Int J Adv Manuf Technol (2006) 31: 10–17 DOI 10.1007/s00170-005-0175-x
ORIGINA L ARTI CLE
Eyup Bagci . Birhan Işık
Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN
Received: 26 January 2005 / Accepted: 18 May 2005 / Published online: 13 January 2006 # Springer-Verlag London Limited 2006
Abstract Fibre reinforced plastics (FRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interactions between the matrix and the reinforcement during machining. Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in contact with other elements or materials during their useful life. Therefore, their study and characterisation is extremely important and, above all, those cases subjected to adverse environmental conditions and in contact with other elements or materials. Thus, measuring and characterising surface properties represent one of the most important aspects in manufacturing processes. In this paper, orthogonal cutting tests were carried out on unidirectional glassfibre reinforced plastics (GFRP), using cermet tools. During the tests, the depth of cut (a), feedrate (f), cutting speed (Vc) were varied, whereas the cutting direction was held parallel to the fibre orientation. Turning experiments were designed based on statistical three level full factorial experimental design technique. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness on the turned part surface. In the development of predictive models, cutting parameters of cutting speed, depth of cut and feed rate were considered as model variables. The required data for predictive models are obtained by conducting a series of turning test and measuring the surface roughness data. Good agreement is observed between the predictive models results and the experimental
E. Bagci (*) TUBİTAK-UME, National Metrology Institute, P.K. 54, 41470, Gebze-Kocaeli, Turkey e-mail:
[email protected] Tel.: +90-262-6538497 Fax: +90-262-6538490 B. Işık Department of Mechanical Education, Technical Education Faculty, Marmara University, İstanbul, Turkey
measurements. The ANN and RSM models for GFRPs turned part surfaces are compared with each other for accuracy and computational cost.
1 Introduction In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Fibre reinforced plastics (FRP) contain two phases of materials with drastically distinguished mechanical and thermal properties, which brings in complicated interections between the matrix and the reinforcement during machining. The material anistrophy resulting from fibre reinforcement heavily influences the chip formation and other machinability indices during machining [1]. FRP have become a highly desired medium for advanced structural applications due to their light weight, high modulus, and specific strength. A typical FRP component is molded to near-net shape and subsequently finish machined to meet geometric tolerance and surface finish requirements. Achieving an acceptable surface quality with conventional methods of machining has been found extremely difficult due to the anisotropic and heterogeneous nature of these materials. Excessive tool wear is prevalent and frequently induces fibre pullout and surface ply delamination in the component part [2–3]. This limitation has provided both academic and industrial motivation for research on the aplication of traditional methods of machining to reinforced polymers. Koplev [4–5] and Sakuma and Seto [6] were the first to distinguish the unique material removal characteristic in orthogonal cutting of FRPs. Koplev et al. [5] have investigated the process of machining of carbon fibre reinforced plastics (CFRP). They measured cutting force both parallel and perpendicular to the fibre orientation using quick stop experiments. They also studied the effect of rake angle during machining of CFRP`s. Additional studies have identified the influence of process parameters to the cutting forces and surface quality [7–9]. A part from fibre orientation, tool geometry has consistently been noted as a crit-
11 Fig. 1 Orthogonal cutting of unidirectional GFRP
ical cutting parameter which influences the cutting force, surface quality and tool wear [10]. Takeyama and Lijma [11] have investigated the chip formation process. In their classical study, they have described that the chip formation process for glass fibre reinforced plastics (GFRP) is strongly governed by the fibre orientation with reference to the cutting direction. Wang et al. [12] have studied, both analytically and experimentally, the orthogonal cutting mechanism of unidirectional graphite/epoxy composites with a diamond tool. They have also developed a regression model to predict cutting force in terms of rake angle; clearance angle, depth of cut and cutting speed. In this study, an artificial neural network (ANN) and RS model based on experimental measurement data are developed to estimate surface roughness in orthogonal cutting of GFRP’s. The ANN and RS model include cutting speed, feed rate and depth of cut as turning parameters. To find Ra value, three different points were measured along fibres direction. The average value of the three values was recorded. An artificial neural network and response surface model are developed to predict surface roughness on the turned part surface. In the development of predictive models, cutting parameters consisting of cutting speed, depth of cut and feed rate were considered as model variables. Good agreement is observed between the predicted optimum and the experimental measurements. The ANN and RSM models for GFRP’s part surfaces are compared with each other for accuracy and computational cost.
2.1 Cutting tools and work-piece materials GFRP rods consist of unidirectional fibres that are pulled through a resin bath into the shape of the rod. GFRP is a cheaper option than Carbon or Kevlar, so GFRP rods were used in this work. Advantages of GFRP include [13]: – – – –
more compatible with resin and timber, due to more compatible material properties; high resistance to corrosion, useful in a humid or acid environment; improved performance due to better resin bonding; more lightweight connection, hence easier handling.
The material was produced by pultrusion method with polyester and E-glass. It has 82.27% glass contents. Fig. 2 shows a SEM photo of the GFRP material while the physical properties are listed Table 1. The turning operation was carried out using TAEGU TEC TCMT 16T304 MT T3000 inserts and a SECO STGCL 2020K16 tool holder Table 2 shows the geometry and mechanical properties material of the tool.
2 Experimental setup and cutting conditions The cutting experiments were conducted turning in dry cutting conditions on a JOHNFORD TC-35 lathe machine equipped with Fanuc 18T CNC control and programmable tailstock and a maximum spindle speed of 3,500 rpm and a 15 kW drive motor. Fig. 1 shows the CNC lathe machine where the turning is operated.
Fig. 2 A SEM photo of the GFRP
12 Table 1 Physical properties of unidirectional GFRP 3
Specific weight (g/cm ) Tensile strength ( N/mm2) Young’s modulus ( N/mm2) Thermal coefficient of expansion (m/mK) Thermal conductivity (°C) Glass fibre Matrix material
2.5 1800 7400 5 0.8 E – glass Polyester resin
2.2 Cutting conditions Design of experiments is a powerful analysis tool for modelling and analysing the effect of process variables over some specific variable which is an unknown function of these process variables [14]. The experimental design method is an effective approach to optimise the various machining parameters. The selection of such points in the design space is commonly called design of experiments (DOE) or experimental design. The choice of the experimental design can have a large influence on the accuracy and the construction cost of the approximations. Randomly chosen design points may cause an inaccurate surface to be constructed or even prevent the ability to construct a surface at all. Several experimental design techniques have been used to aid in the selection of appropriate design points. In a factorial design, a variable range is divided into levels between the lowest and the highest values [15]. A three-level full factorial design creates 3n training data, where n is the number of variables. In this study, three independent variables, such as depth of cut (a), cutting speed (Vc) and feed rate (f) had total of 33 = 27 experimental runs. Ranges for process parameters are shown in Table 3.
3 Surface roughness measurement and result 3.1 Arithmetic mean deviation of the roughness profile (Ra) Ra is the arithmetic mean of the absolute values of the profile departures from the centre line within the evaluation Table 2 Mechanical and technical spesifications of the cutting tool Code
TCMT 16T304 MT CT3000
Clearance angle Rake angle Nose radius Grade Density Hardness Transverse rupture strength Young’s modulus Thermal conductivity Compressive strength Thermal expansion coefficient
7° 6° 0.47 mm Cermet 6.7 gr/cm3 93.0 HRA 160 kg/mm2 103 kg/mm2 0.07 cal/cm.sec.K 460 kg/mm2 10–6/°C
Table 3 Levels of the variables used in this work Factors
Level 1
Level 2
75 0.2 0.6
Vc f a
Level 3
100 0.3 0.9
125 0.4 1.2
length ln and is represented by a formula shown in Eq. 1, where the roughness profile is given as Y=f(x) with the x axis for the centre line and y axis in the direction of the vertical magnification: 1 Ra ¼ L
ZL jY ð xÞjdx
(1)
0
where Ra = the arithmetic average deviation from the mean line Y= the ordinate of the profile curve
3.2 Measurement and result For the surface roughness measurement of the machined surface, a portable Mitutoyo Surftest 211 contact profilTable 4 Experimental results obtained from turned surfaces and cutting parameters Set Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Vc [m.min–1] 125 100 75 125 100 75 125 100 75 125 100 75 125 100 75 125 100 75 125 100 75 125 100 75 125 100 75
a [mm] 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2
f [mm.rev–1] 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4
Ra [μm] 1.53 1.66 1.5 1.65 1.52 1.68 1.52 1.51 1.65 1.65 1.56 1.61 1.66 1.54 1.5 1.5 1.53 1.52 1.62 1.6 1.6 1.55 1.54 1.65 1.53 1.66 1.5
13 Table 5 Comparison of the NN predictions with check data set Test No
1 2 3
Fig. 3 A typical neural network architecture
ometer was used. This contact profilometer utilises a diamond stylus with a radius of 2 μm. Measurements in these areas are conducted at least three times and the mean value is recorded as the Ra value. Cutting inserts are also checked and cleaned after each turning process. Experimental results obtained from the turned surfaces are given in Table 4.
4 Artificial neural network model for prediction of surface roughness Artificial neural networks are capable computational models for a wide diversity of problems. For manufacturing processes where no satisfactory analytic model exist, or a low order empirical polynomial model is inappropriate, neural networks offer a good alternative approach [16]. Until today, many different neural network models have been developed. They include perceptrons, Kohonen, Hassoun, Yuille, Hebbian, Oja, Hopfields, backpropagation and Kolmogorov networks [17], to mention a few of the better know network models. Among the various neural networks models, back propagation is the best generalpurpose model and probably the best at generalisation [16].
Fig. 4 Neural network architecture designed
ANN results (Check data) 1.483163 1.438324 1.556823
Experimental results (Check data) 1.52 1.54 1.63
Error −0.03684 −0.10168 −0.07318
The typical neural networks architecture can be seen in Fig. 3. Layers, the input layer, the hidden layer and the output layer, include several processing units known as neurons. The input layer is used to present the data in the network model and the output to create the ANN’s response. There are several transfer functions such as threshold function, piecewise-linear function, sigmoid/hyperbolic function and logarithmic used in neural network models [17]. Tangent hyperbolic activation function was selected in this work. For the prediction of surface roughness, in this study a multilayer perceptron consisting of an input, two hidden layers and an output layer was used as shown in Fig. 4. The optimal ANN architecture was designed by means of Matlab Neural Network Toolbox [18]. Neurons in the input layer correspond to depth of cut (DoC), cutting speed (Vc) and feed rate (f). The output layer corresponds to surface roughness. In this model, the inputs are fully connected to the hidden layer and hidden layer neurons are fully connected to the outputs. Input and output layers have four and one neuron, respectively. ANN models have two hidden layers and first and second hidden layers have nine and ten neuron, respectively as shown in Fig. 4. Determining the number of hidden layers and the number of the neurons in each hidden layer is a considerable task. The number of hidden layers is usually determined first. Feng [16] argued that a two hidden layer should perform better than a one hidden layer network. The number of hidden layers neurons are decided by trial and error method on the basis of the improvement in the error with increasing number of hidden nodes [19]. They are connected with each other by variable weights to be determined.
14 Table 6 Comparison of NN predictions with training data set Test No
ANN results (Training data)
Experimental results (Training data)
Error
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1.587697 1.619786 1.498232 1.616958 1.600126 1.675674 1.500512 1.51417 1.650057 1.640993 1.601588 1.603063 1.573248 1.530135 1.534782 1.515575 1.528443 1.525539 1.623646 1.608371 1.592289 1.52889 1.58524 1.594579
1.53 1.66 1.5 1.65 1.52 1.68 1.52 1.51 1.65 1.65 1.56 1.61 1.66 1.54 1.5 1.5 1.53 1.52 1.62 1.6 1.6 1.55 1.54 1.65
0.057697 −0.04021 −0.00177 −0.03304 0.080126 −0.00433 −0.01949 0.00417 5.7E-05 −0.00901 0.041588 −0.00694 −0.08675 −0.00987 0.034782 0.015575 −0.00156 0.005539 0.003646 0.008371 −0.00771 −0.02111 0.04524 −0.055421
In the neural network model, the output of neurons on the input layer reach the jth neuron on the next layer and become its input as stated in Eq. 2. net j ¼
N X
wij xi
(2)
j¼0
where N is the number of neurons of the inputs to the j-th neuron in the hidden layer and netj is the total or net input. xi is the input from the i-th neuron in the preceding layer and wij is the weight of between the i-th neuron on the input layer and the j-th neuron on the next layer. A tangent
Fig. 6 Comparison of NN predictions with training data set
hyperbolic activation function (f) that transforms the input value of the hidden layer to produce its output (out j). 1 enetj outj ¼ f netj ¼ 1 þ enetj
(3)
To compute connection weights, a set of desired network output values often referred to as training data set is needed. The data set is generated using full factorial experimental design as shown Table 4. For training the network, the TRAINGD function of MATLAB was used [18]. This function works on the back propagation algorithm. In calculation of weight variables, often referred to as network training, the weights are given quasi-random, intelligently chosen initial values. They are then iteratively updated until convergence to the certain values using the gradient descent method. Traingd is a network training function that updates weight and bias values according to gradient descent. Gradient descent method updates weights so as to minimise the mean square error (MSE) between the training data set and network prediction as given in Eqs. 4 and 5 below: wij new ¼ wold ij ij þ wij
wij ¼
(4)
@E out j @wij
Fig. 7 Steps taken in constructing response surface approximations [21]
(5)
Model Assumptions for Objective and Constraints
Selection of Analysis Points Carrying out Analyses at Selected Points Model Fittings for Objective and Constraints
Fig. 5 Comparison of NN predictions with check data set
15 Fig. 8 Comparison of RSM predictions with experimental data set
where E is the MSE and outj is the j-th neuron output. η is the learning rate parameter controlling the stability and rate of convergence of the network model. The learning rate η, which is a constant between 0 and 1, is chosen to be 0.0001. The training process takes about 3 hours of CPU time on IBM-P4 processor PC for about 1 500 000 training iterations. MSE error for training data is computed as 0.00123.
Table 7 Comparison of RSM predictions with training data set Test No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
RSM results (Training data) 1.607282 1.555377 1.567758 1.618552 1.580933 1.607599 1.525060 1.560774 1.601726 1.613532 1.564960 1.580675 1.594802 1.560516 1.590516 1.477440 1.510357 1.554643 1.637282 1.592044 1.611091 1.588552 1.557599 1.590933
Experimental results (Training data) 1.53 1.66 1.5 1.65 1.52 1.68 1.52 1.51 1.65 1.65 1.56 1.61 1.66 1.54 1.5 1.5 1.53 1.52 1.62 1.6 1.6 1.55 1.54 1.65
Error
0.077282 −0.10462 0.067758 −0.03145 0.060933 −0.0724 0.00506 0.050774 −0.04827 −0.03647 0.00496 −0.02933 −0.0652 0.020516 0.090516 −0.02256 −0.01964 0.034643 0.017282 −0.00796 0.011091 0.038552 0.017599 −0.05907
4.1 ANN approach: results and comparison Training of the neural network model was performed using 24 experimental data out of 27 data as explained in Section 2.2. The trained network model was tested using three experimental data points (check data), which were not used in the training process. The results predicted from the ANN model are compared with those obtained by experimental test in Table 5 for 3 check sets and Table 6 for 24 training sets. It is seen from Table 5 and 6 that ANN prediction is in good agreement with the experimental results. Figs. 5 and 6 compare the neural network surface roughness prediction with experimental test results for training and check data sets. It is found that the developed ANN model has good interpolation capability and can be used as an efficient predictive tool for surface roughness. Increasing the number of nodes increases the computational cost and decreases the error [19].
5 Response surface approach for prediction of Ra: results and discussion RSM is the combination of mathematical and statistical techniques used in an empirical study of relationships and optimisation, where several independent variables influence a dependent variable or response. RSM, the relationships between the responses and the variables investigated is commonly approximated by polynomial functions, Table 8 Comparison of RSM predictions with check data set Test No
1 2 3
RSM results (Check data) 1.494107 1.530357 1.584107
Experimental results (Check data) 1.52 1.54 1.63
Error −0.02589 −0.00964 −0.04589
16
Fig. 9 Comparison of experimental measurements with predicted check set results from RSM
whilst the model parameters are obtained by a small number of experiments utilising a design of experiment [21]. The experimental measurements for surface roughness are replaced by a simpler and more efficient statistical model using response surface models designed in Matlab Toolbox [20]. RSM is a model building technique based on statistical design of experiments and least square error fitting. RSM creates polynomial models for the available data set as must rated in Eq. 6. f ¼ a0 þ
n X i¼1
n X n X ai xi þ aij xi xj þ::::::
(6)
Fig. 11 Comparison of experimental measurements with predicted check results from RSM and ANN
can also be generated in terms of inverse of parameters. That is, xi can be replaced as 1xi (i.e. inversely) in RS model if desired [20]. In creating the RS models, 27 data exploiting experimental measurements obtained from the effective cutting conditions (f), (V) and (a) versus surface roughness are compared with those predicted in the RS method as shown in Figs. 7 and 8. To check the accuracy of the RS model created, the 3 data sets, which are not involved in training sets, are employed in Tables 7 and 8.
i¼1 j¼1
where a0, ai and aij are tuning parameters and n is the number of model parameters (i.e. process parameters). The polynomial models generated by RSM are often referred to as response surface (RS) models in the literature. Response surface methodology (RSM) can be defined as a methodology for constructing smooth approximations of structural responses based on analysis results calculated at various design points in the region of interest. The methodology was originally developed for the model fitting of physical experiments by Box and Draper [21] and later adopted in machining problems. Steps taken in the construction of response surface (RS) approximations for objective and constraints using RSM are illustrated in Fig. 7. RS program has the capability of creating RS polynomials up to 10th order if sufficient data exist. All cross terms in the models can be taken into account. RS models Fig. 10 Comparison of experimental measurements with predicted training results from RSM and ANN
6 Comparison of ANN and RS models for surface roughness Construction of an artificial neural network model needs a large number of iterative computations while on the contrary it is only a single step computation for a response surface model. Depending on the number of variables and parameters and the nonlinearity of the problem, an ANN model may require a high computational cost to generate. In the surface roughness calculation, the ANN model took about 3 hours of CPU time to create whereas the RS model took just a couple of seconds. Models were also compared to predict surface roughness accurately within a wide of range cutting conditions based on DOE (see Figs. 9, 10, and 11). The maximum test errors for ANN and RS model are about 6.36% and 6.30% respectively.
17
7 Concluding remarks In this study, the experimental observations were incorporated into the ANN model for orthogonal cutting of unidirectional GFRP material. A feed forward neural network model and RS model were developed to predict surface roughness after the turning process. Models were also compared to predict surface roughness accurately within a wide range of cutting parameters based on DOE. Good agreement was shown between the predictive models results and the experimental measurements. The ANN model involves more computationally time than a response surface model. It was found that the maximum test errors were 6.30% and 6.36% by comparing roughness (Ra) values predicted from ANN model with those predicted RSM. Acknowledgement This study was supported by the Commission of Scientific Researching Projects of Marmara University. Project Number: BSE-018 / 020103.
References 1. Komanduri R, Zhang B, Vissa CM (1991) Machining of fiber reinforced composite. Proc Conference on Processing and Manufacturing of Composites, ASME pp 1–36 2. König W, Wulf C, Graf P, Willerscheid H (1985) Machining of fiber reinforced plastics. Ann CIRP 34(2):537–547 3. Colligan K, Ramulu M (1992) Edge trimming on composite surface plies. Manuf Rev 4:274–283 4. Koplev A (1980) Cutting of CFRP with single edge tools. Proc 3rd International Conference on Composite Materials, Paris 5. Koplev A, Lystrup A, Vorm T (1983) The cutting forces in machining CFRP. Composites 14(4):371–376 6. Sakuma K, Seto M (1983) Tool wear in cutting glass-fiber reinforced plastics. Bulletin t JSME 26(218):1420–1427 7. Komanduri R (1993) Machining fiber-reinforced composites. Mech Eng 115:58–64
8. Sadat AB (1988) Machining of graphite/epoxy composite material. Sample Q 19(2):1–4 9. Wang DH, Ramula M, Wern CW (1992) Orthogonal cutting characteristics of graphite/epoxy composite material. Trans North Am Manuf Res Inst SME 20:159–165 10. Park J (1991) A study on the effects of PCD tool geometry on the machining of graphite epoxy composite material. Dissertation, Univ Washington 11. Takeyama H, Lijama N (1998) Machinability of glass fibre reinforced plastics and applications of ultrasonic machining. Ann CIRP 37:93–97 12. Wang DH, Ramulu M, Arola D (1995) Orthogonal cutting mechanisms of graphite epoxy composite. Part I: unidirectional laminate. Int J Mach Tools Manuf 35:1623–1638 13. Harvey K, Ansell Martin P (2000) Improved timber connections using bonded-in GFRP rods, Department of Materials Science and Engineering, University of Bath, Bath, UK 14. Arbizu IP, Pérez CJL (2003) Surface roughness prediction by factorial design of experiments in turning processes. J Mater Process Technol 143–144:390–396 15. Myers RH, Montgomery DC (1995) Response surface methodology process and product optimization using designed experiments. Wiley, New York, USA 16. Feng CX, Wang X, Yu Z (2002) SME J Manuf Syst 21(5):395– 408 17. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press 18. Math Works Inc. (2002)Matlab User Manual, Version 6.5 R13, The MathWorks Inc, Natick, MA 19. Benardos PG, Vosniakos C (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43(8)833–844 20. Gurbuz O (2001) Dissertation, yapay sinir ağları ve cevap yüzeyi metotları ile kalay kaplama sürecinde kalýnlýk tahmini, Gebze Institute of Technology, Turkey (in Turkish) 21. Kurtaran H (2001) Dissertation, design optimization of structures under impact loading with approximation methods. George Washington University, Washington, DC, USA 22. Choudhury IA, El- Baradie MA (1999) Machinability assessment inconel 718 by factorial design of experiment coupled with response surface methodology. J Mater Process Technol 95(10):30–39
