of factors using normal probability plot; (vii) analyzing the results using ANOVA; (viii) optimizing the chosen factor levels to attain minimum tool flank wear.
2.1.
Identifying the important factors
Based on the previous published results (Sakuma and Seto,1983; Bhatnagar et al., 1995; Caprino et al., 1996) and experience in the field by authors (Palanikumar et al., 2004, 2007), the machining parameters, which are having significant effect on tool wear in machining of GFRP composites have been identified. The machining parameters identified are: (i) cutting speed; (A), (ii) workpiece (fibre orientation) angle (B); (iii) depth of cut (C); (iv) feed rate (D). Out of the four parameters considered, fibre orientation angle has been specially applied to fibre-reinforced composite materials.
2.2. Finding the upper and lower limits of the factors identified For finding the upper and lower limits of the machining parameters, a detailed analysis has been carried out. The limits identified are discussed below (Palanikumar and Davim, 2007): (i) The studies related to machining of GFRP composites indicate that cutting speed is the factor, which highly influences the tool wear. Also the increase of cutting speed increases the tool wear. The higher cutting speed leads to large deformation rate of glass fibre in the composite and subsequently produces severe tool wear (Hasegawa et al., 1984). In machining of GFRP composites, during machining, at lower cutting speed, large material flow with cut fibers can be produced and it leads to high surface roughness (Palanikumar et al., 2006). For maintaining the proper surface roughness with less tool wear, the cutting speed has been set at reasonable level of being 75 and 175 m/min. (ii) Workpiece fibre orientation is another important factor which influences tool wear. Higher fibre orientation needs more forces to remove the material from the workpiece. At larger fibre orientation angles, the shear takes place
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Table 1 – Control parameters and their levels S. no.
Parameter
Notation
Unit
Levels Low (−1)
1. 2. 3. 4.
Cutting speed Fibre orientation angle Depth of cut Feed rate
A B C D
along the fibres and it generates compressive stress on the rake face of the tool (Takeyama and Lijima, 1988), which leads to high tool wear. In the present study, the fibre orientation angle considered in the workpiece is 30◦ and 90◦ . The fiber orientation angle has been fixed as per the availability from the manufacturer. (iii) In machining of composites, depths of cut play only a small role, but abnormal increase of depth of cut increases the heat generation and wear on tool. Also high depth of cut produce deleterious effect on surface quality of GFRP workpiece. The depth of cut has been chosen based on the previous study (Palanikumar and Davim, 2007) and is 0.50 and 1.50 mm. (iv) The feed rate also plays a significant role in machining of GFRP composites. The higher feed rate produces heat generation, and it leads to high tool wear (Palanikumar et al., 2004). The increases in feed rate also increase the chatter that in turn leads to more tool wear. Hence low feed rates are preferred for machining of GFRP composites. Very low feed rate leads to the incomplete machining of fibers in the composites and hence the feed rate has been selected at reasonable level and is 0.10 and 0.50 mm/rev.
2.3. Developing the experimental design matrix using design of experiments An experiment is a series of trials or tests, which produces quantifiable outcomes. The experiment may be random or deterministic. For experimentation, design of experiment in statistics has been used. The merit of this experimental scheme is that the cost of experimentation is reduced considerably as compared to one factor at a time type experiment. The identified factors and its lower and upper limits are discussed in Sections 2.1 and 2.2. In this experimental scheme, all possible combinations of levels are included so that there are 2n (where n refers to the number of factors, i.e., 24 = 16) trials in the experiment (Cochran and Cox, 1963). The notations, units and their levels chosen are summarized in Table 1. For easy recording and processing of experimental data, the parameters levels are coded as +1 and −1. The intermediate coded value of any levels can be calculated by using the
m/min
High (+1)
75 30 0.5 0.10
◦
mm mm/rev
175 90 1.5 0.50
Table 2 – Composition of GFRP composite Material
Type
Matrix Hardener Reinforcement
Epoxy: Araldite, LY 556 HT 972 E-glass fiber (Multi-filament roving) (linear density: 2400 Tex)
following expression (Ravi et al., 2004): Xi =
X − [Xmax + Xmin /2] [Xmax − Xmin /2]
(1)
where Xmax is the upper level of the parameter, Xmin is the lower level of the parameter and Xi is the required coded values of the parameter of any value of X from Xmin to Xmax .
2.4.
Conducting the experiments
The composite pipes used in this experimental study have been produced by filament winding process. Filament winding is an effective method to manufacture composite pipes and this process gives very high directional strength with precise alignment of fibres. The constituent materials of the composite, and its mechanical properties are given in Tables 2 and 3. The experiments are conducted for turning operation in all geared lathe. The ISO specification of the tool used for the turning operation is a WIDAX tool holder PC LNR 1616 K12. The insert used is a coated carbide tool having composition: Co 6.0%; composite carbide 8.0%; WC rest. The coating layer system is: CVD-TiC + Ti(C, N) + Al2 O3 and layer thickness used is 11 m. The machining tests are carried out as per the design matrix at random to avoid systematic errors. The design matrix and the corresponding responses are given in Table 4. The response studied is tool flank wear. The tool flank wear (Vb ) has been measured with respect to a machining time at predetermined intervals using radical toolmakers’ microscope. The machining time considered for the each experiment is 9 min. The higher time frame is chosen for the experimentation for arriving and analyzing the influence of factors fully. The experiments are conducted for three
Table 3 – Mechanical properties of composite material Material Composite
Tensile strength u (MPa)
Tensile modulus, E (GPa)
Shear modulus, G (GPa)
Poisson’s ratio,
930
E1 = 46 E2 = 13
5
12 = 0.3 21 = 0.08
Mass density, (kg/m3 ) 1876
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Table 4 – Design matrix and corresponding output response Expt. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A
B
C
D
−1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1
−1 −1 1 1 −1 −1 1 1 −1 −1 1 1 −1 −1 1 1
−1 −1 −1 −1 1 1 1 1 −1 −1 −1 −1 1 1 1 1
−1 −1 −1 −1 −1 −1 −1 −1 1 1 1 1 1 1 1 1
Tool flank wear (mm) 0.41 0.91 0.85 1.02 0.31 0.69 0.78 0.67 0.72 1.16 0.78 1.21 0.79 0.95 1.02 0.99
0.49 0.87 0.81 1.06 0.33 0.67 0.71 0.64 0.70 1.08 0.73 1.16 0.72 0.98 0.98 1.04
0.45 0.95 0.77 0.98 0.29 0.65 0.85 0.70 0.74 1.12 0.83 1.26 0.65 0.96 1.05 0.94
times and are tabulated. The experimental results are given in Table 4.
3. Influences of machining parameters and their effects on tool wear The effect of factors which influences the tool flank wear on composite machining process has been analyzed through: (i) response table and effect graph, (ii) normal probability plot; (iii) ANOVA technique.
3.1.
Response table and effect graph
The influence of machining parameters on tool flank wear has been performed using response table. Normally in engineering problems, higher order interactions do not reveal any significant effect and hence three and four factor interactions are not considered in this study. Only main factors and two factor interactions are considered for the present study. The response table for main and two factor interactions at two different levels are shown in Table 5. For analysis the average of three readings are taken. Response tables are used to simplify the calculations needed to analyze the experimental data. In response table, the effect of factor on a response variable is the change in the response when the factor goes from its low level to its high level. In table if effect of a factor is greater than zero, the average response is higher for the higher level of the factor than for the low level. However, if the estimated effect is less than zero (−ve), it indicates that the average response is higher at low level of the factor than at high level. If the effect for a factor is very small, then it is probably because of random variation than a ‘real’ factor effect. The graphical display (Ravi et al., 2004) such as effect graph can be used in conjunction with a response table to identify appropriate settings for machining parameters to minimize the tool flank wear. The effect of main and interaction factors derived for composite machining process is plotted in Fig. 1. From figure, it is inferred that larger the vertical line, the larger the change in tool wear when changing from level −1 to level +1 for a factor. It will be pointed out that the statistical significance of a factor is directly related to the length of the vertical line.
Fig. 1 – Effect graph.
3.2.
Normal probability plot
In effect graph, it is found that some of the factor effects are larger than the other, but it is not clear, whether these results are ‘real’ or ‘chance’. To identify the ‘real’ effects, normal probability plot is used and shown in Fig. 2. Normal plot is a graphical technique based on “Central limit theorem”. The procedure for constructing the normal probability plot is given
Fig. 2 – Normal probability plot.
1.02 0.99 1.02 0.99
0.78 1.21
0.72 1.12 0.78 1.21
0.81 1.02
0.78 0.67
0.45 0.91 0.81 1.02
0.99
0.72 0.96 0.96
1.21
0.72 1.12 1.12
0.67
0.31 0.67 0.67
0.91
1.02
0.45 0.91
1.02
0.72
0.78
0.72
0.78
0.31
0.81
3.3.
Analysis of variance
The normal probability plot has the disadvantage of not providing a clear criterion for what values for estimated effects indicate significant factor or interaction effects. In addition, how do we measure amount of departure from the straightline pattern. ANOVA meets this need by how much an estimate must differ from zero in order to be judged “statistically significant”. The ANOVA result is presented in Table 6. This analysis has been carried out for a level of significance of 5%, i.e., for a level of confidence of 95%. From the ANOVA results, it is concluded that the factors A, B, C, D and their interactions AB, AC, BD and CD have significant effect on tool flank wear and AD, BC have no effect at 95% confidence level. As the interaction effect of AB, AC, BD and CD seems to be significant to the tool flank wear, the average values of the tool flank wear are calculated for all the combinations. By using the values of interaction, the significant interaction graphs are drawn for each combination of levels. When comparing with previous studies on machining of GFRP composites (Palanikumar and Davim, 2007) using same set of experimental conditions, the analysis with repetition of experiments provides best results. In the previous study (Palanikumar and Davim, 2007), only the interactions AB and AC are significant. But in the present analysis, the interaction effects such as BD and CD also are significant. The significant interactions between the parameters (AB, AC, BD and CD) are shown in Figs. 3–6. The insignificant interactions (AD and BC) are shown in Figs. 7 and 8. In these figures the lines are parallel to each other, which show that there is no interaction between parameters. By analyzing these figures also evident from ANOVA analysis, it has been concluded that AB and AC are more interactive than other interactions.
0.45 0.95 0.77 0.98 0.29 0.65 0.85 0.70 0.74 1.12 0.83 1.26 0.65 0.96 1.05 0.94
0.45
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elsewhere (Lochner and Mater, 1990). As per the normal probability plot, the points which are close to a line fitted to the middle group of points represent estimated factors which do not demonstrate any significant effect on the response variable. On the other hand, the points appear to be far away from the straight line are likely to represent the ‘real’ factor effects on the tool flank wear. From Fig. 2, it has been asserted that the main factors A, B, C, D and their two factor interactions AB, AC, BD and CD are quite away from the straight line and are considered to be significant.
0.49 0.87 0.81 1.06 0.33 0.67 0.71 0.64 0.70 1.08 0.73 1.16 0.72 0.98 0.98 1.04 0.41 0.91 0.85 1.02 0.31 0.69 0.78 0.67 0.72 1.16 0.78 1.21 0.79 0.95 1.02 0.99 1. 2. 3. 4. 5. 6. 7. 8. 9. 10 11 12 13 14 15 16
Average Effect
−1 +1 −1
−0.6983 0.9442 −0.7329 0.9096 −0.8775 0.7650 −0.7025 0.9400 −0.8873 0.7613 −0.7567 0.8858 −0.8134 0.8292 −0.8104 0.8321 −0.8504 0.7921 −0.7825 0.8600 0.2459 0.1767 −0.1125 0.2375 −0.1260 0.1291 0.0158 0.0217 −0.0583 0.0775
0.31 0.67 0.78 0.67
0.72 0.96 1.02 0.99
0.45 0.91 0.81 1.02 0.31 0.67 0.78 0.67
+1 +1
−1
C B A Tool flank wear (mm) S. no.
Table 5 – Response table for main factors and two factor interactions
−1
D
0.72 1.12 0.78 1.21 0.72 0.96 1.02 0.99
0.96 1.02
1.12 0.78
1.21 0.72
1.02
0.99
0.96 1.21 0.72
1.12
0.99
0.96
1.21
1.12 0.78
0.78 0.67 0.72 0.67 0.72
0.78
0.72
0.78
0.78 1.21 0.72 0.96
1.02 0.99
1.02 0.99
0.78 1.21
0.78 0.67 0.72 1.12 0.67 0.72
0.67 0.31 0.67
+1
0.91 0.81
0.67 0.78
0.45
1.02 0.31
0.91
1.02 0.31
0.45
0.81
0.45
0.81
0.91
1.02
0.81 1.02 0.31 0.67
0.78 0.67 0.72 1.12
0.31 0.67
0.45 0.91 0.45 0.91
−1 +1 −1 +1 −1 +1 −1
0.72 0.96
0.31 0.67 0.78 0.67 0.72 1.12 0.78 1.21 0.81 1.02
+1 −1 +1
BD BC AD AC AB
0.72 0.96 1.02 0.99
0.45 0.91 0.81 1.02
+1 −1
CD
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Fig. 3 – Interaction between A and B.
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Table 6 – ANOVA test results Factors
Sum of squares
DOF
A B C D AB AC AD BC BD CD Error
0.72521 0.37453 0.15188 0.67687 0.17280 0.20021 0.00301 0.00563 0.04083 0.07208 0.16468
1 1 1 1 1 1 1 1 1 1 37
Total
2.58773
47
Mean square (MS) 0.72521 0.37453 0.15188 0.67687 0.17280 0.20021 0.00301 0.00563 0.04083 0.07208 0.00445
Fratio
P
162.94 84.15 34.12 152.08 38.83 44.98 0.68 1.27 9.17 16.19
0.000 0.000 0.000 0.000 0.000 0.000 0.416 0.268 0.004 0.000
Fig. 6 – Interaction between C and D.
Fig. 4 – Interaction between A and C.
(i) (ii) (iii) (iv)
cutting speed at low level (75 m/min); workpiece (fibre orientation angle) at low level (30◦ ); depth of cut at high level (1.5 mm); feed rate at low level (0.10 mm/rev).
Based on the above optimum conditions, the minimum value of tool flank wear can be obtained from the following expression using the values form response table (Table 5)
Fig. 5 – Interaction between B and D.
4. Optimizing the chosen factor levels to attain minimum tool flank wear From the analysis of response graph, response table, and interaction graphs, the optimal machining parameters for the GFRP machining process is achieved for the minimum value of tool flank wear. The optimal conditions arrived are
Fig. 7 – Interaction between A and D.
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Fig. 8 – Interaction between B and C.
(Lochner and Mater, 1990 and Ravi et al., 2004). Vb(min) = [grand mean] +[contribution of A] + [contribution of B] +[contribution of C] + [contribution of D] +[contribution of AB] + [contribution of AC] +[contribution of BD] + [contribution of CD]
(2)
In this work, experimental design method and ANOVA have been used for optimization of parameters. The experimental design is similar to the orthogonal array proposed by Taguchi. Normally Taguchi’s method uses signal-to-noise ratios for analysis. Box (1988) has discussed signal-to-noise ratios, performance criteria and transformations. He has suggested a data analytic method instead of signal-to-noise ratios. Many of the concerns about the robust design using orthogonal array is clearly debated and is summarized in the extensive panel discussion by Nair, 1992. Ayudthya and McDowell have compared Taguchi’s methodology and variance from slope (VFS) approach. They have concluded that the VFS approach is better than Taguchi’s design. Kunjur and Krishnamurthy have used a multi-criteria optimization approach for a robust design. They have identified a Pareto-optimal solution set based on relative effects of the various factors on the objective function. Recently Yildiz and Ozturk (2006) applied hybrid enhanced genetic algorithm. They have introduced a refined design space for population in genetic algorithm by integrating robust design parameter concept. While seeing the above, the experimental design method and ANOVA table can find only upper or lower limits of variables (parameters) as optimum value. If optimum value is between the levels and then experimental design method and ANOVA table are not effective to find real optimum values. They are effective only to refine the range of variables considered, which is the limitation of this study.
Vb(min) = V¯ b + [A(−1) − V¯ b ] + [B(−1) − V¯ b ] + [C(+1) − V¯ b ] + [D(−1) − V¯ b ] + [(AB(+1) − V¯ b )] + [AC(+1) − V¯ b ] + [(BD(+1) − V¯ b )] + [CD(−1) − V¯ b ]
5.
Discussions
(3)
Vb(min) = 0.82125 + [0.6983 − 0.82125] + [0.7329 − 0.82125] +[0.7650 − 0.82125] + [0.7025 − 0.82125] +[0.76125 − 0.82125] + [0.7567 − 0.82125] +[0.8504 − 0.82125] + [0.7825 − 0.82125] = 0.308 ≈ 0.30 nm
(4)
The above result reveals that the minimum tool flank wear on the machining of GFRP composites within the range of factor under investigation is 0.30 mm. The validity of the optimization procedure has been checked through confirmation experiments (Table 7). The variation is within the permissible limit. It can be seen from the confirmation experimental results that the tool flank wear of the machining process has greatly improved by the optimal setting of machining parameters. The optimum condition obtained through this study is only the near optimal solution.
In machining of GFRP composites, tool flank wear plays an important role and is a factor of great importance in maintaining the accuracy of products. During machining, the tool has been in contact with workpiece. The usage of tool for a particular time leads to wear. The factors which affect the tool state are many but the machining parameters such as cutting speed, feed rate and depth of cut and workpiece fibre orientation have a significant influence on the tool flank wear. From the available literature, it has been known that the mechanism of cutting in GFRP is due to the combination of plastic deformation, shearing and bending rupture. The above mechanism depends on flexibility, orientation and toughness of the fibres (Santhanakrishnan et al., 1989). In the machining of conventional metals, abrasion occurs due to ploughing into softer matrix by hard constituents such as segregated carbides or adhesion and formation of metallic bonds formed over the rubbing surfaces under load and subsequent rupture of these bonds followed by transfer of these elementary particles and some times chemical diffusion occurs in cutting tool (Bhattacharyya, 1996). However, the chip generated in machining of GFRP composite material is different and is of powder
Table 7 – Results of the confirmation trials and their comparison with the results Setting levels Speed = 75 m/min, Fibre orientation angle 30◦ Depth of cut = 1.5 mm Feed rate = 0.10 mm/rev
Expt. no. 1. 2. 3.
Predicted tool flank wear (mm) 0.30 0.30 0.30
Experimental values (mm) 0.29 0.27 0.31
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Fig. 9 – Typical wear patterns observed on the flank (a) and rake (b) faces of the tool after machining of GFRP composites.
form. Since the powdery chips are not flowing over the rake face, crater wear normally does not occur; instead the hard glass fibres abrade the tool edge. Also during machining of FRP, the tool continuously encounters alternate matrix and fibre materials, whose response to machining can vary greatly because of the above properties. The cutting zone experiences both thermal and mechanical stresses. The cutting nose may be subjected to localized dynamic loading, due to the variations in properties of matrix and fibre. This dynamic load may cause tool failure due to possible low cycle fatigue apart from the usual types of wear (Santhanakrishnan et al., 1988). When machining of GFRP at high cutting speeds, the tool tip is intact and there is no indication for any chipping. A reaction product sticking to the surface of the tool is probably a mixture of resin with small fibers (Palanikumar et al., 2004). In FRP machining, the cutting tool would be subjected to flank wear associated with adhesion and intense abrasion. The worn out cutting edges of the tool was examined using scanning electron microscope (SEM). Typical micrographs of wear patterns observed on tool are shown in Fig. 9. In figure (a), it can be seen that the flank wear observed is associated with abrasion marks. This may be attributed to the abrasive nature of the fibres that are used with workpiece material. The
presence of grooves which were on the worn flank surfaces indicate that the wear of tool is due to plastic deformation, shearing and bending rupture. Figure (b) shows the rake face of the worn tool. From the results, it can be seen that the tool flank wear is minimum at 30◦ fibre orientation angle. The tool flank wear increases with the increase of fibre orientation angle. The tool flank wear has been increased more rapidly with the increase of fibre orientation. The reason being at larger fibre angles, compressive strain is generated within the workpiece material. This finding has close relationship with the results presented by Takeyama and Lijima (1988). The tool flank wear observed at the cutting speed of 175 m/min is more than the tool flank wear observed at lower cutting speed. From the results, it is clearly evident that with the increasing cutting velocity, abrasion, diffusion and deformation due to thermal plasticity effects become pronounced indicating that most of the mechanisms of the tool wear has been thermal dependent. Since polymeric composites are poor conductor of heat, most of the mechanisms are operative during machining of composites (Santhanakrishnan et al., 1993). During machining, the feed rate is also one of the important factors, which affects the tool wear. The higher feed rate leads to the increase of chatter during machining, which in-turn produces more wear. The observed results shown proved that the main factor, which affects the tool wear, is cutting speed. The depth of cut only plays small role on composite machining process. The results indicated that the tool flank wear is minimum at a depth of cut of 1.5 mm. In machining, the interaction between the chosen factors also plays some role in deciding the wear of cutting tools. The results indicate that the interactions namely AB, AC, BD and CD have significant effect on tool wear. Out of four main factors considered, cutting speed is the most significant factor which affecting the tool wear; while depth of cut is the least significant parameter. Among the interactions, the interaction between cutting speed and depth of cut is more significant than other parameters.
6.
Conclusion
Using design of experiments technique, the parameters, which are having significant influence on tool flank wear on the machining of GFRP composites, have been studied. (1) This experimental technique is more easy and convenient technique used to study the main and interaction effects of different influential combinations of machining parameters affecting tool wear. (2) Cutting speed is the factor, which has greater influence on tool flank wear, followed by feed rate. (3) The interactions also play some role in deciding the tool wear on the machining of GFRP composites. The interaction between cutting speed and depth of cut has more influence comparing with other interactions on tool flank wear on the machining of GFRP composites. (4) The parameters considered in the experiments are optimized to attain minimum tool flank wear using response table, effect graph, normal probability plot, interaction graphs and ANOVA technique.
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(5) The optimization procedure can be used to predict the tool flank wear for turning of GFRP composites within the ranges of variable studied. However, the validity of the procedure is limited to the range of factors considered for the experimentation. This procedure does not give exact optimal solutions, which is the limitation of this study.
Acknowledgement The authors are very grateful to Dr. L. Karunamoorthy of Department of Mechanical Engineering, Anna University, Chennai, India, and Dr. V. Balasubramaniam of the Faculty of Manufacturing Engineering, Annamalai University, India, for the support rendered. The authors are indebted to Strategic Engineering (P) Ltd., Chennai, India, for supplying the composite material for experimentation.
references
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Koplev, A., Lystrup, A., Vorm, T., 1983. The cutting process, chips and cutting forces in CFRP. Composites 14 (4), 371–376. Kunjur, A., Krishnamurty, S., 1997. A robust multi-criteria optimization approach. Mech. Mach. Theor. 32, 797–810. Lochner, R.H., Mater, J.E., 1990. Designing for Quality. Chapman & Hall, London. Nair, V.N., 1992. Taguchi’s parameter design: a panel discussion. Technometrics 34, 127–161. Palanikumar, K., 2004. Studies on machining characteristics of glass fiber reinforced polymer composites. Ph.D. Thesis. Anna University, Chennai, India. Palanikumar, K., Davim Paulo, J., 2007. Mathematical model to predict tool wear on the machining of glass fibre reinforced plastic composites. Mater. Des. 28, 2008–2014. Palanikumar, K., Karunamoorthy, L., Karthikeyan, R., 2004. Optimal Machining parameters for achieving minimal tool wear in turning of GFRP composites. Int. J. Manuf. Sci. Prod. 6 (3), 119–128. Palanikumar, K., Karunamoorthy, L., Karthikeyan, R., 2006. Assesment of factors influencing surface roughness on the machining of GFRP composites. Mater. Des. 27, 862–871. Ravi, S., Balasubramanian, V., Babu, S., Nemat Nasser, S., 2004. Assesment of factors influencing the fatigue life of strength mis-matched HSLA steel weldments. Mater. Des. 25, 125–135. Sakuma, K., Seto, M., 1983. Tool wear in cutting glass-fibre-reinforced-plastics (the relation between fibre orientation and tool wear). Bull. JSME 26 (218), 1420–1427. Santhanakrishnan, G., Krishnamoorthy, R., Malhotra, S.K., 1988. Machinability characteristics of fibre reinforced plastics composites. J. Mech. Work. Technol. 17, 195–204. Santhanakrishnan, G., Krishnamoorthy, R., Malhotra, S.K., 1989. High speed steel tool wear studies in machining of glass fiber reinforced plastics. Wear 132, 327–336. Santhanakrishnan, G., Krishnamoorthy, R., Malhotra, S.K., 1993. Flank wear and spalling of ceramics during machining of polymeric composites. In: Proceedings of ASM Materials Congress Materials Week’93, Pittsburgh, PA, USA, pp. 109–116. Strategic, 2002. Piping manual. Strategic Engineering (P) Ltd., Chennai, India. Takeyama, H., Lijima, 1988. Machinability of glass fibre reinforced plastics and application of ultrasonic machining. Ann. CIRP 37/1, 93–96. Tondon, S., Jain, V.K., Kumar, P., Rajurkar, P., 1989. Investigations in to machining of composites. Prec. Eng. 12 (4), 227–238. William, C.G., Cox, G.M., 1957. Experimental Designs. John Wiley & Sons, New York. Yildiz, A.R., Ozturk, F., 2006. Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. J. Eng. Manuf. 220 (12), 2041–2053.
journal homepage: www.elsevier.com/locate/jmatprotec
Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools K. Palanikumar a,∗ , J. Paulo Davim b a
Department of Mechanical & Production Engineering, Sathyabama University, Chennai 600119, Tamilnadu, India Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193 Aveiro, Portugal b
a r t i c l e
i n f o
a b s t r a c t
Article history:
Glass fibre-reinforced plastics (GFRP) composite materials are used in many different engi-
Received 12 April 2007
neering fields. The need for machining of GFRP composites has not been eliminated fully.
Received in revised form
The tool wear reduction is an important aspect during machining. In the present work,
4 February 2008
an attempt has been made to assess the factors influencing tool wear on the machining
Accepted 13 February 2008
of GFRP composites. Experimental design concept has been used for experimentation. The machining experiments are carried out on lathe using two levels of factors. The factors considered are cutting speed, fibre orientation angle, depth of cut and feed rate. A procedure
Keywords:
has been developed to assess and optimize the chosen factors to attain minimum tool wear
Optimization
by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interac-
Glass fibre-reinforced plastics
tion graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that cutting
(GFRP) composites
speed is a factor, which has greater influence on tool flank wear, followed by feed rate. Also
Tool wear
the determined optimal conditions really reduce the tool flank wear on the machining of
Response table
GFRP composites within the ranges of parameters studied.
Analysis of variance (ANOVA)
© 2008 Elsevier B.V. All rights reserved.
Normal probability plot
1.
Introduction
Today fibre-reinforced plastics (FRPs) have an important place in the field of engineering materials. They are increasingly being used for varieties of engineering applications from automotive to aircraft components. They have attractive properties than other materials such as high strength to weight ratio, high fracture toughness, and excellent corrosion and thermal resistances (Strategic, 2002). Because of the different applications, the machining of FRP cannot be eliminated. The machining of FRPs are necessary to obtain near-nett shape and for precision fits. The first unique material removal character-
∗
istics in orthogonal cutting of FRPs was presented by Koplev et al. (1983). They have discussed the formation of the chips and the quality of machined surface in cutting of unidirectional carbon fibre-reinforced plastics. They have measured the cutting forces parallel and perpendicular to the cutting direction and discussed the formation of chips and wear of the tool. Sakuma and Seto (1983) conducted face-turning tests. They have measured cutting resistance and surface roughness for analyzing the machinability and tool wear in cutting of glass fibre-reinforced plastics. They have studied how fibre orientation influences both the quality of the machined surfaces and tool wear. Bhatnagar et al. (1995) has studied the machinabil-
Corresponding author. Tel.: +91 44 22280226. E-mail addresses: palanikumar [email protected] (K. Palanikumar), [email protected] (J.P. Davim). 0924-0136/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2008.02.020
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ity of unidirectional carbon fiber reinforced plastic laminates with different fibre orientation. They have presented a model for predicting the cutting forces and the dependence of cutting direction on machinability requirements. The effect of tool wear on cutting forces in the orthogonal cutting of unidirectional glass fibre-reinforced plastics has been studied in depth by Caprino et al. (1996). They have found a strict correlation between the flank wear and vertical force. The machining of fibre-reinforced composite materials differs from normal metal cutting. The behaviour of composites is heterogeneous and depends upon the fibre and matrix properties, orientation of fibres, bond strength between fibre and matrix, and the type of weave. Therefore, the machining of these materials requires special consideration about the wear resistance properties of the tool materials and the cutting tool geometry (Tondon et al., 1989). Davim and Mata (2004a) have studied the influence of cutting parameters on surface roughness in turning glass fibre-reinforced plastics using statistical analysis. The objective has been focused on obtaining the contribution percentages of the cutting parameters (cutting velocity and feed rate) on the surface roughness on the workpiece. Subsequently, these authors conducted the new optimization study of surface roughness in turning GFRP tubes manufactured by filament winding and hand lay-up, using polycrystalline diamond cutting tools. The objective has been in establishing the optimal cutting parameters to obtain a certain surface roughness in the GFRP workpieces, using multiple analysis regression (Davim and Mata, 2004b). When GFRP composites are machined, the glass fibres break-up by rupturing and shearing, thus producing rapid tool wear. Tool wear is an important response in machining process. The tool integrity plays an important role in the form of the machined surface of the workpiece and the control of the cutting quality. Unlike the machining of traditional materials, problems are encountered during the machining of FRP’s. The typical problems encountered are (Santhanakrishnan et al., 1988, 1989): 1. Material delamination: These problems encountered when the resin/filler materials do not back up the fibre materials adequately. 2. Thermal damage: Differential expansion and thermal conductivity between the fibres and filler materials result in thermal damage. 3. Machining tough fibres: Machining of tough fibres results in a fuzzy surface, damaging the cutting tool considerably. The studies on machining GFRP composites indicates that reduction of tool wear has been found very difficult and further research is required to have an effective control. (Caprino et al., 1996; Santhanakrishnan et al., 1988, 1989; Palanikumar, 2004; Palanikumar et al., 2004, 2007). In the present work, experimental design scheme in William and Cox (1957) is used for the experimentation and ANOVA is used for analysis. This experimental scheme is almost similar to the L16 orthogonal array. A procedure has been introduced to assess and optimize the chosen factors to attain minimum tool wear by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of variance (ANOVA) technique.
2.
Scheme of investigation
In order to achieve the desired tool wear on the GFRP composite workpiece, the present investigation has been planned in the following steps: (i) identifying the important factors, which influence the tool wear on the machining of GFRP composites; (ii) finding the upper and lower limits of the factors identified; (iii) developing the experimental design matrix using design of experiments; (iv) conducting the experiments as per the design matrix; (v) assessing the factors and its effects using response table and effect graph; (vi) assessing the real or chance effect of factors using normal probability plot; (vii) analyzing the results using ANOVA; (viii) optimizing the chosen factor levels to attain minimum tool flank wear.
2.1.
Identifying the important factors
Based on the previous published results (Sakuma and Seto,1983; Bhatnagar et al., 1995; Caprino et al., 1996) and experience in the field by authors (Palanikumar et al., 2004, 2007), the machining parameters, which are having significant effect on tool wear in machining of GFRP composites have been identified. The machining parameters identified are: (i) cutting speed; (A), (ii) workpiece (fibre orientation) angle (B); (iii) depth of cut (C); (iv) feed rate (D). Out of the four parameters considered, fibre orientation angle has been specially applied to fibre-reinforced composite materials.
2.2. Finding the upper and lower limits of the factors identified For finding the upper and lower limits of the machining parameters, a detailed analysis has been carried out. The limits identified are discussed below (Palanikumar and Davim, 2007): (i) The studies related to machining of GFRP composites indicate that cutting speed is the factor, which highly influences the tool wear. Also the increase of cutting speed increases the tool wear. The higher cutting speed leads to large deformation rate of glass fibre in the composite and subsequently produces severe tool wear (Hasegawa et al., 1984). In machining of GFRP composites, during machining, at lower cutting speed, large material flow with cut fibers can be produced and it leads to high surface roughness (Palanikumar et al., 2006). For maintaining the proper surface roughness with less tool wear, the cutting speed has been set at reasonable level of being 75 and 175 m/min. (ii) Workpiece fibre orientation is another important factor which influences tool wear. Higher fibre orientation needs more forces to remove the material from the workpiece. At larger fibre orientation angles, the shear takes place
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Table 1 – Control parameters and their levels S. no.
Parameter
Notation
Unit
Levels Low (−1)
1. 2. 3. 4.
Cutting speed Fibre orientation angle Depth of cut Feed rate
A B C D
along the fibres and it generates compressive stress on the rake face of the tool (Takeyama and Lijima, 1988), which leads to high tool wear. In the present study, the fibre orientation angle considered in the workpiece is 30◦ and 90◦ . The fiber orientation angle has been fixed as per the availability from the manufacturer. (iii) In machining of composites, depths of cut play only a small role, but abnormal increase of depth of cut increases the heat generation and wear on tool. Also high depth of cut produce deleterious effect on surface quality of GFRP workpiece. The depth of cut has been chosen based on the previous study (Palanikumar and Davim, 2007) and is 0.50 and 1.50 mm. (iv) The feed rate also plays a significant role in machining of GFRP composites. The higher feed rate produces heat generation, and it leads to high tool wear (Palanikumar et al., 2004). The increases in feed rate also increase the chatter that in turn leads to more tool wear. Hence low feed rates are preferred for machining of GFRP composites. Very low feed rate leads to the incomplete machining of fibers in the composites and hence the feed rate has been selected at reasonable level and is 0.10 and 0.50 mm/rev.
2.3. Developing the experimental design matrix using design of experiments An experiment is a series of trials or tests, which produces quantifiable outcomes. The experiment may be random or deterministic. For experimentation, design of experiment in statistics has been used. The merit of this experimental scheme is that the cost of experimentation is reduced considerably as compared to one factor at a time type experiment. The identified factors and its lower and upper limits are discussed in Sections 2.1 and 2.2. In this experimental scheme, all possible combinations of levels are included so that there are 2n (where n refers to the number of factors, i.e., 24 = 16) trials in the experiment (Cochran and Cox, 1963). The notations, units and their levels chosen are summarized in Table 1. For easy recording and processing of experimental data, the parameters levels are coded as +1 and −1. The intermediate coded value of any levels can be calculated by using the
m/min
High (+1)
75 30 0.5 0.10
◦
mm mm/rev
175 90 1.5 0.50
Table 2 – Composition of GFRP composite Material
Type
Matrix Hardener Reinforcement
Epoxy: Araldite, LY 556 HT 972 E-glass fiber (Multi-filament roving) (linear density: 2400 Tex)
following expression (Ravi et al., 2004): Xi =
X − [Xmax + Xmin /2] [Xmax − Xmin /2]
(1)
where Xmax is the upper level of the parameter, Xmin is the lower level of the parameter and Xi is the required coded values of the parameter of any value of X from Xmin to Xmax .
2.4.
Conducting the experiments
The composite pipes used in this experimental study have been produced by filament winding process. Filament winding is an effective method to manufacture composite pipes and this process gives very high directional strength with precise alignment of fibres. The constituent materials of the composite, and its mechanical properties are given in Tables 2 and 3. The experiments are conducted for turning operation in all geared lathe. The ISO specification of the tool used for the turning operation is a WIDAX tool holder PC LNR 1616 K12. The insert used is a coated carbide tool having composition: Co 6.0%; composite carbide 8.0%; WC rest. The coating layer system is: CVD-TiC + Ti(C, N) + Al2 O3 and layer thickness used is 11 m. The machining tests are carried out as per the design matrix at random to avoid systematic errors. The design matrix and the corresponding responses are given in Table 4. The response studied is tool flank wear. The tool flank wear (Vb ) has been measured with respect to a machining time at predetermined intervals using radical toolmakers’ microscope. The machining time considered for the each experiment is 9 min. The higher time frame is chosen for the experimentation for arriving and analyzing the influence of factors fully. The experiments are conducted for three
Table 3 – Mechanical properties of composite material Material Composite
Tensile strength u (MPa)
Tensile modulus, E (GPa)
Shear modulus, G (GPa)
Poisson’s ratio,
930
E1 = 46 E2 = 13
5
12 = 0.3 21 = 0.08
Mass density, (kg/m3 ) 1876
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Table 4 – Design matrix and corresponding output response Expt. no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A
B
C
D
−1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1 −1 1
−1 −1 1 1 −1 −1 1 1 −1 −1 1 1 −1 −1 1 1
−1 −1 −1 −1 1 1 1 1 −1 −1 −1 −1 1 1 1 1
−1 −1 −1 −1 −1 −1 −1 −1 1 1 1 1 1 1 1 1
Tool flank wear (mm) 0.41 0.91 0.85 1.02 0.31 0.69 0.78 0.67 0.72 1.16 0.78 1.21 0.79 0.95 1.02 0.99
0.49 0.87 0.81 1.06 0.33 0.67 0.71 0.64 0.70 1.08 0.73 1.16 0.72 0.98 0.98 1.04
0.45 0.95 0.77 0.98 0.29 0.65 0.85 0.70 0.74 1.12 0.83 1.26 0.65 0.96 1.05 0.94
times and are tabulated. The experimental results are given in Table 4.
3. Influences of machining parameters and their effects on tool wear The effect of factors which influences the tool flank wear on composite machining process has been analyzed through: (i) response table and effect graph, (ii) normal probability plot; (iii) ANOVA technique.
3.1.
Response table and effect graph
The influence of machining parameters on tool flank wear has been performed using response table. Normally in engineering problems, higher order interactions do not reveal any significant effect and hence three and four factor interactions are not considered in this study. Only main factors and two factor interactions are considered for the present study. The response table for main and two factor interactions at two different levels are shown in Table 5. For analysis the average of three readings are taken. Response tables are used to simplify the calculations needed to analyze the experimental data. In response table, the effect of factor on a response variable is the change in the response when the factor goes from its low level to its high level. In table if effect of a factor is greater than zero, the average response is higher for the higher level of the factor than for the low level. However, if the estimated effect is less than zero (−ve), it indicates that the average response is higher at low level of the factor than at high level. If the effect for a factor is very small, then it is probably because of random variation than a ‘real’ factor effect. The graphical display (Ravi et al., 2004) such as effect graph can be used in conjunction with a response table to identify appropriate settings for machining parameters to minimize the tool flank wear. The effect of main and interaction factors derived for composite machining process is plotted in Fig. 1. From figure, it is inferred that larger the vertical line, the larger the change in tool wear when changing from level −1 to level +1 for a factor. It will be pointed out that the statistical significance of a factor is directly related to the length of the vertical line.
Fig. 1 – Effect graph.
3.2.
Normal probability plot
In effect graph, it is found that some of the factor effects are larger than the other, but it is not clear, whether these results are ‘real’ or ‘chance’. To identify the ‘real’ effects, normal probability plot is used and shown in Fig. 2. Normal plot is a graphical technique based on “Central limit theorem”. The procedure for constructing the normal probability plot is given
Fig. 2 – Normal probability plot.
1.02 0.99 1.02 0.99
0.78 1.21
0.72 1.12 0.78 1.21
0.81 1.02
0.78 0.67
0.45 0.91 0.81 1.02
0.99
0.72 0.96 0.96
1.21
0.72 1.12 1.12
0.67
0.31 0.67 0.67
0.91
1.02
0.45 0.91
1.02
0.72
0.78
0.72
0.78
0.31
0.81
3.3.
Analysis of variance
The normal probability plot has the disadvantage of not providing a clear criterion for what values for estimated effects indicate significant factor or interaction effects. In addition, how do we measure amount of departure from the straightline pattern. ANOVA meets this need by how much an estimate must differ from zero in order to be judged “statistically significant”. The ANOVA result is presented in Table 6. This analysis has been carried out for a level of significance of 5%, i.e., for a level of confidence of 95%. From the ANOVA results, it is concluded that the factors A, B, C, D and their interactions AB, AC, BD and CD have significant effect on tool flank wear and AD, BC have no effect at 95% confidence level. As the interaction effect of AB, AC, BD and CD seems to be significant to the tool flank wear, the average values of the tool flank wear are calculated for all the combinations. By using the values of interaction, the significant interaction graphs are drawn for each combination of levels. When comparing with previous studies on machining of GFRP composites (Palanikumar and Davim, 2007) using same set of experimental conditions, the analysis with repetition of experiments provides best results. In the previous study (Palanikumar and Davim, 2007), only the interactions AB and AC are significant. But in the present analysis, the interaction effects such as BD and CD also are significant. The significant interactions between the parameters (AB, AC, BD and CD) are shown in Figs. 3–6. The insignificant interactions (AD and BC) are shown in Figs. 7 and 8. In these figures the lines are parallel to each other, which show that there is no interaction between parameters. By analyzing these figures also evident from ANOVA analysis, it has been concluded that AB and AC are more interactive than other interactions.
0.45 0.95 0.77 0.98 0.29 0.65 0.85 0.70 0.74 1.12 0.83 1.26 0.65 0.96 1.05 0.94
0.45
515
elsewhere (Lochner and Mater, 1990). As per the normal probability plot, the points which are close to a line fitted to the middle group of points represent estimated factors which do not demonstrate any significant effect on the response variable. On the other hand, the points appear to be far away from the straight line are likely to represent the ‘real’ factor effects on the tool flank wear. From Fig. 2, it has been asserted that the main factors A, B, C, D and their two factor interactions AB, AC, BD and CD are quite away from the straight line and are considered to be significant.
0.49 0.87 0.81 1.06 0.33 0.67 0.71 0.64 0.70 1.08 0.73 1.16 0.72 0.98 0.98 1.04 0.41 0.91 0.85 1.02 0.31 0.69 0.78 0.67 0.72 1.16 0.78 1.21 0.79 0.95 1.02 0.99 1. 2. 3. 4. 5. 6. 7. 8. 9. 10 11 12 13 14 15 16
Average Effect
−1 +1 −1
−0.6983 0.9442 −0.7329 0.9096 −0.8775 0.7650 −0.7025 0.9400 −0.8873 0.7613 −0.7567 0.8858 −0.8134 0.8292 −0.8104 0.8321 −0.8504 0.7921 −0.7825 0.8600 0.2459 0.1767 −0.1125 0.2375 −0.1260 0.1291 0.0158 0.0217 −0.0583 0.0775
0.31 0.67 0.78 0.67
0.72 0.96 1.02 0.99
0.45 0.91 0.81 1.02 0.31 0.67 0.78 0.67
+1 +1
−1
C B A Tool flank wear (mm) S. no.
Table 5 – Response table for main factors and two factor interactions
−1
D
0.72 1.12 0.78 1.21 0.72 0.96 1.02 0.99
0.96 1.02
1.12 0.78
1.21 0.72
1.02
0.99
0.96 1.21 0.72
1.12
0.99
0.96
1.21
1.12 0.78
0.78 0.67 0.72 0.67 0.72
0.78
0.72
0.78
0.78 1.21 0.72 0.96
1.02 0.99
1.02 0.99
0.78 1.21
0.78 0.67 0.72 1.12 0.67 0.72
0.67 0.31 0.67
+1
0.91 0.81
0.67 0.78
0.45
1.02 0.31
0.91
1.02 0.31
0.45
0.81
0.45
0.81
0.91
1.02
0.81 1.02 0.31 0.67
0.78 0.67 0.72 1.12
0.31 0.67
0.45 0.91 0.45 0.91
−1 +1 −1 +1 −1 +1 −1
0.72 0.96
0.31 0.67 0.78 0.67 0.72 1.12 0.78 1.21 0.81 1.02
+1 −1 +1
BD BC AD AC AB
0.72 0.96 1.02 0.99
0.45 0.91 0.81 1.02
+1 −1
CD
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Fig. 3 – Interaction between A and B.
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Table 6 – ANOVA test results Factors
Sum of squares
DOF
A B C D AB AC AD BC BD CD Error
0.72521 0.37453 0.15188 0.67687 0.17280 0.20021 0.00301 0.00563 0.04083 0.07208 0.16468
1 1 1 1 1 1 1 1 1 1 37
Total
2.58773
47
Mean square (MS) 0.72521 0.37453 0.15188 0.67687 0.17280 0.20021 0.00301 0.00563 0.04083 0.07208 0.00445
Fratio
P
162.94 84.15 34.12 152.08 38.83 44.98 0.68 1.27 9.17 16.19
0.000 0.000 0.000 0.000 0.000 0.000 0.416 0.268 0.004 0.000
Fig. 6 – Interaction between C and D.
Fig. 4 – Interaction between A and C.
(i) (ii) (iii) (iv)
cutting speed at low level (75 m/min); workpiece (fibre orientation angle) at low level (30◦ ); depth of cut at high level (1.5 mm); feed rate at low level (0.10 mm/rev).
Based on the above optimum conditions, the minimum value of tool flank wear can be obtained from the following expression using the values form response table (Table 5)
Fig. 5 – Interaction between B and D.
4. Optimizing the chosen factor levels to attain minimum tool flank wear From the analysis of response graph, response table, and interaction graphs, the optimal machining parameters for the GFRP machining process is achieved for the minimum value of tool flank wear. The optimal conditions arrived are
Fig. 7 – Interaction between A and D.
517
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Fig. 8 – Interaction between B and C.
(Lochner and Mater, 1990 and Ravi et al., 2004). Vb(min) = [grand mean] +[contribution of A] + [contribution of B] +[contribution of C] + [contribution of D] +[contribution of AB] + [contribution of AC] +[contribution of BD] + [contribution of CD]
(2)
In this work, experimental design method and ANOVA have been used for optimization of parameters. The experimental design is similar to the orthogonal array proposed by Taguchi. Normally Taguchi’s method uses signal-to-noise ratios for analysis. Box (1988) has discussed signal-to-noise ratios, performance criteria and transformations. He has suggested a data analytic method instead of signal-to-noise ratios. Many of the concerns about the robust design using orthogonal array is clearly debated and is summarized in the extensive panel discussion by Nair, 1992. Ayudthya and McDowell have compared Taguchi’s methodology and variance from slope (VFS) approach. They have concluded that the VFS approach is better than Taguchi’s design. Kunjur and Krishnamurthy have used a multi-criteria optimization approach for a robust design. They have identified a Pareto-optimal solution set based on relative effects of the various factors on the objective function. Recently Yildiz and Ozturk (2006) applied hybrid enhanced genetic algorithm. They have introduced a refined design space for population in genetic algorithm by integrating robust design parameter concept. While seeing the above, the experimental design method and ANOVA table can find only upper or lower limits of variables (parameters) as optimum value. If optimum value is between the levels and then experimental design method and ANOVA table are not effective to find real optimum values. They are effective only to refine the range of variables considered, which is the limitation of this study.
Vb(min) = V¯ b + [A(−1) − V¯ b ] + [B(−1) − V¯ b ] + [C(+1) − V¯ b ] + [D(−1) − V¯ b ] + [(AB(+1) − V¯ b )] + [AC(+1) − V¯ b ] + [(BD(+1) − V¯ b )] + [CD(−1) − V¯ b ]
5.
Discussions
(3)
Vb(min) = 0.82125 + [0.6983 − 0.82125] + [0.7329 − 0.82125] +[0.7650 − 0.82125] + [0.7025 − 0.82125] +[0.76125 − 0.82125] + [0.7567 − 0.82125] +[0.8504 − 0.82125] + [0.7825 − 0.82125] = 0.308 ≈ 0.30 nm
(4)
The above result reveals that the minimum tool flank wear on the machining of GFRP composites within the range of factor under investigation is 0.30 mm. The validity of the optimization procedure has been checked through confirmation experiments (Table 7). The variation is within the permissible limit. It can be seen from the confirmation experimental results that the tool flank wear of the machining process has greatly improved by the optimal setting of machining parameters. The optimum condition obtained through this study is only the near optimal solution.
In machining of GFRP composites, tool flank wear plays an important role and is a factor of great importance in maintaining the accuracy of products. During machining, the tool has been in contact with workpiece. The usage of tool for a particular time leads to wear. The factors which affect the tool state are many but the machining parameters such as cutting speed, feed rate and depth of cut and workpiece fibre orientation have a significant influence on the tool flank wear. From the available literature, it has been known that the mechanism of cutting in GFRP is due to the combination of plastic deformation, shearing and bending rupture. The above mechanism depends on flexibility, orientation and toughness of the fibres (Santhanakrishnan et al., 1989). In the machining of conventional metals, abrasion occurs due to ploughing into softer matrix by hard constituents such as segregated carbides or adhesion and formation of metallic bonds formed over the rubbing surfaces under load and subsequent rupture of these bonds followed by transfer of these elementary particles and some times chemical diffusion occurs in cutting tool (Bhattacharyya, 1996). However, the chip generated in machining of GFRP composite material is different and is of powder
Table 7 – Results of the confirmation trials and their comparison with the results Setting levels Speed = 75 m/min, Fibre orientation angle 30◦ Depth of cut = 1.5 mm Feed rate = 0.10 mm/rev
Expt. no. 1. 2. 3.
Predicted tool flank wear (mm) 0.30 0.30 0.30
Experimental values (mm) 0.29 0.27 0.31
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Fig. 9 – Typical wear patterns observed on the flank (a) and rake (b) faces of the tool after machining of GFRP composites.
form. Since the powdery chips are not flowing over the rake face, crater wear normally does not occur; instead the hard glass fibres abrade the tool edge. Also during machining of FRP, the tool continuously encounters alternate matrix and fibre materials, whose response to machining can vary greatly because of the above properties. The cutting zone experiences both thermal and mechanical stresses. The cutting nose may be subjected to localized dynamic loading, due to the variations in properties of matrix and fibre. This dynamic load may cause tool failure due to possible low cycle fatigue apart from the usual types of wear (Santhanakrishnan et al., 1988). When machining of GFRP at high cutting speeds, the tool tip is intact and there is no indication for any chipping. A reaction product sticking to the surface of the tool is probably a mixture of resin with small fibers (Palanikumar et al., 2004). In FRP machining, the cutting tool would be subjected to flank wear associated with adhesion and intense abrasion. The worn out cutting edges of the tool was examined using scanning electron microscope (SEM). Typical micrographs of wear patterns observed on tool are shown in Fig. 9. In figure (a), it can be seen that the flank wear observed is associated with abrasion marks. This may be attributed to the abrasive nature of the fibres that are used with workpiece material. The
presence of grooves which were on the worn flank surfaces indicate that the wear of tool is due to plastic deformation, shearing and bending rupture. Figure (b) shows the rake face of the worn tool. From the results, it can be seen that the tool flank wear is minimum at 30◦ fibre orientation angle. The tool flank wear increases with the increase of fibre orientation angle. The tool flank wear has been increased more rapidly with the increase of fibre orientation. The reason being at larger fibre angles, compressive strain is generated within the workpiece material. This finding has close relationship with the results presented by Takeyama and Lijima (1988). The tool flank wear observed at the cutting speed of 175 m/min is more than the tool flank wear observed at lower cutting speed. From the results, it is clearly evident that with the increasing cutting velocity, abrasion, diffusion and deformation due to thermal plasticity effects become pronounced indicating that most of the mechanisms of the tool wear has been thermal dependent. Since polymeric composites are poor conductor of heat, most of the mechanisms are operative during machining of composites (Santhanakrishnan et al., 1993). During machining, the feed rate is also one of the important factors, which affects the tool wear. The higher feed rate leads to the increase of chatter during machining, which in-turn produces more wear. The observed results shown proved that the main factor, which affects the tool wear, is cutting speed. The depth of cut only plays small role on composite machining process. The results indicated that the tool flank wear is minimum at a depth of cut of 1.5 mm. In machining, the interaction between the chosen factors also plays some role in deciding the wear of cutting tools. The results indicate that the interactions namely AB, AC, BD and CD have significant effect on tool wear. Out of four main factors considered, cutting speed is the most significant factor which affecting the tool wear; while depth of cut is the least significant parameter. Among the interactions, the interaction between cutting speed and depth of cut is more significant than other parameters.
6.
Conclusion
Using design of experiments technique, the parameters, which are having significant influence on tool flank wear on the machining of GFRP composites, have been studied. (1) This experimental technique is more easy and convenient technique used to study the main and interaction effects of different influential combinations of machining parameters affecting tool wear. (2) Cutting speed is the factor, which has greater influence on tool flank wear, followed by feed rate. (3) The interactions also play some role in deciding the tool wear on the machining of GFRP composites. The interaction between cutting speed and depth of cut has more influence comparing with other interactions on tool flank wear on the machining of GFRP composites. (4) The parameters considered in the experiments are optimized to attain minimum tool flank wear using response table, effect graph, normal probability plot, interaction graphs and ANOVA technique.
j o u r n a l o f m a t e r i a l s p r o c e s s i n g t e c h n o l o g y 2 0 9 ( 2 0 0 9 ) 511–519
(5) The optimization procedure can be used to predict the tool flank wear for turning of GFRP composites within the ranges of variable studied. However, the validity of the procedure is limited to the range of factors considered for the experimentation. This procedure does not give exact optimal solutions, which is the limitation of this study.
Acknowledgement The authors are very grateful to Dr. L. Karunamoorthy of Department of Mechanical Engineering, Anna University, Chennai, India, and Dr. V. Balasubramaniam of the Faculty of Manufacturing Engineering, Annamalai University, India, for the support rendered. The authors are indebted to Strategic Engineering (P) Ltd., Chennai, India, for supplying the composite material for experimentation.
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