Machinability study of glass fibre-reinforced polymer ... - Springer Link

Int J Adv Manuf Technol (2013) 64:247–261 DOI 10.1007/s00170-012-4006-6

ORIGINAL ARTICLE

Machinability study of glass fibre-reinforced polymer composites during end milling A. I. Azmi & R. J. T. Lin & D. Bhattacharyya

Received: 22 March 2011 / Accepted: 13 February 2012 / Published online: 8 March 2012 # Springer-Verlag London Limited 2012

Abstract Machining of composite materials is usually performed to achieve required geometrical shapes and dimensional tolerances. However, machinability evaluation of glass fibre-reinforced polymer (GFRP) composites in end milling has not yet received its due attention in the research community despite the extensive industrial use of this process. This work aims to elucidate the end milling machinability of GFRP composites with respect to surface roughness, tool life and machining forces. Experiments were conducted under different experimental parameters and their levels according to the Taguchi design of experiment method. Taguchi analysis combined with statistical analysis of variance (ANOVA) was performed to quantify the effects of spindle speed, feed rate and depth of cut on those characteristics. Multiple regression analysis (MRA) was also employed to establish parametric relationships between the experimental parameters and the machinability outputs. Results from ANOVA and MRA reveal that feed rate is the governing factor affecting all the machinability outputs. The calculated values from MRA have been found to be fairly close to experimental values in almost all cases. Validation tests under randomly selected machining conditions have further demonstrated the feasibility of the developed mathematical models with 8–12% error for tool life and machining forces predictions while >19% error for calculating the surface roughness.

A. I. Azmi (*) : R. J. T. Lin : D. Bhattacharyya Centre for Advanced Composite Materials, Department of Mechanical Engineering, The University of Auckland, Private Bag, 90192 Auckland, New Zealand e-mail: [email protected]

Keywords End milling machinability . GFRP composites . Taguchi method . Design of experiments . Multiple regression analysis

1 Introduction Composite materials have found a wide range of applications in various industrial components and structures where high performance of the materials is often required. Among different composite materials, fibre-reinforced polymer (FRP) composites show a tremendous increase in application due to combined properties of high strength or stiffness, low density, good corrosion as well as fatigue resistance. Although in common occurrence, composite products are manufactured with near-net shapes, secondary processes involving machining are often necessary as to achieve the required geometrical shapes and dimensional tolerances. Indeed, various machining processes such as turning, drilling and milling have been used to machine composite materials for different product requirements. Despite the existing experience and knowledge in machining traditional materials such as metals, it has been a challenge to maintain consistent results in terms of machining quality for composite materials. First, due to fibre arrangements and/or orientation, poor surface finish which includes fibre pullout, matrix delamination, sub-surface damage and matrix polymer failures is usually observed if improper machining conditions were employed [1]. Secondly, tool sharpness is greatly affected by the abrasiveness of the fibres embedded in composite materials. Inadequate sharpness accounted for the increase in tool forces and heat generation on machined surface [2]. These result in performance degradation of the machined part and put a restriction on the widespread usage of FRP

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composites. Of all the conventional machining processes, studies on drilling of composite materials have received the most attention among researchers owing to the need for component assembly as well as to achieve delaminationfree composite materials [3–6]. Furthermore, some fundamental studies on machinability of FRP composites can also be found from in the literature with regard to turning and orthogonal cutting of these materials due to its relatively simple cutting mechanism [7–11]. Comparatively speaking, studies in end milling of FRP composites have not yet received its due attention until recently. This is despite being one of the most widely used material removal processes in industry. This machining process is usually employed for trimming of composite parts as well as for removing of the excess material to achieve final shape and dimensional tolerance. Based on currently available literature, Puw and Hocheng were the first to report some preliminary results of milling uni-directional carbon fibre-reinforced plastic (CFRP) composites [12, 13]. However, only a handful of researchers, hitherto, have reported experimental results on limited aspects of FRP's milling machinability characteristics, such as machining forces and delamination damage [12–20]. Even though Davim et al. have reported some promising results with regard to improving surface quality and reducing delamination damage of machining FRP composites [14, 15], their studies were limited to only two machining parameters, namely, feed rate and the cutting speed. Furthermore, performance of machinability in terms of tool wear and tool life were not disclosed. On the other hand, Sheikh Ahmad et al. developed a force prediction model during end milling of uni-directional CFRP composites with different fibre orientations [19], yet their study was limited to relatively low machining conditions. Hence, summarising on some of the reported studies in milling FRP composites, it can be concluded that a complete machinability evaluation for glass fibre-reinforced polymer (GFRP) composites using a proper design of experiment technique has not been fully addressed. It is important to note that current knowledge from turning FRP composites and milling conventional metals cannot often be directly applied to end milling of FRP composites. Consequently, this paper investigates the effects of machining parameters on different machinability characteristics of GFRP composites using Taguchi design of experiment (DOE) method. This method was employed to systematically formulate the experimental layout, analyse the significant influence of each experimental parameter using statistical analysis of variance (ANOVA) and finally predict the optimal parametric combination to yield the best machining conditions. It is worth to highlight that the statistical analyses reported herein focuses solely on either minimisation or maximisation of individual machinability output. This is due

Int J Adv Manuf Technol (2013) 64:247–261

to the fact that simultaneous optimisation of end milling GFRP composites would be difficult due to contradictory effects of each machinability output. In addition to Taguchi analysis, multiple regression analysis (MRA) has also been employed to establish parametric relationships between machining parameters and the different machinability outputs, namely, surface roughness, machining forces and tool life. The adequacy of the developed models has been confirmed through the coefficient of determination (R2) and confirmation tests under randomly selected conditions.

2 Experimental procedure 2.1 Sample manufacturing The GFRP composite panels for this study were manufactured using vacuum-assisted resin transfer moulding, resulting in high laminate quality, i.e. higher volume fraction and less number of voids. The laminates consisted of 16 layers of uni-directional E-glass fibre (EU450-1270 supplied by SP High Modulus (NZ)) with epoxy resin as the matrix material. During the fabrication, the E-glass fibre mats were laid dry on a flat glass mould with vacuum bagging. The mixture of epoxy resin and hardener, (R300 and R310 respectively, Nuplex FGI), at the ratio of 4:1, was then infused into the dry fabrics under a vacuum pressure of approximately 10 mBar. Those infused panels were left over night for curing and were post cured in an oven at 60°C for 30 min. The laminates were cut into plates with a size of 200×135×6 mm using a water-cooled diamond saw for the subsequent machining experiments. Regular monitoring of fibre volume fraction, vf , was performed according to ASTM D3171-09 to ensure the consistency of laminate quality with an average value of vf to be 0.52. 2.2 Machine tool setup and data acquisition The end milling experiments were carried out on a Centroid 1050A (28 kW spindle power and 7,000 rpm maximum spindle speed) machine centre under dry conditions. A vacuum cleaner was used to handle the hazardous chips as well as to minimise the chip interference that could lead to local heat accumulation in the cutting zone [5]. The machining force components were measured with a Kistler® 9265B milling dynamometer, connected to a Kistler® 5,001 charger amplifier and a PC with LabVIEW® data acquisition system. In the current study, the cutting was performed along the fibre and table feed direction, x, Fig. 1. The composite plate was held firmly on top of the dynamometer using cap screws. The surface roughness was measured along the x direction, using a Taylor Hobson Surtronic-3 surface measurer


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with cut-off and traverse lengths of 0.8 and 5 mm, respectively. The centre line average roughness, Ra was used to characterise the surface quality in which, an average of three readings was used as a measurement value representing the machined surface finish at a particular cutting time under each experimental condition. Uncoated tungsten carbide K20 end mill cutter of 12 mm diameter was selected as the cutting tool in line with those used in references [9, 21]. The end mill cutter had the geometry of 30°, 9° and 16° as helix, primary relief and secondary clearance angles, respectively. After a predetermined number of machining passes, the end mill cutter was removed from the tool holder for tool wear measurement under a Leica MZ16 stereo microscope at ×115 magnifications. Within the range of test parameters, mechanical abrasion on the flank face was found to be the dominant type of wear mechanism and was observed on the peripheral edges or flank face of the cutting tool. The average of three flank wear, VB, Fig. 2, for each cutting flute was measured using the Image Tool® software. The tool life was determined by setting the criterion at 0.3 mm of flank wear.

2.3 Taguchi experimental design and selection of parameters Extensive and expensive experimentations (e.g. time/labour/ materials, etc.) would typically be required to evaluate the machinability of a material. Hence, experimental approach of machinability assessments can be well achieved through statistically designed tests or series, commonly known as design of experiment (DOE). DOE methodology involves full factorial as well as partial or fractional approaches. Full factorial experiments may provide all possible effects and interactions, but the scale of experimentations can be prohibitive for scientific investigations. Realistically, fractional factorial approaches such as the Taguchi methodology, which involves significantly fewer tests, but with highly acceptable or reliable results, would be more attractive.

Fig. 2 Scheme of tool flank wear measurement

In this study, Taguchi DOE method was used to design the experimental matrix. Taguchi method systematically plans the experiments according to a specially designed orthogonal array (OA) which can significantly reduce the number of experiments [22]. In Taguchi's OA, each combination of factors has a balance, in which within a column of the array, each factor has equal number of levels or appears at equal number of times. The unique characteristics of GFRP composites affect their machinability differently to those of the traditional homogenous materials. Physical properties of fibre reinforcements and the matrix material, fibre orientation, types, matrix material and volume fraction greatly influence the machinability of GFRP composites apart from processing parameters which includes cutting speed, feed rate and depth of cut, tool materials and geometries. Such a large number of influencing factors inevitably add to the complexity of experimental investigations. Hence, in this part of work, only machining or processing parameters were considered for the parametric analysis of their significant influence. The three important machining parameters, namely feed rate, f; spindle speed, s; and depth of cut, d; that affect the

Fig. 1 End milling setup showing tool feed direction

135.00

Fz Fx

200.00 x

Fy y

250

Int J Adv Manuf Technol (2013) 64:247–261 A

Table 1 Experimental parameters and their levels Factors

2 (Medium)

3 (High)

500 3,000 1

750 4,000 1.5

1,000 5,000 2.0

surface roughness, Ra; tool life, TL and resultant machining force, Fm were chosen for this study. Three different levels, low (1), medium (2) and high (3), of each parameter, in Table 1, encompassing a typical range of machining parameters employed in the industry, have been selected for the experimentation. Justifications for selection of those parameters and the three levels setting were twofold: (1) to incorporate all possible processing parameters which were not covered in the literature and (2) to investigate any nonlinear effects they have on the machinability output. The range of machining conditions was selected owing to the importance of industrial applications, within the limit of the machine tool as well as over the range of conditions employed in the reported literatures [14–18]. Cutting speed or spindle speed has significant influence on the extent of tool wear as well as surface roughness. Initially, the spindle speed was set at 2,000 rpm, but this resulted in premature failure or chipping of the cutting tool edges. This could due to nature of discontinuous cutting actions in milling as the tool encounter inhomogeneous layers of GFRP composites. In addition, spindle speed of 2,000 rpm is deemed to be low as far as machining productivity is concerned. On the hand, higher spindle speed (above 5,000 rpm) leads to rapid tool wear which lasted the tool for only a couple of end milling passes to reach the predefined tool life criteria. Consequently, 3,000–5,000 rpm was set as test range for the spindle speed herein. This range truly represents the typical range of industrial applications. On the contrary, as indicated in the previous studies [12–18], the employed feed rate was reported to be within the range of 200–800 mm/min when machining CFRP composites. Selection of feed rate range during machining is

Fm is resultant force, calculated qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi from Fm ¼ Fx2 þ Fy2 þ Fz2 , where Fx, Fy and Fz are feed, cutting and thrust forces, respectively

AXB

Average of S/N

A1

-6.2 S/N Ratio of Ra (dB)

1 (Low)

Table 2 Experimental results and the calculated S/N ratio for all machinability output

C

-5.8

Level

-6

A: Feed rate (mm/min) B: Spindle speed (rpm) C: Depth of cut (mm)

B

-6.4 -6.6

B3

-6.8

AB

-7

C1

-7.2 -7.4

C2

AB

-7.6 -7.8

A3

B1

-8

Fig. 3 Response graph from Taguchi analysis on S/N value of surface roughness, Ra

critical because it determines the surface roughness of the machined components. A value lower than those reported in the literature would diminish machining productivity while a higher value accelerates heat generation, machining forces and tool wear, hence, deteriorates the surface quality. As a result, the 500–1,000-mm/min range of feed rate was found to be appropriate for the current study. It is important to highlight that although depth of cut plays a small role during metal machining, its range was selected to be 1–2 mm, in accordance to a rough and finish machining of the given material. In the traditional full factorial experimentation, 27 trials would be needed to complete the entire experimental work of three factors at three levels. However, based on the selected parameters and their levels, current parametric study could be well performed using the L9 Taguchi OA in which nine experimental runs would be required to complete the array. The Taguchi experimental layout was arranged according to Table 2 with each trial performed in a random order as to minimise any chances of systematic error during measurement of the machinability outputs.

Number

Random no.

A

B

C

Ra (μm)

S/N Ra

TL (s)

S/N TL

Fm (N)

S/N Fm

1 2 3 4 5 6 7 8 9

1 9 2 6 7 5 8 4 3

1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3

1 2 3 3 1 2 2 3 1

2.13 2.09 1.80 2.69 2.19 2.39 2.58 2.42 2.41

−6.55 −6.40 −5.12 −8.60 −6.81 −7.57 −8.22 −7.66 −7.65

940 701 642 576 443 313 272 140 188

59.46 56.91 56.15 55.20 52.92 49.91 48.69 42.92 45.50

20.68 30.52 38.16 66.70 29.35 44.07 66.41 90.87 42.88

−26.31 −29.69 −31.63 −36.48 −29.35 −32.88 −36.44 −39.17 −32.65

Int J Adv Manuf Technol (2013) 64:247–261 Table 3 Response table and ANOVA results of surface roughness, Ra, based S/N ratio

251

Level (S/N)

SS sum of square, DF degree of freedom, MS mean squares, F Fisher test, Fratio at 2, 4 06.94

Factor A B C Response Factor A B C Error Total

1 −6.03 −7.79 −7.01 SS

2 −7.66 −6.96 −7.40 DF

6.01 1.74 0.24 1.32 9.07

2 2 – 4 8

In the Taguchi analysis, the average value of experimental response and its corresponding signal to noise ratio (S/N) of each run can be calculated to analyse the effects of the machining parameters [23, 24]. However, in this paper, S/N ratio was chosen for the Taguchi analysis because S/N ratio can represents both the average (mean) and variation (standard deviation) of the experimental results [22–24]. Hence, depending on the qualitative characteristics of the experimental response, the S/N ratio can take up either ‘the lower the better’ or ‘the higher the better’ category, given by Eqs. 1 and 2, respectively. A robust quality of a characteristic always corresponds to higher value of S/N regardless of the category. Additionally, interactions of the main factors were also considered in this study. It is important to note that, the effects of factorial interactions were marginal based on reported studies of the others on different machinability domains [8]. The lower the better: n 1X S=N ¼ 10 log x2 n i¼1

! ð1Þ

Table 4 Response table and ANOVA results of tool life, TL based on S/N ratio

Max–min 1.82 1.01 0.39 F

3.00 0.87 – 0.33

9.13 2.65 – 1

Rank 1 2 3 % Contribution 66.27 19.22 – 14.51 100.00

The higher the better: n 1X 1 S=N ¼ 10 log n i¼1 x2

! ð2Þ

3 Results and discussion 3.1 Taguchi and statistical analyses Orthogonality of Taguchi experimental design makes it possible to isolate the effects of each machining parameters at different levels using either the average values of experimental outputs or their corresponding S/N ratios. Herein, analyses of the effects of machining parameters were performed on the S/N ratios of machinability outputs using response tables, response graphs and analysis of variance (ANOVA). Response graph and table allow direct identification of the parameter effects by observing the difference between the lowest and the highest S/N ratio values of experimental outputs. The higher the difference implies a greater influence the factor has on the experimental output.

Level (S/N) Factor A B C Response

SS sum of square, DF degree of freedom, MS mean squares, F Fisher test, Fratio at 2, 4 06.94

3 −7.85 −6.78 −7.13 MS

Factor A B C Error Total

1 57.51 54.45 52.63 SS

2 52.68 50.92 51.84 DF

3 45.70 50.52 51.42 MS

Max–min 11.81 3.93 1.20 F

211.35 28.12 2.24 8.70 201.99

2 2 – 4 8

105.67 14.06 – 2.18

48.58 6.47 –

Rank 1 2 3 % Contribution 85.16 11.33 – 3.51 100.00

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Int J Adv Manuf Technol (2013) 64:247–261 A 58.00

B

AxB

C

Average of S/N

A

A1

-29

AxB

Average of S/N

B1 54.00

AB

C1

52.00 B3

A1

C1

-30

AB

C3

48.00

S/N Ratio of Fm (dB)

S/N Ratio of TL (dB)

B

-28

56.00

50.00

C

-31 -32

A3

-34

B3

AB

-35

-37

44.00

AB

-33

-36

46.00

B1

A3

C3

-38

Fig. 4 Response graph from Taguchi analysis on S/N value of tool life, TL

In addition, a higher S/N ratio value always corresponds to a more robust quality characteristic of each machinability output regardless of the category, Eqs. 1 and 2. ANOVA was performed to determine the relative influence of experimental parameters on each of the machinability outputs. This can be accomplished by calculating the variability of the computed S/N ratio for each parameter and the associated error. Table 2 displays complete experimental results and the corresponding S/N for Ra, Fm and TL. Using ‘the lower the better’ category, values of S/N ratio for Ra and Fm were calculated using Eq. 1. Whereas, for TL, Eq. 2 was used to indicate that longer tool life is more desirable. Response graph of the S/N ratio, Fig. 3, displays the effects of changing machining conditions on surface roughness, Ra. Combinations of feed rate and spindle speed have, expectedly, the strongest effect on Ra, with feed rate being the dominant factor at 67% contribution, whereas spindle speed at 19%, as shown in the ANOVA table, Table 3. On the other hand, the small difference between the minimum and maximum S/N ratios for the depth of cut, from response graph,

Table 5 Response table and ANOVA results of resultant force, Fm, based S/N ratio

SS sum of square; DF degree of freedom; MS mean squares; F Fisher Test; Fratio at 2, 4 06.94

Fig. 5 Response graph from Taguchi analysis on S/N value of resultant machining force, Fm

Fig. 3, and response table, Table 3, indicates that the effect of this parameter on Ra is trivial. It is further confirmed that the change in depth of cut has no considerable effect on Ra, Table 3, from the ANOVA output either. The lower value of Fratio for depth of cut warrant it to be pooled with random error associated with the experimentation. These results substantiate some of the previously reported findings during turning of composite materials [7, 8, 10], which emphasised the negligible effect of depth of cut on surface finish. As far as the TL is concerned, feed rate has also demonstrated highest influence on time for the tool to reach the flank wear criterion of 0.3 mm. Although increasing spindle speed is expected to accelerate tool wear and reduce TL, results from this study showed that increase in tool wear is minimal due to changes in the spindle speed. Percentage contribution of spindle speed was only about 11% as compared to feed rate at 85%, shown in the ANOVA output, Table 4. This current finding contradicts some of previous studies on drilling of CFRP composites and turning of metal matrix composites [6,

Level (S/N) Factor A B C Response Factor A B C Error Total

1 −29.21 −33.08 −29.44 SS

2 −32.91 −32.74 −33.01 DF

3 −36.09 −32.39 −35.76 MS

Max–min 6.87 0.69 6.32 F

71.03 0.72 60.33 1.054 132.41

2 2 2 4 8

35.52 – 30.17 0.53

67.42 – 57.26

Rank 1 3 2 % Contribution 53.64 – 45.56 0.80 100.00


253

25]. As reported, these studies found that cutting speed governed the time to reach the predetermined tool life. Hence, the anomaly of this result will be further discussed in the next section. Expectedly, the effect of depth of cut on TL is also marginal as indicated in ANOVA and response table, Table 4 and response graph, Fig. 4. Finally, the ANOVA results given in Table 5 show that feed rate and depth of cut contributed to the variation of the resultant machining force, Fm, at 54% and 46%, respectively. Likewise, consistent results are also displayed on the response graph, Fig. 5, showing large differences between the lower and higher values of the Fm and the S/N ratio for both feed rate and depth of cut. Apparently, a gentle increase in the S/N ratio value for Fm is noticeable as spindle speed increases, Fig. 5,

indicating its minor effect on Fm. This is also evident in the ANOVA results, Table 5, in which spindle speed is considered to be insignificant and pooled with error associated with the experimentation. Although a previous study [15] has shown that feed rate has the largest influence on the resultant machining force (in comparison to that due to cutting speed), the current work has highlighted that the depth of cut could be an equally dominant parameter influencing Fm during end milling of GFRP composites. Except for Ra, it appears that the interactions effect of feed rate and spindle speed on Fm and TL output are little as compared to that of the main parameters, Figs. 4 and 5. Hence, it can be concluded that the machining parameters are to be independent of one another particularly for Fm and TL. This will be further discussed in the subsequent section of this paper.

(a) (a)

2.40 2.30 2.20 2.10 2.00 500

750 Feed rate (mm/min), f

1000

Surface roughness, Ra (µm)


2.50

2.20 2.10 2.00 1.90 1.80 1.70 1.60 1.50 3000

4000 Spindle Speed (RPM), s

5000

3000

4000 Spindle speed (RPM), s

5000

3000

4000 Spindle speed (RPM), s

5000

(b) Surface roughness, Ra (µm)

2.80 2.60 2.40 2.20 2.00 1.80 500


1000

(c)

3.00 2.80 2.60 2.40 2.20 2.00

(c) Surface roughness, Ra (µm)

2.75 Surface roughness, Ra (µm)

Surface roughness, Ra (mm)

(b)

3.00

2.50 2.25 2.00 1.75 1.50 500


1000

Fig. 6 Effect of feed rate on surface roughness, Ra at different spindle speed: (a) s = 3000 RPM, (b) s = 4000 RPM, (c) s = 5000 RPM

2.70 2.60 2.50 2.40 2.30 2.20

Fig. 7 Effect of spindle speed on surface roughness, Ra, at different feed rate: a f0500 mm/min, b f0750 mm/min, c f01,000 mm/min

254


Fig. 8 Cutting mechanism of GFRP [30]

Table and tool feed direction

Tool

Fibre and matrix interfacial fracture

Fibre fracture

3.2 Interpretations and discussions of the parametric/ factorial effects For final applications of GFRP composites, the perceived machining quality determined by surface roughness value, Ra, delamination damage, severity of the fibre pullout and matrix failure, need to be critically quantified. Previously presented statistical analyses of factorial effects suggested that different combinations of feed rate and spindle speed have significant effects on Ra. Figures 6 and 7 exhibited these different effects, in which it is apparent that Ra values

deteriorated with the increase of feed rate. Whereas marginal improvement of Ra is evident with respect to increasing spindle speed as depicted Fig. 7. Nevertheless, the effect of changing of feed rate on Ra has been more dominant than that of the spindle speed and depth of cut as indicated in the previous statistical analyses. This is expected, since feed rate influences mechanisms of chip formation, which will largely determine the value of Ra. Furthermore, deterioration in surface roughness at higher feed rate could be attributed to the increase strain rate on the composite material which promotes excessive fractures on glass fibres and epoxy

Fig. 9 Scanning electron micrographs of the machined surface

(a)

(b)

(c)

(d)


255

Fig. 10 Scanning electron micrographs showing (a–b) abrasive wear on the flank face of the cutting tool as well as (d) rounding of cutting edges

(a)

(b)

(c)

(d)

matrix. Nonetheless, it is well understood that from the theoretical surface roughness (in turning with certain tool pffiffiffiffiffiffi radius), Ra is given by [26]: Ra ¼ f 2 =18 3R, where f is the feed rate, R is the tool nose radius. It should be noted that this equation only provides a simple relationship of Ra, taken into consideration of only feed rate, tool geometry and homogenous material during turning operation. As a matter of fact, the values of Ra during end milling can also be affected by various factors, which include the extent of tool wear, tool geometry, such as tool concavity and relief angles [27], as well as the vibrations or chatter [26]. In the case of FRP composites, Ra value is also highly sensitive to the direction of measurement with respect to fibre orientation.

An increased spindle speed preserves the machined surface due to a reduction in material deformation at the tool-chip interface during the cutting process, hence results in an improved surface roughness. However, the spindle speed should be controlled at an optimum level so as to alleviate the wear of the cutting tool when machining these highly abrasive GFRP composites. Although results of this surface roughness study are comparable to those reported in the literature, as mentioned earlier, it is also worth to highlight that as previously found with Kevlar machining, Ra values may not always clearly reflect the surface qualities of the machined composites [3, 4]. Particular care has to be taken when measuring surface roughness of fibrous composite materials, since there might be fracture of fibres, fibre pull out or fibres protrusion,

Fig. 11 Contact and rubbing actions between the fibres and each cutting flutes as the tool rotates and cuts across the fibres (at centre position of the cutting flutes)

V Fibre reinforcement

(b) Matrix phase

x

(a)

(b) [17]

256


Fig. 12 Example of EDS analysis on the rake face of K20 end mill tool

delamination damage and matrix failure; all of which can lead to large variations in Ra measurements [28, 29] as depicted in Figs. 6 and 7. The aforementioned matrix and fibre failures could be closely related to the mechanism during cutting. Fibre reinforcements are subjected to buckling and bending failures as the cutting progresses along the fibre direction (angle of 0o), Fig. 8 [30]. Furthermore, surface below the tool edge is being compressed which results in failure of fibres and matrix due to interfacial fracture. Thus, in the scanning electron microscope (SEM) images of machined surfaces, Fig. 9a–b, it is apparent that sharp and brittle fractures of the fibres indicate these failure modes. On the other hand, Fig. 9c–d, displays evidence of fibres protrusion and pullout from machined surface due to fibre debonding or matrix failure. This is likely due to the reduction in tool sharpness, in which, the tool no longer cuts the fibres cleanly. The presence of the fibres on the machined surface will affect the movement of stylus tip of the surface roughness measurer to cause large variations in the Ra readings as shown earlier.

Fig. 13 Orthogonal cutting chip formation [31] and end milling chip formation diagrams

With regard to tool wear and tool life, TL, it appears that the governing factor for TL was feed rate, which is somewhat in contrary with other results [25] and in Taylor's model [31]. It is worthwhile to note that the 3.51% error level associated with the experimentation, Table 4, was within acceptable limit, suggesting that measurement of tool wear for tool life had been accurately performed. In view of this, it is believed that the effects of cutting edge rounding and chipping observed in the lower machining conditions range (level 1 in any of the machining conditions) hinder accurate measurement of tool flank wear. The rounding of cutting edges arises due to abrasive, bending and spring-back actions of the E-glass fibres on cutting tool edges at the tool–fibre interface [11]. It is worth arguing that the experimental range tested in this experiment may not be sufficiently discriminative to show any dominant effect of spindle speed on TL. Furthermore, it is anticipated the change in feed rate results in a higher heat energy being absorbed by the tool as compared to that of spindle speed to accelerate the tool wear. However, this warrants further investigations incorporating a wider range of machining conditions in the forthcoming study of the authors. It is also evident that

f

b f



3.00 2.70 2.40 2.10 Experimental

1.80

Predicted MRA 1.50

Experiment Number

Fig. 14 Variation of experimental and calculated values from MRA for surface roughness, Ra

As far as machining force, Fm, is concerned, it appears that the depth of cut is an equally dominant parameter that influence Fm during end milling of GFRP composites apart from the feed rate. This may be attributed to the fact that feed rate determine the area of undeformed chip thickness [31]. On the other hand, the effect of depth of cut is mainly due to the increase in the cross-sectional area as well as the number of fibre layers to be cut. This can be visualised using orthogonal chip formation diagrams, Fig. 13, where the area of undeformed chip thickness is Ac and f can be interpreted as feed rate while width of cut, b is the depth of cut. Hence, any changes in these parameters significantly influence the amount of force experienced by the cutting tool during machining. Nevertheless, when machining of brittle material such as GFRP composites, fracture of glass and epoxy matrix reduces tool/chip contact on the tool flank and rake faces which tends to alleviate friction between tool and the workpiece material. Hence, this results in lower machining forces as compared to homogenous and ductile material such as metals. The minor effect of spindle speed on the Fm, in terms of decreasing machining force with higher spindle speed, contradicts the common perception. This is could be justified by the fact that increase in spindle generate friction, which elevates the cutting zone temperatures. As 1200

Experimental Predicted_MRA

1000 Tool life, TL (sec)

the interactions effect between feed rate and spindle speed is little in comparison to that of the main effect. Nonetheless, SEM images of cutting tool edge, Fig. 10, indicate the abrasive wear on the tool flank face is to be the dominant tool wear mechanism. Two body abrasions or rubbing actions of highly abrasive fibres at the contact point of the tool flank face results in the scratch marks as depicted in the SEM micrographs. Figure 11 illustrates the increase in contact and rubbing actions of fibre reinforcement on each cutting flutes as the chip thickness changes from minimum, as the cutting tool plunges into the material, to maximum, as the cutting flute is at the centre of the cutting. At this position, the cutting flute was fracturing orthogonally across the fibre, as illustrated in Fig. 11b. This leads to a direct rubbing of the fibres on the tool flank face to rapidly wear the cutting tool. Although chip thickness reduces to small as the cutting flute exiting the material, each cutting flute maintains the same amount of contact (as in the tool entrance) with the fractured fibres. Furthermore, impact from fibre reinforcements generates dynamic stresses on the tungsten carbide (WC) hard grains of the cutting tool which results in crack initiation and propagation of the grains [6]. This process is demonstrated by the smooth scratches on the surface of the cutting tool as shown in Fig. 10a. In addition, cyclic stresses of intermittent cutting action during end milling as the tool encounters different phases of GFRP materials resulted in microchippings on some of the cutting flutes, Fig. 10. The combination of shearing and bending rupture with little plastic deformation is the main cutting mechanism of FRP composites; hence, the absence of other forms of wear exists. In fact, crater wear and formation of built up edge (BUE) on the tool rake face as observed in metal cutting was unlikely to occur due to the chip formation mechanisms. During end milling, chips are mostly discontinuous type and in particular, for GFRP composites, the chips were mostly in the form of dusts as a result of bending, buckling and brittle fractures of glass fibres and epoxy matrix. This could be attributed to the increase in strain on the composite material with higher cutting speed which accelerates brittle fracture or behaviour of the epoxy matrix and glass fibres. Hence, ploughing on the tool rake face by those chips to create crater wear did not take place. Furthermore, small temperature increase in the cutting zone as compared to metal cutting also explained the absence of BUE and crater wear on the end mill tool. Sediments of charred epoxy matrix can also be seen on the flank and rake face of the cutting flutes, Fig. 10c, due to the increase of temperature in the cutting zone, resulting from the low thermal conductivities of both epoxy resin and the glass fibres. Energy dispersive X-ray spectroscopy (EDS) analysis performed at one of the positions on the rake face confirmed the adhesion of carbon (due to charred epoxy), Fig. 12.

257

800 600 400 200

0

Experiment Number

Fig. 15 Variation of experimental and calculated values from MRA for tool life, TL

258


Resultant force, Fm (N)

100

80

TL and Fm) is considered in terms of a power series equation given by:

60

Y ¼ Csa f b d g

40 Experimental

20

Predicted_MRA

0

ð3Þ

where Y is the machinability output, C, α, β, γ, are coefficients of the empirical model, s is the spindle speed in revolutions per minute, f is the feed rate in millimetres per minute, d is the depth of cut in millimetres. The respective equations obtained for Ra, TL and Fm are as follows: Ra ¼ 100:293 s0:232 f 0:313 d 0:025 ;

Experiment Number

R2 ¼ 0:718

ð4Þ

Fig. 16 Variation of experimental and calculated values from MRA for resultant force, Fm

a result of low thermal conductivities of glass fibres and epoxy matrix, this would tend to soften the polymer matrix and hence, requiring less force to shear the material. Similar results have been reported by Wang et al. during orthogonal cutting of graphite epoxy composites [32]. Likewise, Lee reported a reduction in machining forces with increase of cutting speed during turning operation of GFRP composites for the entire cutting tool materials tested [33]. Similar to that of TL, the interactions effect between feed rate and spindle speed is negligible in comparison to that of the main effect on the resultant machining forces, Fm, Fig. 5.

TL ¼ 1011:398 s0:911 f 1:922 d 0:201 ;

Fm ¼ 101:214 s0:156 f 1:136 d 1:048 ;

ð5Þ

R2 ¼ 0:926

R2 ¼ 0:996

ð6Þ

Correlations between machining parameters and the machinability outputs have been modelled using MRA. The parametric dependency of each machinability output (Ra,

The coefficients of determination, R2, are higher for Fm and TL models compared to that for Ra. Thus, Eqs. 5 and 6 can be effectively used to obtain reliable estimates of Fm and TL during end milling GFRP composites within the range of experimental parameters. As discussed earlier, other less controllable factors, could lead to inconsistent measurement of surface roughness. This explains the reason for lower value of R2 in Eq. 4, which indicates that the equation can only be used with sufficient care while predicting the surface roughness. Nevertheless, judging from the coefficients values of each parameter of the derived semi-empirical models, it is further confirmed that feed rate has the most significant effect on each of the machinability outputs studied here.

Table 6 Conditions and results of validation experiments

Condition

f (mm/min)

s (rpm)

d (mm)

1 2 3 4

640 500 1,000 1,440 Condition 1

4,000 5,000 5,000 6,000 Condition 2

2 3 2 2 Condition 3

Condition 4

Pre.

863

691

159

67

Exp. % Error Pre.

769 10.91 2.21

605 9.63 1.91

148 7.24 2.41

73 9.08 2.59

Exp. % Error Pre.

3.19 44.43 49.82

2.34 19.20 59.23

2.59 6.24 109.85

3.10 19.79 125.13

Exp. % Error

53.79 7.97

52.96 10.60

120.24 8.64

136.22 8.87

3.3 Correlations of experimental parameters with machinability output

Machinability output Tool life, TL (s)

Surface roughness, Ra (μm)

Resultant force, Fm (N) Pre. predicted, Exp. experimental


3.4 Validation tests A comparison of experimental data with the calculated results from MRA equations within the reported range of experimental conditions shows average variations of 4.9%, 13.0% and 2.7% for Ra, TL and Fm, respectively. The results of experimental variations for all machinability output are presented in Figs. 14, 15 and 16. A very close agreement between the predicted values and the experimental results can be observed indicating the accuracy of the semiempirical models developed. Validation experiments were additionally performed under randomly selected machining conditions (some of which were outside the range of L9 array) to further evaluate the accuracies of the developed semi-empirical models. Experimental conditions for confirmation tests and the results are summarised in Table 6, showing the absolute percentage errors of within 7–12% for Fm and TL. As expected, variations in percentage errors were obtained for Ra due to the lower correlation in Eq. 6. SEM photos of validation experiments, Fig. 17(a) confirm this result, showing evidence of fracture and protruding fibres as well as debris of epoxies that give large variations in Ra readings, hence make the prediction less accurate or

259

reliable particularly for validation test 4. However, when applying the derived Ra equation to predict the resulting Ra under the extreme conditions of this study (all factors at level 3, Table 6), the predictive capability showed acceptable error variance. This is consistent with the SEM micrograph, Fig. 17(b), in which it is apparent that interfacial fracture and matrix–fibre debonding were minimal which resulted in a reliable reading of Ra values, Table 6. Hence, it is demonstrated that the developed parametric models show good predicting capability of the machinability output within the range of parameters studied here.

(a) 2.8

2.4

Ra (mm) 2.0 3000

s (RPM) 4000 5000

1000

750

500

f (mm/min)

(a) (b) 800 600

TL (sec) 400 200 3000

s (RPM) 4000 5000

(b)

1000

750

500

f (mm/min)

(c)

80 60

Fm (N) 40 20 1.0

d (mm) 1.5

Fig. 17 SEM micrographs of validation experiments

1000 2.0

750 500

f (mm/min)

Fig. 18 Surface plot of machinability output against the main experimental parameters

260


3.5 Effect of main parameters using surface plots In order to further understand the effects of the aforesaid main parameters, surface plots of machinability outputs against the parameters have been created in Fig. 18a–c. As shown in Fig. 18a, the combination of high spindle speed and low feed rate results in better surface finish which is in agreement with the previous findings of others. However, when both feed rate and spindle speed increase, a higher value of Ra is observed. Hence, from the machining economy point of view, a low but acceptable feed rate needs to be used with a high spindle speed for meeting the critical surface finish requirement but without compromising the production rate as well as the tool life. Meanwhile, both spindle speed and feed rate have shown significant influence on the tool life, TL. As apparent, the dominant effects of feed rate on TL are more pronounced at higher levels. On the other hand, Fig. 18c shows that the increases in both the feed rate and depth of cut have significantly increased the resultant force, Fm. The combination of high feed rate and depth of cut shows a steep increase in the value of Fm. Analysis of these surface plots reconfirmed the earlier statement, that feed rate is the most dominant parameter affecting all the machinability characteristics during end milling of GFRP composites.

4 Conclusions This paper has presented and discussed the end milling machinability results for GFRP composites using Taguchi's design of experiment method. The following conclusions can be drawn based on the results of the present work: –

– –

– –

Feed rate has the most dominant role in influencing the surface roughness, Ra, followed by spindle speed with each factor contributing 67% and 19%, respectively. The effect of depth of cut has been found to be negligible. The dominant effect of feed rate on Ra may be attributed to the different mechanisms of chip formation at various feed rates. It is important to note that Ra value alone may not be sufficient to describe the surface finish of FRP composites due to fibre protrusion. SEM images were used to verify the surface integrity and morphology of the machined laminates. The tool life performance of the K20 end mill cutter was mainly influenced by the feed rate (85% contribution) and spindle speed (11% contribution). It is believed that the effect of cutting edge rounding and chipping observed in the lower machining conditions range may hinder accurate measurement of tool flank wear and eventually the tool life.

–

– –

–

–

Additionally, experimental range tested in this experiment may not be sufficiently discriminative to show any dominant effect of spindle speed on the tool life as to that of feed rate. Apart from that, the heat energy being absorbed by the tool due to change in feed rate has been more dominant as to that due to spindle speed to accelerate the tool wear. As apparent, the predominant tool wear mechanisms were mechanical abrasions on the flank face of the cutting tool. Nonetheless, similar to that of Ra, the influence of depth of cut on tool life or tool wear was insignificant. The resultant machining force, Fm, was significantly affected by feed rate and depth of cut at 54% and 45% contributions respectively. This is because both parameters determine the cross-sectional area of the undeformed chip. Spindle speed has been found to show marginal effect on the Fm. Comparatively, the results obtained from MRA and Taguchi statistical analyses drew similar conclusions in term of relative influence of each factor on different machinability outputs. Validation tests have demonstrated that the developed semi-empirical models from MRA are generally good in predicting the TL and Fm (within 8–12% error band). However, low correlation of Eq. 4 indicates that the derived equation can only be used with proper care to get a reliable estimate of the Ra value.

Acknowledgements The authors acknowledged the financial supports from the Ministry of Higher Education Malaysia (MOHE) and Universiti Malaysia Perlis (UniMAP) for the SLAI Doctoral Scholarship to Mr. Azwan Azmi. Support from the University of Auckland PReSS account grant is also appreciated. We would also like to thank the anonymous reviewers for their thorough comments and valuable suggestions on this manuscript.

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