Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO)

Engineering Science and Technology, an International Journal xxx (xxxx) xxx

Contents lists available at ScienceDirect

Engineering Science and Technology, an International Journal journal homepage: www.elsevier.com/locate/jestch

Full Length Article

Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO) Bobby Oedy Pramoedyo Soepangkat ⇑, Rachmadi Norcahyo, M. Khoirul Effendi, Bambang Pramujati Department of Mechanical Engineering, Faculty of Industrial Technology, Insitut Teknologi Sepuluh Nopember, Surabaya, Indonesia

a r t i c l e

i n f o

Article history: Received 1 March 2019 Revised 10 September 2019 Accepted 9 October 2019 Available online xxxx Keywords: BPNN-PSO Drilling process CFRP Multi-response optimization

a b s t r a c t An integrated approach has been applied to predict and optimize multi-performance-characteristics, being optimum thrust force (FTh), torque (M), hole entry delamination (FDen) and hole exit delamination (FDex), in the drilling process of carbon fiber reinforced polymer (CFRP). The drilling operation was performed by using a full factorial design of experiments with two different drill geometry (DG), three diverse levels of spindle speed (n), and feeding speed (Vf). The quality characteristics of FTh, M, FDen, and FDex were smaller the better. Back propagation neural network (BPNN) was first performed to model the drilling process and to predict the optimum drilling responses. Particle swarm optimization (PSO) was executed to attain the best combination of drilling parameters levels that would give optimum performance. The influences of drill geometry, speeds of spindle, and feeding speed on the responses were examined by using the response graphs. In addition, the scanning electron microscope (SEM) photos of the drilled hole are also provided to show the difference of the hole quality before and after optimization. The outcome of the confirmation experiment disclosed that the integration of BPNN and PSO managed to substantially predicted and enhanced the multi-performance characteristics accurately. Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction The usage of composite materials in the aviation, spacecraft, automotive, and sports industries has increased significantly. The composite material is chosen because it has high strength with low weight, good formability, high corrosion and wears resistance, and controlled force direction. Component assembly process made of composite with other components is generally performed by using nuts, bolts, and rivets. This assembly process requires a hole generated from the drilling process. The resulting hole that does not meet the specification will cause a failure of the connection. Due to its anisotropy and heterogeneity, drilling of composite materials is a complex and challenging machining process to control in comparison with the conventional metal machining [1]. There are several parameters that have significant effects on the dimensional and precision of composite mechanical parts, such as delamination factors, surface roughness, and thrust force. ⇑ Corresponding author. E-mail addresses: [email protected] (B.O.P. Soepangkat), [email protected] (R. Norcahyo), [email protected] (M.K. Effendi), [email protected] (B. Pramujati). Peer review under responsibility of Karabuk University.

The drilling operation could be evaluated based on the damage which appears at the hole entry or exit and the surface roughness on the wall of the hole [2–4]. Composite drilling has a significant behavior difference from conventional drilling of metals due to the delamination phenomenon. Delamination is regarded as major damage, which takes place during drilling of laminated composites. The quality of the drilled hole is extensively affected by delamination, which causes unacceptable tolerances in assembly. Delamination also increases surface roughness, decreases fatigue properties and reduces the life of the composites [5–8]. The effect of push down delamination at the drill exit can be considered more significant when compared to peel-up delamination at the drill entry while drilling of composites [9,10]. Won and Dharan [5] state that the generated thrust force and torque during drilling operations are the main contributor of poor surface roughness and occurrence of delamination. There are several reports regarding minimizing the generated thrust force during composite drilling process would reduce delamination [11–16]. There have been quite a number of researches regarding the impact of drilling parameters on thrust force, torque, and delamination [17–21]. These parameters are drill bit geometry, point angle, and diameter, as well as cutting speed and feed rate. Drill bit geometry and feeding speed affect thrust force and

https://doi.org/10.1016/j.jestch.2019.10.002 2215-0986/Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

2

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

delamination around the hole [22]. Drilling of composite materials has emerged as a notable topic for the studies of industry practitioners and academia. One of a significant factor in the machinability study of drilling composites is the determination of the optimal drilling parameters. Optimization has exceptionally practical importance especially for operating the manufacturing machinery. Therefore, it is mandatory to optimize the aforementioned drilling parameters for increasing the accuracy of drill holes [23]. There are quite a number of researches have been conducted in modeling along with a simulation of drilling CFRP composites, where the delamination-free drilling aspects have been emphasized as well as a main research focus. Multi-response optimization of drilling CFRP composites has been performed, but mostly by using statistically based optimization methods such as response surface methodology (RSM) and Taguchi method combined with one of these techniques, namely principal component analysis (PCA), grey fuzzy, fuzzy logic, and grey relational analysis. Over the years there has been a dynamic increase of complications in metal and composite cutting processes. The determination of near-optimal or optimal cutting parameters is related to the discrete and continuous parameters spaces. The objective functions or responses are multi-modal and differentiable as well as nondifferentiable. Hence, the task to determine the optimal solutions for complex problems and geometries analytically is very arduous. Soft computing has come up as a formal computer science area of study since the early 1990s. Computational approaches and methods have been used in relatively simple systems for modeling and precise analysis. However, medicine, biology, machining, and similar fields have become a more complex system. The presence of imprecision and uncertainty increased the complexity in the machining process problems and remained unsolvable to the traditional mathematical and analytical methods. It can be used for modeling and analyzing complex, nonlinear, and vague phenomenon which may exist in the process parameters. These techniques have been used as a substitute to counterpart the statistical techniques to model and analyze the metal machining processes successfully [24–28]. Optimization in the machining process using meta-heuristic method, namely, particle swarm optimization (PSO), can give rapid convergences to obtain a global optimum solution with high accuracy. Regarding the utilization of PSO, Gupta et al. [29] found that a combination of response surface methodology (RSM) with PSO, gives a better prediction and optimization result compared to RSM-teaching learning-based optimization (TLBO) on turning process of Inconel-800 using minimum quantity lubrication (MQL) cooling technique. Gosh et al. [30] modeled and optimized surface roughness of C40 steel during milling operation utilizing RSM, artificial neural network (ANN), PSO, and genetic algorithm (GA). They discovered that the integration of RSM-PSO gives an excellent result with reasonable accuracy for response surface prediction. Further, a combination of ANN with PSO method was adapted by Bensingh et al. [31] to model and optimize the manufacturing process of the bi-aspheric lens using the injection molding process. Liu et al. [32] stated that back propagation neural network based PSO gives a high accuracy prediction of grinding temperature during the grinding process of titanium matrix composites. Moreover, Moghaddam and Kolahan [33] also modeled and optimized the electrical discharge machining process of material by utilizing ANN and PSO. Optimization in the fiber reinforced polymer drilling process by implementing conventional and meta-heuristic optimization methods have been applied in recent years. Krishnamoorthy et al. [34] found the best combination of feed rate, point angle, and spindle speed to obtain the minimum entry-delamination factor, exit-delamination factor, torque, eccentricity, and thrust force during CFRP drilling process, by utilizing grey fuzzy analysis. Abhishek et al. [1] combined the Taguchi method with principal component analysis (PCA) and

fuzzy inference system (FIS) to optimize the drilling process of CFRP. They varied drill bit diameter, feed, and drill to find the best entry delamination, exit delamination, torque, and thrust force. Multi-objective optimization in drilling of CFRP composites was conducted by Abhishek et al. [35] using a fuzzy embedded harmony search (HS) algorithm. The optimization process aimed to obtain the best setting of drill bit diameter, spindle speed, and feed speed that yield minimum entry-exit delamination factor, torque, and thrust force. The implementation of PSO in composites drilling optimization was very limited. Malik et al. [36] predicted the drilling induced damage during CFRP drilling process using PSO-ANN approach. The varied drilling parameters were drill geometry, drill diameter, feed rate, and spindle speed. The response of this study was only the delamination factor. In this case, PSO was applied to optimize the weights of the interconnecting synaptics, in order to establish a better prediction model. Shunmugesh and Panneerselvam [37] optimized a drilling process of bidirectional carbon fiber epoxy composite using PSO-GSA, and also studied their machinability. The responses were the vibration, torque, and thrust force in the transverse and longitudinal direction, while the varied drilling parameters were tool material, feed rate, and cutting speed. Those responses were not optimized simultaneously, but individually. Kalita et al. [38] were minimized delamination during glass fiber reinforced polymer (GFRP) drilling process by utilizing GA and PSO, with material thickness, drill bit diameter, feed rate, and spindle speed as their varied drilling process parameters. They found that PSO was converged considerably faster with little computation time compared to the genetic algorithm. It can be summarized that PSO was only implemented for single-objective optimization in the composites drilling process. There are a lot of studies that have been conducted to model and optimize drilling of CFRP composites. Nevertheless, there is no study in which modeling and optimization in the drilling of CFRP composites have been done to attain the minimum FTh, M, FDen, and FDex simultaneously utilizing a full factorial design experiment, and integrated optimization method using BPNNPSO. Therefore, in this study BPNN has been developed to establish the relationship between drilling process parameters (input) and drilling responses (output) in drilling CFRP composites. The BPNN method utilizes three input drilling parameters (DG, n, and Vf) and four output drilling responses (FTh, M, FDen, and FDex). Multiobjective optimization then performed by applying PSO to determine the appropriate settings of drilling parameters that would result in minimum FTh, M, FDen, and FDex simultaneously. Besides conducting multi-objective optimization, the influences of the drilling parameters on the four responses are analyzed using response graphs. In addition, the scanning electron microscope (SEM) photos of the drilled hole are also provided to show the difference of the hole quality before and after optimization.

2. Experimental work The specimen material used in this study was a carbon fiber reinforced polymer (CFRP) made of pre-preg carbon fiber epoxy resin (CYCOM934-PWCT300-UT) manufactured by Cytec Engineered Materials Inc., Texas-USA. Hand lay-up method was used to fabricate the specimen plates. The fiber orientation for each layer is [45/90/45/90/45/90/45/adhesive film/45/90/45/90/45/90/45]. The specimen plate then cured using a hot blanket method at 350°F temperature to solidify CFRP specimen plate. The dimension of CFRP specimen plate after curing were 3 mm in thickness, 200 mm in width, and 300 mm in length. The mechanical properties for this specimen were 469 MPa for tensile strength and 53 GPa for tensile modulus. CFRP specimen is then cut, so the specimen plate material

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

3

Fig. 1. CFRP Specimen.

Fig. 2. Geometry of the drill bit (a) twist drill (b) brad & spur drill.

has 3 mm thickness (with 0.25 mm thickness per ply), 30 mm width, and 200 mm length as shown in Fig. 1. Two drills with different geometry were used in the experiment, i.e., two edges twist drill and brad & spur drill, which are shown in Fig. 2. Each drilling experiments were carried out with a new set of tools. The drilling experiments are performed on Brother TC-22A-O CNC Drill & Tapping Machine without using cutting fluid. In order to measure thrust force and torque, the specimen was mounted on a Kistler 9272 four-component piezoelectric dynamometer, which can be seen in Fig. 3. The scanning electron microscope (SEM) examinations were performed on the specimens by using HITACHI FlexSEM 1000 scanning electron microscope. The microscopic delamination photos were taken by utilizing Carl Zeiss Stemi DV4 Series Stereo microscope that equipped with an image capturing device. Fig. 4 illustrates the signals of thrust force (FTh) and torque (M) measured in CFRP drilling. The measured signals are obtained by applying a low pass filter which frequency is selected with respect to the spindle speed frequency. At point A the drill tip starts touching the material. The drill tip and the cutting edges or margins start entering the material at point B. From A to B the thrust force and torque increase gradually due to the progressive contact between the drill cutting edge and the material. At point B all of the drill cutting edges or margin already in contact with the material. Point C is the position where the tip and the drill cutting edges already

Fig. 3. CFRP drilling experiment equipped with Kistler 9272 dynamometer.

leaving the material. It is observed that the progressive drill tip and cutting edges exit occur between C and D. Between these two points the thrust force and torque gradually decrease to zero, contrary to what previously occurs during tip entry. The delamination mechanism in the hole entry of the CFRP drilling process is referred to peel up delamination. Peel-up delamination take places as the drill bit contacts the laminate and is schematically shown in Fig. 5. Contact between the drill bit cutting edge and the composite layer on the hole entry area produces a peeling force in the axial direction through the slope of the drill flute. The flute is prone to lift the upper CFRP layer resulting in

Fig. 4. (a) Thrust force (FTh) and (b) torque (M) measurements using Kistler 9272 dynamometer during CFRP drilling process.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

4

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

The various drilling parameters were drill geometry, spindle, and feeding speed. Drill geometry has two levels, while the other two parameters have three levels. Table 1 shows the parameters and levels used in this experiment. 3. Development of prediction model using ANN Artificial neural networks (ANN) is defined as computational networks using a large number of parallel processing units that interact with each other through weighted interconnections. An ANN is considered as an information system having certain performance characteristics which are quite similar to the biological neural network. The mathematical models of neural biology or human cognition have been used to develop ANN based on several assumptions such as:

Fig. 5. Mechanisms of peel-up delamination at hole entry.

1. Information processing is conducted at many elements which are called neurons. 2. The neurons connection links are utilized to pass the signals between neurons. 3. Each connection link possesses an associated weight which is used to multiply the signal transmitted (this is a common phenomenon in a typical neural net). 4. An activation function (usually nonlinear) is utilized by each neuron to its net input (sum of weighted input signals) in determining its output signal.

Fig. 6. Mechanisms of push-out delamination at hole exit.

the composite material to rotate upward, so there is no cutting process. As a result, the upper composite layer can be easily separated from the uncut part, causing delamination at the entrance [39]. The delamination mechanism in the hole exit of the CFRP drilling process is referred as push out delamination. Push out delamination take places as the drill bit reach to the exit side of laminates and is schematically shown in Fig. 6. As the drill bit gets closer to the bottom of the CFRP layers, the uncut CFRP thickness gets thinner hence decreases its deformation resistance. Up to a certain point, the thrust force outstrips the interlaminar bond strength and initiate crack. The uncut CFRP layers get bent due to thrust force from the drill bit. At a certain point, the thrust force outstrips the interlaminar bond strength of CFRP causing a hole exit delamination as the drill bit pierces through the exit side. This phenomenon occurs before the CFRP laminate entirely penetrated by the drill bit [40]. The value of delamination in the hole entry and hole exit is represented by delamination factor which is calculated as the ratio between maximum diameter (Dmax) in the delamination area and the nominal diameter (D0) of the drill [41], or:

D¼

Dmax ; D0

ð1Þ

In ANN, neurons will be positioned in layers called layers of neurons (neuron layers). Neurons on one layer will be associated with neurons on the preceding and succeeding layers. The information provided on the neural network will be propagated from one layer to the next layer, from the input layer to the output layer via the hidden layer. The most important factor in determining the behavior of a neuron is its activation function, which usually is not linear, and weight pattern. Generally, neurons located in the same layer will have a similar situation, so that in each layer the same neurons have the same activation function. There are three types of ANN architecture: 1. Single layer perceptron network (SLP). This network has only one input layer and one output layer with connected weights. This network receives only input then it will directly process it into output without having to go through the hidden layer. How big the relationship between two neurons is determined by the corresponding weights. The activation function used is hard limiting. The use of this activation function will produce an output of one if the amount of input weight is greater than its bias value. 2. Multi-layer perceptron network (MLP). This network has at least one hidden layer located between the input layer and the output layer. Networks with many layers can solve more difficult problems than single layers with more complicated learning. In many cases, learning on networks with multiple layers is more successful in solving problems. One of the problems in forming artificial neural network MLP is to determine how many hidden layers that can provide optimal results on artificial neural network architecture. The number of hidden

Table 1 Varied levels of drilling parameters. Parameter

Unit

Level 1

Level 2

Level 3

Drill Geometry (DG) Spindle Speed (n) Feeding Speed (Vf)

– rpm mm/min

Twist Drill 1000 50

Brad & Spur 2000 150

– 3000 250

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

layers can be determined empirically by plotting the results of artificial neural networks versus hidden layers. Hidden layers can be determined based on the results of the best exercises or the smallest errors. 3. Competitive layer network. In this network a set of neurons competes for the right to be active. Generally the relationship between neurons in this competitive layer is not shown in the architectural diagram. The ANN learning process can be performed by two approaches, i.e., supervised and unsupervised. The supervised approach uses learning samples with expected outputs and more suitable for classification problem. On the other hand, the unsupervised approach uses learning without expected outputs and appropriate for clustering problem. On the other hand, the unsupervised approach uses learning without expected outputs and appropriate for clustering problem. Rumelhart et al. [42] introduced a new supervised learning procedure, namely a back-propagation neural network (BPNN), to overcome the limitation of perceptron. This procedure can be applied for linear and non-linear classification. BPNN supervised algorithm is developed by using the error correction learning rule. In applying this rule, an error function is utilized for modifying or adjusting weights of the connection to reduce the error gradually. The difference between the actual network output and the desired output is defined as error and back propagated. The weight adjustment result causes hidden units to arrange their weights for representing the important features of the domain of task. This kind of network basically consists of an input layer, hidden layer, and an output layer neurons. The complexity of the problem will determine the number of the hidden layers as well as the number of hidden units in each hidden layers. There are two stages of the learning process in BPNN: 1. Forward propagation (first stage) In this stage, outputs are calculated based on the inputs and current weights. In this case, net excitation is calculated by using every individual hidden unit and output unit that relies on:  The values of the previous layer units which are related to the considered unit.  Weights among the preceding layer unit and considered unit.  Thresholds value on the considered unit. This net excitation will be used by activation function to appraise the output value for the considered unit. This function should be distinctive and continuous. There are numerous activation functions that can be used in a back propagation neural network, for instance sigmoid function and linear function. 2. Backward propagation of error (second stage) The propagated error is computed as the diversity among the actual output and targeted output for every individual output unit. The error value is propagated to the previous layer, i.e., hidden layer. The error for individual node/unit/neuron in the hidden layer N is computed. A similar error computation is conducted at every individual node of the preceding hidden layer, which is N-1. These computed errors are used to align the weights in order to minimize the error at each output unit. The error is reduced until meeting the designated level by repeating forward and backward steps. 4. Optimization using PSO Particle Swarm Optimization (PSO) imitates the social act of certain animals in natural habitats, such as bird flocks or fish.

5

Social act consists of the influence of other individuals in a group and individual actions. For example, the commonly used term particles denote a bird in a flock of birds. The behavior of each particle or individual is determined from by the influenced behavior of its collective group and also its own intelligence. When one particle or a bird discover the correct or short course to the source of food, the rest of the other flock will also be able to adhere the course instantly even though their position is quite distant from the group. Particle swarm optimization (PSO) is one of the branches of the evolution algorithm. PSO is developed based on the behavior of a flock of birds or fish, where they have no leader to find food so they will spread randomly to find the location of the food. This algorithm is based on the social behavior of these organisms. Social behavior consists of the influence of other individuals and individual actions. In the PSO, it is assumed that the flock has a certain size with the initial position of each particle located in a random location in a multi-dimensional space. It is also assumed that each particle has two characteristics, i.e., position and velocity. Each particle moves in a given space or area, and also memorizes the best position that has been traversed to find the food source or the objective function value. The information regarding the best position would be passed to the other particle for adjusting its position and velocity to be in accordance with the received position information. The behavior of birds within it flocks can be used as an example. Even though every bird has its limitations on intelligence, the bird will generally follow the following habits: 1. When a bird approaches a target or food, each bird rapidly sends information to the other birds in a particular flock. 2. The other bird will follow the direction to the food but not directly. 3. There is a component that depends on the mind of each bird, its memory of what has been passed in the past. The search for a solution to the PSO algorithm is carried out by a population which consists of several particles. Populations are raised randomly and the limits are the smallest and largest values of process parameters. Each particle could be regarded as the position or solution of the problem encountered, searches for an optimal solution based on its intelligence and experience by traversing the search space dimensions. Simultaneously, each particle adjusts to its best position (personal or local best), and also to the best position of the entire flock (global best) as it traverses the search space. The experience or information deployment takes place within the particle itself and also among a particle with the finest particles of the entire herd during the solution search process. The search process is then performed to find the best position for each particle in a certain number of iterations to obtain a position that is relatively equal or reach the predefined iteration limit. Each solution is represented by the particle position and would be evaluated for performance on each iteration by entering the solution into the fitness function. Each particle is regarded as a point on a certain space of dimension. There are two factors which characterized the status of the particle in its space search, namely particle position and particle velocity [43]. The following equations are used to update the the velocity and position of each particle, respectively.

 Pbest;j  xj ði  1Þ  þ c2 r 2 Gbest  xj ði  1Þ

v j ðiÞ ¼ v j ði  1Þ þ c1 r1


xj ðiÞ ¼ v j ðiÞ þ xj ði  1Þ




ð2Þ ð3Þ

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

6

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

with: i = 1; 2;    ; N denote the number of the iteration. j = 1; 2;    ; N denote the number of the particle. Pbest;j = P best;1 ; P best;2 ;    ; Pbest;j Denote the best fitness value for jth particle. Gbest = best fitness value for all of the particle. c1 ,c2 = learning factor. r1 ; r2 = random number between 1 and 0. The calculation of a new particle velocity is conducted by using Eq. (2) based upon the preceding velocity, the spacing among the present position and the personal best position, and also the spacing between the present position and the global best position. Next, the particle switch to a new position. The new positioning of the particle is calculated by using Eq. (3). PSO algorithm is executed with a number of iteration to reach the stopping criteria, so that the solutions located in global best would be obtained. Therefore, a simulation based on these two equations with a number of iteration would be conducted in a space having a certain dimension. In each iteration, the particle’s position will further lead to the intended target (minimization or maximization of function value). This is done until the maximum iteration is reached or the achievement of other stopping criteria. The PSO algorithm includes the following steps: 1. Generating the starting position of a number of particles along with its initial velocity at random. 2. Evaluating each particle fitness value based on its position. 3. Determining the particles with the best fitness and set as Gbest. For each particle, determine the initial Pbest value equal to the initial position. 4. Using existing Pbest and Gbest and equation (2) to update each particle velocity. After the new velocity is obtained, the position of each particle is also updated by using equation (3). 5. Evaluating each particle fitness value. 6. Determining the particle with the best fitness value and set as Gbest. For each particle, determine Pbest by comparing the current position with Pbest from the previous iteration. 7. Check the criteria for stopping. When the stopping criteria is reached then stop the counting cycle. If the stopping criteria has not been reached then repeat step 4 until the stopping criteria is reached. 5. Result and discussion 5.1. Experimental result The measurements of FTh, M, FDen, and FDex resulted during CFRP drilling experiment are shown in Table 2.

to make the interval value of data suitable to the interval value of activation function that will be used in BPNN modeling. Therefore, in the pre-processing method various intervals in input and output data must be modified to a single interval. Data normalization is a process of converting a data value into a value of between 1 and 1. Data pre-processing was done using a mapminmax function on Matlab R2015.a, and the used equation is:

pn ¼

2ðp  min ðpÞÞ 1 ðmax ðpÞ  min ðpÞÞ

ð4Þ

where: p = input and output BPNN data which have various unit and interval value pn = normalized input and output BPNN data which have 1 to 1 interval value and unitless. 5.2.2. Determination of BPNN network architecture and stopping criteria BPNN can produce a precise prediction if using an optimum BPNN network architecture. BPNN network architecture consists of the input and output layer with a certain number of neurons and the hidden layer with a particular number of neurons. In the input and output layer, the number of neurons defined by the number of drilling parameters and responses. Hence, the number of neuron in the input layer and the output layer are three and four correspondingly. The number of neurons in the hidden layer and the number of hidden layers were defined by conducting the trial and error method to attain the minimum value of mean square error (MSE). The design criteria for determining the BPNN architecture using trial and error method are as follows:      

Number of neuron in the hidden layer is from 4 to 35. Number of hidden layer from 1 to 5. Activation function using tansig. Training function using trainlm. Learning rate 0.05. Performance goal 0.0001.

Trial and error method was done according to those design criteria, and the result is shown that only one hidden layer with 12 neurons needed and producing 0.0031 MSE value, which is the minimum MSE. Therefore, the configuration of BPNN network architecture in this study was 3-12-4, as seen in Fig. 7. In this BPNN model, the hidden layer and output layer activation functions were tansig and purelin, respectively. The applied training function was Levenberg-Marquardt. The value of stopping criteria for BPNN training data, namely maximum epoch number, performance goal, minimum performance gradient, and maximum validation failure in succession are 10000, 0.0001, 0.00001, and 1000.

5.2. BPNN modelling Input data of BPNN is process parameters of the drilling, such as DG, n, and Vf. The responses measurements are used as the output, which are the data of FTh, M, FDen, and FDex. In general, the stages in BPNN are:  Data pre-processing.  Modeling the BPNN network architecture and determination of stopping criteria.  BPNN training, testing, and validation of data. 5.2.1. Pre-processing of the data Input and output data should be normalized prior to BPNN modeling. Normalization of the input and out data is important

5.2.3. BPNN training, testing, and validation of data Predicting response values using BPNN method requires three sets of data for training, testing, and validation. Seventy percent, fifteen percent, and fifteen percent of data were utilized for training, testing, and validating respectively. The overall graphical data of BPNN on the comparison between the predicted and experimental value of all the combinations upon each response shown in Fig. 8 and the details are listed in Table 2. The error percentage in between prediction and experiment is calculated using the following equation:

Error ¼

Exp  Pred  100%; Exp

ð5Þ

where:

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

7

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx Table 2 Experimental results and BPNN prediction. Exp 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 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Average

DG

1 2 2 1 2 1 1 2 2 2 1 2 1 1 1 1 2 2 1 2 2 2 2 1 2 2 1 1 1 2 1 2 1 1 1 2 2 2 2 1 1 2 1 1 2 2 1 1 2 2 1 1 1 2

n

1 2 1 2 2 3 2 1 1 3 1 1 2 3 2 2 3 3 1 3 1 1 2 3 3 2 2 1 2 1 3 3 1 1 2 1 3 2 3 1 2 1 2 2 3 2 1 1 3 1 1 2 3 2

Vf

3 3 2 1 2 2 3 1 2 3 1 3 2 1 3 2 1 1 1 3 3 2 1 1 2 3 2 2 3 1 3 1 1 3 2 2 1 1 3 3 3 3 2 2 2 1 1 3 3 2 1 2 1 2

FTh (N)

M (Nm)

FDen

FDex

Data

Exp.

Pred.

Error (%)

Exp.

Pred.

Error (%)

Exp.

Pred.

Error (%)

Exp.

Pred.

Error (%)

29.69 178.60 101.55 24.22 125.60 28.50 32.74 28.47 64.55 130.70 25.63 88.47 25.90 29.87 28.30 32.10 54.30 78.49 26.14 130.20 90.44 88.26 68.22 29.15 62.33 188.6 30.51 27.43 26.91 43.49 30.67 29.08 22.56 33.52 25.22 140.20 47.51 32.10 86.87 27 31.3 126.60 31.56 29.40 137.40 29.76 23.41 34.45 153.00 116.30 28.22 24.85 27.30 63.48

30.75 165.76 102.36 22.72 134.27 28.11 34.36 29.31 63.03 129.12 25.92 88.58 25.42 29.62 26.89 31.33 43.57 64.66 25.92 129.12 88.58 102.36 64.66 29.62 63.03 165.76 31.33 28.11 26.89 43.57 30.75 29.31 22.72 34.36 25.42 134.27 64.66 43.57 88.58 26.89 30.75 129.12 31.33 28.11 134.27 29.31 22.72 34.36 165.76 102.36 29.62 25.42 25.92 63.03

3.56 7.19 0.79 6.21 6.91 1.39 4.96 2.94 2.36 1.21 1.13 0.13 1.86 0.84 4.98 2.39 19.76 17.62 0.85 0.83 2.05 15.97 5.22 1.61 1.12 12.11 2.70 2.46 0.07 0.19 0.25 0.78 0.69 2.52 0.78 4.23 36.09 35.74 1.97 0.41 1.77 1.99 0.72 4.40 2.28 1.53 2.97 0.25 8.34 11.99 4.96 2.29 5.06 0.71 4.89

0.766 1.224 0.897 0.639 1.093 0.737 0.849 0.752 0.883 0.931 0.721 0.908 0.670 0.735 0.698 0.771 0.763 0.873 0.699 1.015 0.942 0.905 0.907 0.722 0.786 1.225 0.759 0.728 0.705 0.782 0.745 0.709 0.648 0.852 0.660 1.044 0.853 0.740 0.893 0.697 0.752 0.964 0.762 0.727 1.083 0.701 0.637 0.843 1.287 0.991 0.723 0.658 0.709 0.870

0.747 1.255 0.951 0.643 1.073 0.728 0.842 0.720 0.828 0.968 0.708 0.914 0.666 0.727 0.705 0.764 0.763 0.878 0.708 0.968 0.914 0.951 0.878 0.727 0.828 1.255 0.764 0.728 0.705 0.763 0.747 0.720 0.643 0.842 0.666 1.073 0.878 0.763 0.914 0.705 0.747 0.968 0.764 0.728 1.073 0.720 0.643 0.842 1.255 0.951 0.727 0.666 0.708 0.828

2.37 2.57 6.00 0.57 1.86 1.20 0.81 4.30 6.26 4.01 1.85 0.57 0.59 1.02 0.95 0.98 0.08 0.55 1.34 4.60 3.04 5.11 3.21 0.81 5.34 2.48 0.64 0.06 0.05 2.35 0.38 1.46 0.87 1.16 0.90 2.74 2.96 3.15 2.32 1.12 0.58 0.44 0.24 0.09 0.96 2.67 0.95 0.07 2.46 4.07 0.61 1.22 0.12 4.89 1.89

1.435 1.145 1.073 1.158 1.087 1.189 1.523 1.020 1.054 1.092 1.256 1.074 1.072 1.383 1.379 1.285 1.054 1.072 1.280 1.099 1.068 1.064 1.064 1.316 1.042 1.113 1.261 1.202 1.398 1.048 1.450 1.034 1.192 1.519 1.043 1.095 1.078 1.052 1.078 1.402 1.425 1.084 1.245 1.195 1.092 1.028 1.178 1.512 1.125 1.079 1.352 1.050 1.279 1.046

1.451 1.134 1.070 1.185 1.092 1.199 1.512 1.023 1.048 1.092 1.272 1.075 1.059 1.350 1.398 1.264 1.056 1.074 1.272 1.092 1.075 1.070 1.074 1.350 1.048 1.134 1.264 1.199 1.398 1.056 1.451 1.023 1.185 1.512 1.059 1.092 1.074 1.056 1.075 1.398 1.451 1.092 1.264 1.199 1.092 1.023 1.185 1.512 1.134 1.070 1.350 1.059 1.272 1.048

1.09 0.97 0.27 2.37 0.48 0.82 0.70 0.27 0.54 0.04 1.31 0.12 1.17 2.33 1.37 1.64 0.20 0.19 0.59 0.68 0.68 0.58 0.94 2.59 0.61 1.88 0.21 0.27 0.01 0.78 0.05 1.09 0.55 0.44 1.58 0.25 0.37 0.39 0.25 0.29 1.80 0.69 1.52 0.31 0.02 0.51 0.63 0.02 0.79 0.82 0.13 0.90 0.51 0.22 9.74

1.109 1.520 1.286 1.014 1.372 1.070 1.124 1.078 1.154 1.445 1.032 1.344 1.054 1.048 1.086 1.074 1.123 1.213 1.038 1.435 1.324 1.272 1.245 1.049 1.132 1.570 1.060 1.074 1.072 1.132 1.098 1.085 1.021 1.114 1.042 1.384 1.189 1.114 1.365 1.083 1.104 1.472 1.070 1.068 1.367 1.081 1.018 1.105 1.548 1.293 1.043 1.047 1.035 1.149

1.097 1.533 1.281 1.021 1.375 1.071 1.106 1.080 1.148 1.451 1.038 1.341 1.047 1.033 1.073 1.068 1.122 1.217 1.038 1.451 1.341 1.281 1.217 1.033 1.148 1.533 1.068 1.071 1.073 1.122 1.097 1.080 1.021 1.106 1.047 1.375 1.217 1.122 1.341 1.073 1.097 1.451 1.068 1.071 1.375 1.080 1.021 1.106 1.533 1.281 1.033 1.047 1.038 1.148

1.04 0.88 0.40 0.71 0.23 0.12 1.64 0.21 0.50 0.43 0.58 0.20 0.67 1.47 1.24 0.52 0.06 0.32 0.00 1.13 1.31 0.69 2.26 1.54 1.44 2.33 0.77 0.23 0.05 0.85 0.05 0.43 0.02 0.73 0.47 0.64 2.34 0.75 1.73 0.97 0.60 1.41 0.17 0.31 0.60 0.07 0.32 0.06 0.91 0.94 0.95 0.01 0.29 0.07 0.72

Exp = Measurement value of responses from CFRP drilling experiment. Pred = Prediction value of responses from BPNN prediction. The average error between the experimental and predicted was no more than 10%. This confirms the idea that the prediction on the responses is not very much different from the experimental data [44]. Fig. 9 shows that the BPNN output or predicted result are in excellent conformity with the experimental values, i.e., the achieved correlation coefficient for training, testing, validating, and all data in succession were 0.99522, 0.97938, 0.99111, and 0.99287.

Validating Training Testing Testing Training Testing Testing Training Training Training Training Training Training Testing Testing Training Training Training Training Training Validating Training Training Validating Training Validating Training Training Training Training Training Training Training Validating Training Training Training Training Testing Validating Validating Training Training Training Training Training Training Training Training Training Training Validating Training Testing

5.3. Optimization using PSO The general steps of optimization using PSO are as follows:  Definition of the fitness function.  Determination of PSO parameters.  Optimization using PSO and confirmation experiment. 5.3.1. Definition of the fitness function The fitness function is an essential function that used to attain the optimum value of responses during the drilling process. Fitness function could be an objective function or its modification. A num-

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

8

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

Fig. 7. BPNN network architecture.

(a)

(b)

(c)

(d)

Fig. 8. Comparisons between prediction and experimental data of BPNN for (a) FTh, (b) M, (c) FDen, and (d) FDex.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

ber of potential solutions will be randomly generated during the first iteration of the optimization process, and these potential solutions will be appraised by utilizing a fitness function [45]. In the present study, those function was developed by combining four objective functions, where each objective function belonged to FTh, M, FDen, and FDex. A tansig (hyperbolic tangent sigmoid) transfer function was used as an activation function. The objective function for each response and the activation value of each neuron in the hidden layer were developed by using the following equations [46].

Objk ¼

12 X

v jk :



j¼1

z¼

12 X


uij :xi



2 1 þ e2z

!

 1

þ v 0k

ð6Þ

! ð7Þ

þ u0j

j¼1

where:

9

After the four objective function were obtained, they were combined into a one fitness function and would be minimized as shown in the following equation:

minimizef ðxÞ ¼ Obj1 þ Obj2 þ Obj2 þ Obj4

ð8Þ

where: Obj1 Obj2 Obj3 Obj4

= Ojective = Ojective = Ojective = Ojective

function function function function

of of of of

thrust force. torque. hole entry delamination. hole exit delamination.

5.3.2. Determination of PSO parameters The PSO parameters should be determined properly in order to achieve the optimum value of responses. The parameters used in the optimization using PSO are: Number of particle = 100 Number of maximum iteration = 1000

i = number of drilling parameters. j = number of neurons in hidden layer. k = number of drilling responses. uij = weight value from input layer to hidden layer. v jk = weight value from hidden layer to output layer. u0j = bias value from the input layer to hidden layer. v 0k = bias value from hidden layer to output layer.

The PSO optimization process will be terminated if one of the termination criteria has been reached. The criteria for termination of PSO are:  Reaching the maximum number of iteration, or  Variation of Pbest, j<108

Fig. 9. Correlation coefficients graph of BPNN.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

10

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

Table 3 Lower and upper limit interval of CFRP drilling process parameters. Drilling Parameters

Unit

Limit

Drill Geometry Spindle Speed Feeding Speed

– rpm mm/min

Lower

Upper

1 1000 50

2 3000 250

The setting of process parameters obtained from PSO optimization needs to be limited in order to be in the interval values of the process parameters. The lower and upper limit values of the parameters of the CFRP drilling process showed in Table 3. The twist drill and brad & spur drill geometry are coded as 1 and 2 respectively. 5.3.3. Result of optimization and confirmation experiment By solving multi-performance optimization utilizing PSO method, the optimum FTh, M, FDen, and FDex could be attained by combining the twist drill geometry with spindle speed at 2993 rpm and feeding speed at 79 mm/min. This process parameters setting is then used as an input to predict the responses values using BPNN. The comparison between the predicted FTh, M, FDen, and

FDex using BPNN and confirmation experiments are shown in Table 4. The confirmation experiments using optimum drilling parameter setting are replicated five times, and the averages are presented in Table 4. It can be seen that the errors between the results of BPNN-PSO prediction and confirmation experiment do not exceed 5% for FTh, M, FDen, and FDex (drilling responses), which verifies that the prediction of the drilling responses is close to the experimental data. Fig. 10 shows the microscopic delamination photos in the hole entry and hole exit before optimization using twist drill, spindle speed at 1000 rpm and feeding speed at 250 mm/min. The minimum delamination in the hole entry and hole exit was attained by applying twist drill, using spindle speed of 2993 rpm and feeding speed of 79 mm/min. Fig. 11 (a) shows the drilled hole surface under the conditions before optimization (drill bit using twist drill, spindle speed = 1000 rpm, and feeding speed = 250 mm/min). There are various types of defects formed such as fiber pull-out, interlaminar delamination, exit delamination, and matrix smearing. Meanwhile, the drilled hole surfaces obtained after drilling with optimized parameters (drill bit using twist drill, spindle speed = 2993 rpm, and feeding speed = 79 mm/min) is shown in Fig. 11 (b). The drilled

Table 4 Comparison between BPNN-PSO prediction and confirmation experiments. Drilling Parameters

FTh (N)

M (Nm)

FDen

FDex

DG

N

Vf

pred./exp.

Error (%)

pred./exp.

Error (%)

pred./exp.

Error (%)

pred./exp.

Error (%)

Twist drill

2993

79

22.99/22.12

3.9

0.634/0.636

0.31

1.132/1.130

0.18

1.028/1.034

0.58

Fig. 10. Delamination defects in the hole entrance and exit that were observed before and after optimization.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

11

Fig. 11. SEM photograph of drilled hole (a) before and (b) after optimization.

Fig. 12. Response graphs for the mean values of (a) FTh, (b) M, (c) FDen, and (d) FDex.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

12

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

Fig. 13. The relationship of thrust force with (a) FDen and (b) FDex during drilling process of CFRP using brad & spur drill geometry.

hole surface is smooth and containing only minor matrix smearing while the signs of delamination are already disappeared.

6. Effect of the drilling process parameters 6.1. Effect of the drill bit geometry and drilling parameters on the thrust force (FTh), torque (M), hole entry delamination (FDen), and hole exit delamination (FDex) The response graphs for all responses are presented in Fig. 12. It can be seen in Fig. 12 (a) and (b) that the use of twist drill bit would decrease thrust force and torque. The twist drill bit would decrease thrust force, because it has better penetration ability than brad & spur drill bit. By using the twist drill bit, the cutting process of the CFRP material progresses gradually from the chisel edge to the outermost cutting edge. On the other hand, the usage of brad & spur drill bit causes the cutting process of CFRP material in the chisel edge and cutting edge occurs at the same time. Therefore, the twist drill geometry is capable to produce smaller torque during CFRP drilling process as has also been obtained by Kumar et al. [40]. Regarding the effect of spindle speed on thrust force and torque, the increasing spindle speed would increase the cutting temperature. This phenomenon would soften the CFRP material during the cutting process. Therefore, the increasing spindle speed would decrease thrust force and torque as shown in Fig. 12 (a) and (b). The same figures also show that the increasing feeding speed would increase thrust force and torque. This corresponds to the thrust force and torque empirical equations during the drilling process, which state that both thrust force and torque are increasing along with the increase of feeding speed [47]. The phenomena shown in Fig. 12 (a) and (b) are quite similar to the results found in Abhisek et al. [1,35] Fig. 12 (c) shows that the use of brad & spur drill bit will decrease hole entry delamination. Delamination at the hole entry is affected by the contact surface area of the drill bit. Contact surface area for twist drill bit defined by one point of the drill tip and the length of the two chisel edge, whereas the brad & spur drill bit is only influenced by three points of the drill tip without the length of chisel edge. This causes twist drill bit has more contact surface area than brad & spur drill bit, and produces larger hole entry. In this figure, it is also shown that the increase in spindle speed would decrease hole entry delamination. It has been mentioned that the increase of the spindle speed would decrease thrust force during the drilling process and produce smaller hole entry delamination. It can be seen in Fig. 12 (c) that slow feeding speed would lower thrust force and consequently decreases the hole entry delamination. The similar findings are also obtained by Krishnamoorthy et al. [34], and Tan and Azmi [48].

Fig. 12 (d) shows that the use of a twist drill bit would decrease hole exit delamination. Brad & spur drill bit has a more extended drill tip than a twist drill bit. This made uncut CFRP laminate zone wider due to excessive thrust force. Therefore, hole exit delamination produced by twist drill bit is smaller than brad & spur drill. This figure also indicates that the increase in spindle speed would decrease hole exit delamination. The increase in the spindle speed would decrease thrust force during the drilling process, which in turn decrease the hole exit delamination. Fig. 12 (d) also depicts that the decreasing of feeding speed would decrease the thrust force during the drilling process, which causes the decrease of the hole exit delamination during CFRP drilling process. These results are in agreement with the results of Kalita et al. [49]. 6.2. Correlation of thrust force (FTh) on delamination In the drilling process of CFRP composite, the thrust force generated is considered as the important factor for explaining the occurrence of delamination. Fig. 13 (a) and (b) illustrate the correlation between thrust force and delamination in the hole entry and hole exit during drilling of CFRP composite using brad & spur drill bit. It is obvious that the trends variation of thrust force and delamination factor with experimental data for brad & spur drill bit is alike. Hence, the drilling induced delamination by can be lowered by reducing the thrust force emerged during drilling of CFRP using brad & spur drill bit. The same phenomenon is also observed while drilling of CFRP using twist drill bit. A similar procedure to correlate thrust force and delamination factor were also applied by Shetty et al. [45]. 7. Conclusion In this study, the integration of particle swarm optimization (PSO) and back propagation neural network (BPNN) have been applied to minimize thrust force, torque, hole entry delamination, and hole exit delamination in the drilling of CFRP composites. The experimental works and optimization come up with the following concluding remarks:  Based on the process analysis, thrust force, torque, hole entry delamination, and hole exit delamination increase considerably with increasing feeding speed compared to the increasing of spindle speed and the type of drill geometry. Thrust force, torque, hole entry delamination, and hole exit delamination are affected by the type of drill geometry in the lesser degree compared to the effect of feeding speed. Spindle speed has the second-highest effect only on torque.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

 The SEM analysis of the drilled hole surface before optimization indicates the occurrence of defects such as fiber pull-out, interlaminar delamination, exit delamination, and matrix smearing. After optimization, the SEM analysis of the drilled hole reveals those defects can be eliminated successfully.  BPNN has been applied to predict the responses such as thrust, torque, hole entry delamination, and hole exit delamination. Various BPNN architectures have been studied and 3–12-4 configuration (one hidden layer with twelve neurons, three neurons on the input layer and four neurons on the output layer is obtained as the best architecture. The mean square error (MSE) of this architecture configuration is 0.0031. Trainlm, tansig, and purelin were the activation functions used on BPNN training, hidden layer, and output layer subsequently.  BPNN has successfully predicted the minimum FTh, M, FDen, and FDex after properly trained since the average error produced are less than 5%.  The minimum thrust force, torque, hole entry delamination, and hole exit delamination obtained by applying PSO for the input parameters investigated. The four responses can be minimized simultaneously by the usage of a two edges twist drill geometry, high spindle speed (2933 rpm) and low feeding speed (79 mm/min).  BPNN based PSO optimization method is effective and acceptable due to all of the relative errors between prediction and experiments confirmation are less than 5%. For future work, this study can still be expanded based on the type of CFRP materials such as fiber type, fiber orientation, material thickness, etc. Furthermore, a combination of ANN with more recent nature-inspired optimization methods such as grey wolf optimizer, grasshopper optimization algorithm, dragonfly algorithm, whale optimization algorithm can be utilized as the prediction and optimization methods.

Acknowledgments The authors acknowledge the Research Grant Provided by the Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia. We are also indebted to GMF Aeroasia for providing the CFRP composite.

References [1] K. Abhishek, S. Datta, S.S. Mahapatra, Optimization of thrust, torque, entry, and exist delamination factor during drilling of CFRP composites, Int. J. Adv. Manuf. Technol. 76 (2014) 401–416, https://doi.org/10.1007/s00170-014-6199-3. [2] T. Radhakrishnan, S.M. Wu, On-line hole quality evaluation for drilling composite material using dynamic data, J. Eng. Ind. 103 (1981) 119, https:// doi.org/10.1115/1.3184452. [3] J. Campos Rubio, A.M. Abrao, P.E. Faria, A.E. Correia, J.P. Davim, Effects of high speed in the drilling of glass fibre reinforced plastic: evaluation of the delamination factor, Int. J. Mach. Tools Manuf 48 (2008) 715–720, https://doi. org/10.1016/j.ijmachtools.2007.10.015. [4] A. Velayudham, R. Krishnamurthy, Effect of point geometry and their influence on thrust and delamination in drilling of polymeric composites, J. Mater. Process. Technol. 185 (2007) 204–209, https://doi.org/10.1016/j. jmatprotec.2006.03.146. [5] M.S. Won, C.K.H. Dharan, Drilling of aramid and carbon fiber polymer composites, J. Manuf. Sci. Eng. 124 (2002) 778, https://doi.org/10.1115/ 1.1505854. [6] D. Che, I. Saxena, P. Han, P. Guo, K.F. Ehmann, Machining of carbon fiber reinforced plastics/polymers: a literature review, J. Manuf. Sci. Eng. 136 (2014), https://doi.org/10.1115/1.4026526. [7] S. Arul, L. Vijayaraghavan, S.K. Malhotra, R. Krishnamurthy, The effect of vibratory drilling on hole quality in polymeric composites, Int. J. Mach. Tools Manuf. 46 (2006) 252–259, https://doi.org/10.1016/j.ijmachtools.2005.05.023. [8] T.J. Grilo, R.M.F. Paulo, C.R.M. Silva, J.P. Davim, Experimental delamination analyses of CFRPs using different drill geometries, Compos. B Eng. 45 (2013) 1344–1350, https://doi.org/10.1016/j.compositesb.2012.07.057.

13

[9] D.F. Liu, Y.J. Tang, W.L. Cong, A review of mechanical drilling for composite laminates, Compos. Struct. 94 (2012) 1265–1279, https://doi.org/10.1016/ j.compstruct.2011.11.024. [10] A.M. Abrão, P.E. Faria, J.C.C. Rubio, P. Reis, J.P. Davim, Drilling of fiber reinforced plastics: a review, J. Mater. Process. Technol. 186 (2007) 1–7, https://doi.org/ 10.1016/j.jmatprotec.2006.11.146. [11] J.P. Davim, P. Reis, Study of delamination in drilling carbon fiber reinforced plastics (CFRP) using design experiments, Compos. Struct. 59 (2003) 481–487, https://doi.org/10.1016/S0263-8223(02)00257-X. [12] J.P. Davim, P. Reis, Drilling carbon fiber reinforced plastics manufactured by autoclave-experimental and statistical study, Mater. Des. 24 (2003) 315–324, https://doi.org/10.1016/S0261-3069(03)00062-1. [13] C.C. Tsao, H. Hocheng, Taguchi analysis of delamination associated with various drill bits in drilling of composite material, Int. J. Mach. Tools Manuf. 44 (2004) 1085–1090, https://doi.org/10.1016/j.ijmachtools.2004.02.019. [14] F. Su, Z. Wang, J. Yuan, Y. Cheng, Study of thrust forces and delamination in drilling carbon-reinforced plastics (CFRPs) using a tapered drill-reamer, Int. J. Adv. Manuf. Technol. 80 (2015) 1457–1469, https://doi.org/10.1007/s00170015-7124-0. [15] A. Caggiano, R. Angelone, R. Teti, Image analysis for CFRP drilled hole quality assessment, Procedia CIRP. 62 (2017) 440–445, https://doi.org/10.1016/j. procir.2017.03.045. [16] L. Liu, C. Qi, F. Wu, J. Xu, X. Zhu, Experimental thrust forces and delamination analysis of GFRP laminates using candlestick drills, Mater. Manuf. Processes 33 (2018) 695–708, https://doi.org/10.1080/10426914.2017.1376072. [17] L. Sorrentino, S. Turchetta, C. Bellini, A new method to reduce delaminations during drilling of FRP laminates by feed rate control, Compos. Struct. 186 (2018) 154–164, https://doi.org/10.1016/j.compstruct.2017.12.005. [18] S.L. Soo, A.M. Abdelhafeez, M. Li, R. Hood, C.M. Lim, The drilling of carbon fibre composite–aluminium stacks and its effect on hole quality and integrity, Proc. Inst. Mech. Eng., Part B 233 (2019) 1323–1331, https://doi.org/10.1177/ 0954405417728312. [19] H.B. Kaybal, A. Ünüvar, M. Koyunbakan, A. Avcı, A novelty optimization approach for drilling of CFRP nanocomposite laminates, Int. J. Adv. Manuf. Technol. 100 (2019) 2995–3012, https://doi.org/10.1007/s00170-018-2873-1. [20] J. Xu, C. Li, M. Chen, M. El Mansori, F. Ren, An investigation of drilling highstrength CFRP composites using specialized drills, Int. J. Adv. Manuf. Technol. (2019), https://doi.org/10.1007/s00170-019-03753-8. [21] R. Angelone, A. Caggiano, I. Improta, L. Nele, R. Teti, Characterization of hole quality and temperature in drilling of Al/CFRP stacks under different process condition, Procedia CIRP. 79 (2019) 319–324, https://doi.org/10.1016/j. procir.2019.02.074. [22] L.M.P. Durão, D.J.S. Gonçalves, J.M.R.S. Tavares, V.H.C. de Albuquerque, A. Aguiar Vieira, A. Torres Marques, Drilling tool geometry evaluation for reinforced composite laminates, Compos. Struct. 92 (2010) 1545–1550, https://doi.org/10.1016/j.compstruct.2009.10.035. [23] S. Jayabal, U. Natarajan, Optimization of thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites using Nelder-Mead and genetic algorithm methods, Int. J. Adv. Manuf. Technol. 51 (2010) 371–381, https:// doi.org/10.1007/s00170-010-2605-7. [24] I. Mukherjee, P.K. Ray, A review of optimization techniques in metal cutting processes, Comput. Ind. Eng. 50 (2006) 15–34, https://doi.org/10.1016/ j.cie.2005.10.001. [25] R.V. Rao, Advanced Modeling and Optimization of Manufacturing Processes, Springer-Verlag, London, London, 2011. doi:10.1007/s10895-017-2051-0. [26] S. Al-Zubaidi, J.A. Ghani, C.H. Che Haron, Application of ANN in milling process: a review, Model. Simul. Eng. 2011 (2011), https://doi.org/10.1155/2011/ 696275. [27] J.P. Davim, Machining: Fundamentals and recent advances, London (2008), https://doi.org/10.1007/978-1-84800-213-5. [28] B.O.P. Soepangkat, B. Pramujati, M.K. Effendi, R. Norcahyo, A.M. Mufarrih, Multi-objective optimization in drilling kevlar fiber reinforced polymer using grey fuzzy analysis and backpropagation Neural Network-Genetic Algorithm (BPNN–GA) Approaches, Int. J. Precis. Eng. Manuf. 20 (2019) 593–607, https:// doi.org/10.1007/s12541-019-00017-z. [29] M.K. Gupta, M. Mia, C.I. Pruncu, W. Kapłonek, K. Nadolny, K. Patra, T. Mikolajczyk, D.Y. Pimenov, M. Sarikaya, V.S. Sharma, Parametric optimization and process capability analysis for machining of nickel-based superalloy, Int. J. Adv. Manuf. Technol. (2019) 3995–4009, https://doi.org/10.1007/s00170-019-03453-3. [30] G. Ghosh, P. Mandal, S.C. Mondal, Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization, Int. J. Adv. Manuf. Technol. 100 (2019) 1223–1242, https://doi.org/10.1007/s00170-017-1417-4. [31] R.J. Bensingh, R. Machavaram, S.R. Boopathy, C. Jebaraj, Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO), Measure.: J. Int. Measure. Confederation. 134 (2019) 359–374, https://doi.org/10.1016/j. measurement.2018.10.066. [32] C. Liu, W. Ding, Z. Li, C. Yang, Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm, Int. J. Adv. Manuf. Technol. 89 (2017) 2277–2285, https://doi.org/10.1007/ s00170-016-9267-z. [33] M. Azadi Moghaddam, F. Kolahan, Using combined artificial neural network and particle swarm optimization algorithm for modeling and optimization of electrical discharge machining process, Scientia Iranica. (2019), https://doi. org/10.24200/sci.2019.5152.1123.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002

14

B.O.P. Soepangkat et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx

[34] A. Krishnamoorthy, S. Rajendra Boopathy, K. Palanikumar, J. Paulo Davim, Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics, Measure.: J. Int. Measure. Confederation. 45 (2012) 1286–1296, https://doi.org/10.1016/j. measurement.2012.01.008. [35] K. Abhishek, S. Datta, S.S. Mahapatra, Multi-objective optimization in drilling of CFRP (polyester) composites: application of a fuzzy embedded harmony search (HS) algorithm, Measure.: J. Int. Measur. Confederation 77 (2016) 222– 239, https://doi.org/10.1016/j.measurement.2015.09.015. [36] J. Malik, R. Mishra, I. Singh, PSO-ANN approach for estimating drilling induced damage in Cfrp laminates, Adv. Prod. Eng. Manage. APEM 6 (2011) 95–104. [37] K. Shunmugesh, K. Panneerselvam, Machinability study of Carbon Fiber Reinforced Polymer in the longitudinal and transverse direction and optimization of process parameters using PSO–GSA, Eng. Sci. Technol., Int. J. 19 (2016) 1552–1563, https://doi.org/10.1016/j.jestch.2016.04.012. [38] K. Kalita, P.K. Mallick, A.K. Bhoi, R.K. Ghadai, Optimizing drilling induced delamination in GFRP composites using genetic algorithm & particle swarm optimisation, Adv. Compos. Lett. 27 (2018) 1–9, https://doi.org/10.1177/ 096369351802700101. [39] U.A. Khashaba, Drilling of polymer matrix composites: a review, J. Compos. Mater. 47 (2013) 1817–1832, https://doi.org/10.3224/zff.v30i2.02. [40] D. Kumar, K.K. Singh, R. Zitoune, Experimental investigation of delamination and surface roughness in the drilling of GFRP composite material with different drills, Adv. Manuf. Polym. Compos. Sci. 2 (2016) 47–56, https://doi. org/10.1080/20550340.2016.1187434. [41] V.N. Gaitonde, S.R. Karnik, J. Paulo Davim, Taguchi multiple-performance characteristics optimization in drilling of medium density fibreboard (MDF) to minimize delamination using utility concept, J. Mater. Process. Technol. 196 (2008) 73–78, https://doi.org/10.1016/j.jmatprotec.2007.05.003.

[42] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by backpropagating errors, Nature 323 (1986) 533–536, https://doi.org/10.1038/ 320129a0. [43] J. Kennedy, R. Eberhart, Particle swarm optimization, in: IEEE International Conference on Neural Networks, 1995, pp. 1942–1948, https://doi.org/ 10.1016/0040-1951(89)90193-5. [44] Y. Rong, Z. Zhang, G. Zhang, C. Yue, Y. Gu, Y. Huang, C. Wang, X. Shao, Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA, Opt. Lasers Eng. 67 (2015) 94–104, https://doi.org/10.1016/j. optlaseng.2014.10.009. [45] N. Shetty, M.A. Herbert, R. Shetty, D.S. Shetty, G.S. Vijay, Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite, Appl. Soft Comput. J. 41 (2016) 466–478, https://doi.org/10.1016/j. asoc.2016.01.016. [46] R. Norcahyo, B.O.P. Soepangkat, Sutikno, Multi response optimization of thrust force and delamination in carbon fiber reinforced polymer (CFRP) drilling using backpropagation neural network-particle swarm optimization (BPNN-PSO), in: AIP Conference Proceedings, 2018, https://doi.org/10.1063/1.5046263. [47] E.J.A. Armarego, Material Removal Processes: Twist Drills and Drilling Operations, University of Melbourne, Department of Mechanical and Manufacturing Engineering, Manufacturing Science Group, Melbourne, 1996. [48] C.L. Tan, A.I. Azmi, Analytical study of critical thrust force for on-set delamination damage of drilling hybrid carbon/glass composite, Int. J. Adv. Manuf. Technol. 92 (2017) 929–941, https://doi.org/10.1007/s00170-0170152-1. [49] K. Kalita, P.K. Mallick, A.K. Bhoi, R.K. Ghadai, Optimizing drilling induced delamination in GFRP composites using genetic algorithm& particle swarm optimisation, Adv. Compos. Lett. 27 (2018) 1–9, https://doi.org/10.1177/ 096369351802700101.

Please cite this article as: B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi et al., Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO), Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.10.002