Application of BPNN-GA To Optimize Drilling Process of ...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:19 No:04 30

191804-3535-IJMME-IJENS © August 2019 IJENS I J E N S

Abstract— Metal composite stacks such as stainless steel-glass

fiber reinforced polymer or stainless steel-GFRP stacks can be

considered as an innovative structural configuration in the

manufacturing industry. Several industrial applications require

holes to be drilled out in the stacked materials for assembly

purposes. The high quality holes in stainless steel-GFRP stacks

could be achieved by minimizing thrust force (Fz) and torque (Mz)

of the drilling process, as well as the hole surface roughness (SR)

and delamination (D). The drilling sequence strategy was drilling

from stainless steel to GFRP. In this case, Fz and Mz were

measured during stainless steel drilling process. The

measurements of SR were also done in the holes of stainless steel,

while the D measurements were performed in the GFRP’s holes.

In this study, the minimization of Fz, Mz, SR, and D has been

conducted by applying back propagation neural network (BPNN)

combined with genetic algorithm (GA) optimization method. The

drilling experiments were carried on by utilizing a full factorial

3x3x3 design of experiments. The varying drilling parameters

were spindle speed, feed rate, and drill point angle. The quality

characteristics of Fz, Mz, SR, and D were smaller the better. BPNN

was first performed to obtain the modeling of drilling experiment

and a prediction of optimum drilling responses. GA was then

executed to attain the best combination of drilling parameters

levels that would minimize Fz, Mz, SR, and D. The influence of

spindle speed, feed rate, and drill point angle on responses was

performed by reviewing the response graphs. The outcome of a

confirmation experiment disclosed that the integration of BPNN

and GA manage to substantially enhance and predict the multi-

performance characteristics accurately.

Index Term— BPNN, Drilling process, GA, GFRP, Multi

performance optimization, Stainless steel

I. INTRODUCTION

Composites materials possess many appreciable properties,

but on some occasions when strength is very required, they are

not much of use. In these occasions, it has now become a

common practice to utilize a metal which is stacked with a

polymer composite. This combination will give a better strength

to weight ratio, which has been a fundamental requirement in

defense and aerospace industries. One type of the bi-material

assemblies which comprise two different constituents is

stainless steel and glass fiber reinforced polymer composites

(GFRP). These metal composites stacks can furnish integrated

structural advantages of each stacked phase and also eludes

their individual shortcomings.

Stainless steel AMS 5528 or 17-7 PH is categorized as

precipitation-hardening austenitic stainless steel. It possesses

excellent fatigue properties, good corrosion resistance, high

strength and hardness, good formability, and minimum

distortion on heat treatment. GFRP composites are important

materials used in a number of industries, such as automobiles,

marine vessels, aircraft, construction, audio equipment,

electrical power generation, and sporting goods manufacturing,

owing to the benefit of possessing good mechanical properties.

Some of the advantages of properties are high tensile strength,

strength to weight ratio, fracture toughness and also excellent

corrosion and thermal resistance. With the oncoming utilization

of stainless steel-GFRP composites stacks in many types of

structural parts, machining process of metal composites stacks

have become main attention for the manufacturing industries.

In assembling these metal composites stacks, the basic

manufacturing applied is hole drilling. In drilling dissimilar

materials which involve different material properties, it is

necessary to choose appropriate drilling parameters that nearly

accords with drilling conditions for both materials. Xu et al. [1]

conducted a review work regarding the drilling of metal

composite stacks and stated that drilling metal composites stack

or bi-material system could cause some problems. There is a

significant reason that can be related between dissimilar

properties of the stacked materials and the poor machinability

of each material. During the drilling process, the severe hole

damage problems including the unrepairable delamination in

the composite part and burrs in the metal part always happen in

the material removal process and causing quite a number of hole

defects. Aside from that, high tool wear rate and premature tool

wear can give a significant contribution to the increase of

machining costs. In specific, the use of incorrect drilling

parameters and drill bits, and also the unfavourable cutting

environments are the causes of out of specification parts [2].

The determination of appropriate drilling parameters for

Application of BPNN-GA

To Optimize Drilling Process of Stainless Steel-

GFRP Stacks

Bobby O. P. Soepangkat1*, Rachmadi Norcahyo1, M. Khoirul Effendi1, Angga Sateria2, Bambang

Pramujati1

1Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember 2Mechanical Engineering Department, Politeknik Manufaktur Bangka Belitung

Gedung C Lantai II, Kampus ITS Sukolilo, Surabaya 60111, East Java, Indonesia

Corresponding Author: [email protected]



metal composites stacks is still limited. The quality of drilling

hole in a precision drilling is essential and significantly affected

by generated thrust force and torque. The higher magnitude of

thrust force and torque would decrease the accuracy of the

drilling. Over the years, there are quite a number of research

conducted trying to minimize thrust force and torque generated,

particularly during the precision drilling operation.

In drilling dissimilar materials which involves different

material properties, it is necessary to choose appropriate drilling

parameters that nearly accords with drilling conditions for both

materials. The drilling process in stainless steel and composites

basically uses the same drilling parameters, i.e., spindle speed,

feed rate, drill point angle, and drill point geometry. The drilling

process performances on stainless steel can be measured by

several machinability criteria (thrust force and torque) or

critical to quality characteristics (hole roundness and hole

surface roughness). The similar drilling process performances

are also applied for composites material, plus delamination.

Drilling parameters such as feed rate and tool geometry affect

thrust force, which in turn initiates delamination. Delamination,

hole surface roughness, torque, and thrust force can be

considered as responses which emerge during drilling process.

The higher the values of thrust force and torque, the lesser will

be the quality of drilled hole. Therefore, it is necessary to

minimize those four responses simultaneously to increase the

quality of drilled hole. Establish the optimal drilling parameters

by applying multiple performance optimization could improve

the drilling result simultaneously.

Xu and El [2]stated that in hybrid composite metal stacks

drilling, there are two different drilling strategies that could be

applied, i.e., drilling from composite to metal and from metal to

composite. This strategy is related to the aspect of the entry and

exit of the drill bit throughout the chip removal process. Their

study also revealed that most researchers [3]–[6] have

conceived that the composite to metal drilling order is

frequently a rational and efficient strategy for reducing the

considerable hole damage which occurs during vertical drilling

of the bi-material system. Their main arguments were that in

that particular drilling order, a lesser degree of exit

delamination breakage can be attained since the metal part at

the bottom will prevent the occurrence of laminate deflection

and restrict the workpiece dynamics [1], [3]. In addition, Qi et

al. [7] have proposed an analytical model for drilling composite

to metal stacks that ascertained a higher critical thrust force

(CTF) value than that will be acquired in the metal to composite

stacks. This means that delamination which occurs during

composite to metal stacks drilling will be lower than during

metal to composite stacks drilling. Nevertheless, there are

several innate disadvantages possessed by this drilling order.

For example, the position of the metal at the bottom will cause

metal chip evacuation seriously difficult. Furthermore, the

spiral form which characterizes the chips of the drill-out metal

and also hot can induce substantial erosion/abrasions activities

on the composite part and seriously deteriorate the hole quality.

In addition, the spiral metal chips can entangle themselves

within the drill bit, which can induce premature tool failures

such as edge fracture or microchipping. On the contrary, the

metal composite stacks drilling order can yield an efficient

metal chip ejection, as well as fast heat dissipation, since the

chip evacuation length is short. However, this drilling order can

cause a severe delamination for the composite. Hence, in this

optimization study the drilling sequence strategy was drilling

from stainless steel to GFRP.

Back propagation neural network (BPNN) as one of the

derived method of artificial neural network (ANN) has gained

popularity to model a complicated nonlinear problems like

machining operation and also predict the outputs. This method

can be utilized to predict the relationship among the machining

parameters (input) and their responses (output). Numerous

researches have been extensively using a non-conventional

meta-heuristic search-based technique such as genetic

algorithm (GA) for machining process optimization. Chatterjee

et al. [8] conducted an optimization of two responses (burr

height and circularity) in drilling of hole on AISI-304 stainless

steel. The optimization method applied was non-dominated

sorting genetic algorithm II (NSGA II). The varied drilling

parameters were spindle speed, feed rate, and drill bit diameter.

Applying GA for optimization and response surface

methodology (RSM) for analyzing the effects of drilling

parameters have been done by Kilickap and Huseyinoglu [9].

The drilled material was AISI 304 stainless steel and the

minimized response was burr height. The drilling parameters

and drill geometry analysed were cutting speed, feed rate and

point angle. Karnik et al. [10] integrated Taguchi principle,

artificial neural network (ANN) and genetic algorithm (GA)

optimization method to conduct minimization of burr

dimensions, i.e., burr height and burr thickness, in drilling of

AISI 316L stainless steel. The input parameters were drill

diameter, feed rate, and drill point angle. Mohan et al. [11]

performed a minimization of cost in drilling operation using

glass fibre reinforced polymer (GFRP) composite material. The

input parameters were cutting speed and feed rate, while the

optimization method was GA. A minimization of induced

delamination in drilling of GFRP composite was studied by

Kalita et al. [12] using the varied material thickness, drill

diameter, spindle speed, and feed rate. GA and particle swarm

optimization (PSO) techniques are then applied for predicting

the global optimum or minimum delamination factor.

The publication regarding the multi objective optimization of

drilling stainless steel GFRP stacks is not available yet. The

objective of this study is to determine the levels of drilling

parameters to minimize thrust force, torque, hole surface

roughness, and delamination of GFRP in drilling stainless steel-

GFRP stacks by applying BPNN based GA optimization

method.

II. BACK PROPAGATION NEURAL NETWORK BASED

GENETIC ALGORITHM OPTIMIZATION METHOD

BPNN is considered as one of the basic and commonly used

ANNs. Its architecture typically consists of one or more hidden

layers, an input layer, and an output layer. Each neuron is

connected to all the neurons of the next layer in most

applications. The neurons of hidden and output layers multiply

the incoming signal by weight and process the multiplication



result by using a transfer function. Some of the most commonly

used transfer functions are sigmoid and various hyperbolic

functions depending on the need. The BPNN model can

produce response predictions correctly if the BPNN network

architecture used produces the smallest mean square error

(MSE) value [13].

Genetic algorithm or GA is a search technique to discover

optimal solutions for complex problems. The technique was

developed by applying evolutionary biology concepts such as

genetic inheritance, natural selection, crossover (sexual

reproduction or recombination), and mutation. In this

algorithm, binary or decimal numbers which are considered as

string or chromosome, represent a point in the search space of

candidate solutions. A population of solutions is formed by a

set of chromosomes. A fitness value is then assigned to each

chromosome to show whether the desired objectives are

satisfied. The best solution(s) could be obtained from a

population by utilizing three fundamental operations:

1. Selection (selecting the best chromosome having high

fitness values as a parent chromosome).

2. Crossover (combining the multiple couple of parent

chromosomes to create the new chromosomes).

3. Mutation (conducting a random modification of parent

chromosomes).

In this study, BPNN was used to predict the drilling process

responses and also determine the objective function. This

objective function will be modified into a fitness function that

would be optimized using GA. Fig. 1 shows the steps of BPNN

and GA integration used in this study.

Fig. 1. Steps of BPNN based GA optimization method.

III. EXPERIMENTAL WORK

Stainless steel plate and glass fiber reinforced polymer

(GFRP) composite were stacked and used as the work piece

material. Stainless steel type 17-7 PH TH 1050 which

categorized as precipitation-hardening austenitic stainless steel

was used as stainless steel plate. The chemical composition of

the stacked 17-7 PH TH 1050 stainless steel is shown in Table

1. GFRP is made from fiberglass pre-preg BMS8-79 CL3

STYLE 1581 that cured using hot blanket method under 250°F

for 180 minutes. Hand lay-up method was used to fabricate the

TABLE I

Chemical composition of 17-7 PH Stainless steel

Wt.% C Mn P S Si Cr Ni Al

Min - - - - - 16.00 6.50 0.75

Max 0.09 1.00 0.040 0.030 1.00 18.00 7.75 1.50

TABLE II

Basic mechanical properties of stainless steel-GFRP specimen

Stainless steel 17-7 PH TH 1050 Fiberglass pre-preg BMS8-79 CL3

STYLE 1581

Ultimate Tensile

Strength 1379 MPa Tensile Strength 517.05 Mpa

0.2% Yield

Strength 290 MPa Tensile Modulus 23.37 Gpa

Elongation, % in

50.8 mm 8 mm

Compression

Strength 482.58 Mpa

Rockwell

Hardness C 43

Compression

Modulus 26.82 Gpa

TABLE III

Drilling parameters and their levels

Parameters Unit Level

1 2 3

Spindle speed (n) rpm 2000 2500 3000

Feed rate (Vf) mm/min 50 100 150

Drill point angle (PA) degree 90 118 135

(a)

(b)

(c)

Fig. 2. Drill point geometry for (a) 118º, (b) 130º, and (c) 140º.

specimen plates with fiber orientation for each layer is

[45/90/45/90/45/90/45/45/90/45/90/45/90/45]. These two

different materials were bonded using Loctite resin EA 943 NA.

The basic mechanical properties of stainless steel-GFRP are

summarized in Table 2. Moreover, the work piece material is

had a length of 200 mm and 30 mm of width, with a thickness

of 4 mm for GFRP and 1.2 mm for stainless steel.

A straight twist drill bit having two flutes and 8 mm of

diameter was used as the drilling bits. Three different drill point

angles were used in this study, i.e., 118º, 130º, and 140º as



Fig. 3. Thrust force and torque measurements using Kistler 9272

dynamometer during stainless steel-GFRP stacks drilling process

(a)

(b)

(c)

Fig. 4. Graph of (a) thrust force, (b) torque measurement and (c) drill bit

position during stainless steel-GFRP drilling process

shown in Fig. 2. All of these drill bits were made of HSS-cobalt.

Spindle speed, feed rate, and drill point angle each has three

levels and used as the drilling parameters. Based on the

literature related to the drilling process parameters applied in

composites, the range of cutting speed is 40 - 80 m/min and feed

rate is 0.05-0.15 mm/rev, while for stainless steel the range of

cutting speed is 15 - 60 m/min and feed rate is 0.10-0.2 mm/rev.

The drilling parameters used for drilling stainless steel-GFRP

stacks were selected to match the setting for stainless steel. The

varied levels of drilling parameters used in this experiment are

shown in Table 3. The drilling experiments were carried out on

Brother TC22AO CNC vertical machining center and the

experiments were run without using cutting fluid.

Kistler 9272 four-component piezoelectric dynamometer

was utilized to measuring thrust force (Fz) and torque (Mz), as

shown in Fig. 3. The results of measurement of thrust force and

torque using dynamometer during the cutting process can be

seen in the graph displayed by Dyno Wire software. Fig. 4 (a)

and (b) each shows a graph of the measurement of thrust force

and torque in the drilling process. Point 1 is the position where

the tool tip is on the surface of the stainless steel, and point 4 is

the position where the tool tip and cutting edge of the tool have

penetrated the workpiece (stainless steel-GFRP) as seen in Fig.

4 (c). Point 3 is the position where the tool tip and the cutting

edges penetrated GFRP composites already. The value of thrust

force and torque used in this study is the maximum thrust force

and torque produced on stainless steel drilling, i.e., at point 2.

Hence, thrust force and torque measurements are carried out in

the drilling process of stainless steel only.

Mitutoyo Surftest SJ-310 was utilized to measure the hole

surface roughness. The measurement of hole surface roughness

in this study was performed in the hole drilled in stainless steel.

Material properties can be altered incidently during

manufacturing processes. This phenomenon often causes the

emergence of residual stresses that can influence the fatigue life

of the material. Fasteners hole for joining process are

commonly made by using conventional drilling process. The

emerge of notches and rougher surfaces in the drilled hole

would create greater stress concentrations. They become the

fatigue crack initiation sites and a subsequent reduction in

fatigue life [14]. The rivet used in the assembly process will

penetrate the drilled hole at the stainless steel and then to the

drilled hole at the GFRP. The surface roughness of the holes in

the stainless steel must be kept minimum so that residual

stresses would not emerge. Therefore, in this study

minimization of hole surface roughness was performed for

stainless steel only.

The value of GFRP’s delamination was calculated by using

delamination factor. Delamination factor is a ratio between

maximum hole damage diameter (Dmax) in the delamination

area and the nominal diameter (D0) of the drill bit [15], or:

D = Dmax

D0

(1)

In this study, full factorial was selected as the design of

experiments. The running experiments were ordered to

maintain the experiment randomness. Replication was

conducted three times. The experimental results for all of the

responses are shown in Table 4.



TABLE IV

Result of experiments

No n Vf PA Fz Mz SR D No n Vf PA Fz Mz SR D

1 600 90 140 451.80 5.291 1.986 1.170 43 1500 120 130 355.20 3.261 1.961 1.129

2 2400 120 130 300.70 2.828 1.548 1.109 44 2400 90 118 188.50 1.911 1.352 1.070

3 1500 90 130 284.10 2.317 1.625 1.098 45 2400 60 140 214.40 1.101 1.042 1.073

4 1500 90 118 235.50 1.954 1.451 1.088 46 1500 120 140 430.60 3.157 1.925 1.147

5 1500 60 140 254.50 2.225 1.220 1.095 47 1500 60 130 224.10 1.855 1.342 1.081

6 2400 90 130 243.20 2.149 1.231 1.093 48 600 60 130 296.50 2.853 1.403 1.105

7 600 60 118 232.80 1.812 1.545 1.079 49 600 90 130 362.10 4.064 1.943 1.139

8 600 90 118 302.70 2.855 1.942 1.115 50 2400 90 140 286.60 1.571 1.291 1.099

9 1500 60 118 162.40 1.334 1.262 1.051 51 600 120 140 557.20 5.055 2.490 1.165

10 1500 120 118 290.40 2.571 1.926 1.108 52 600 60 140 339.40 2.444 1.456 1.114

11 1500 90 140 343.00 3.135 1.546 1.124 53 2400 120 140 385.80 2.143 1.635 1.143

12 600 120 118 350.30 3.280 2.595 1.124 54 2400 60 118 109.40 1.551 1.144 1.050

13 600 120 130 433.40 5.067 2.364 1.166 55 600 90 140 451.30 3.681 1.957 1.169

14 2400 60 130 175.50 1.416 1.080 1.054 56 2400 120 130 298.60 2.328 1.554 1.115

15 2400 120 118 250.90 2.195 1.535 1.095 57 1500 90 130 280.00 2.597 1.631 1.096

16 1500 120 130 350.90 3.591 1.935 1.136 58 1500 90 118 234.10 2.824 1.487 1.083

17 2400 90 118 190.80 1.587 1.358 1.068 59 1500 60 140 251.00 1.475 1.192 1.089

18 2400 60 140 209.80 1.597 1.011 1.073 60 2400 90 130 241.40 1.687 1.267 1.086

19 1500 120 140 431.70 4.492 1.986 1.165 61 600 60 118 228.90 3.175 1.523 1.081

20 1500 60 130 225.80 1.631 1.313 1.074 62 600 90 118 305.90 4.482 1.921 1.119

21 600 60 130 293.10 2.743 1.414 1.109 63 1500 60 118 158.60 2.198 1.286 1.055

22 600 90 130 365.60 3.615 1.983 1.136 64 1500 120 118 293.90 3.597 1.910 1.102

23 2400 90 140 289.90 2.481 1.281 1.099 65 1500 90 140 346.70 2.255 1.589 1.129

24 600 120 140 555.90 5.620 2.501 1.189 66 600 120 118 353.90 5.632 2.546 1.143

25 600 60 140 342.10 3.114 1.417 1.123 67 600 120 130 438.60 5.287 2.387 1.158

26 2400 120 140 383.70 4.080 1.605 1.150 68 2400 60 130 176.20 1.320 1.105 1.066

27 2400 60 118 109.20 1.065 1.113 1.050 69 2400 120 118 250.90 2.765 1.598 1.088

28 600 90 140 448.50 3.625 1.935 1.161 70 1500 120 130 351.30 3.297 1.910 1.134

29 2400 120 130 303.40 2.375 1.535 1.111 71 2400 90 118 193.40 1.920 1.343 1.073

30 1500 90 130 279.20 2.545 1.665 1.096 72 2400 60 140 213.20 1.091 1.022 1.080

31 1500 90 118 230.20 2.895 1.423 1.084 73 1500 120 140 434.50 3.145 1.935 1.153

32 1500 60 140 249.50 1.448 1.186 1.088 74 1500 60 130 220.40 1.825 1.356 1.081

33 2400 90 130 240.00 1.678 1.223 1.088 75 600 60 130 294.20 2.812 1.434 1.110

34 600 60 118 234.30 3.187 1.587 1.085 76 600 90 130 367.80 4.092 1.958 1.150

35 600 90 118 307.10 4.414 1.907 1.112 77 2400 90 140 287.10 1.564 1.315 1.101

37 1500 120 118 289.20 3.612 1.953 1.105 78 600 120 140 555.10 5.025 2.521 1.178

38 1500 90 140 348.80 2.212 1.602 1.118 79 600 60 140 344.10 2.400 1.411 1.128

39 600 120 118 355.60 5.666 2.534 1.131 80 2400 120 140 386.80 2.124 1.598 1.151

40 600 120 130 437.20 5.211 2.402 1.159 81 2400 60 118 110.60 1.611 1.148 1.050

41 2400 60 130 171.70 1.311 1.087 1.063

42 2400 120 118 252.80 2.734 1.579 1.093



IV. OPTIMIZATION PROCESSES

A. BPNN modelling

Input data of BPNN is a process parameters of the drilling,

such as n Vf, and PA. The responses measurement is used as the

output, which is the data of Fz, Mz, SR, and D. 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.

1) BPNN modelling

Input and output data should be normalized prior to BPNN

modeling. Normalization of the input and out data is important

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 by

using a mapminmax function on Matlab R2015.a, and the used

equation is:

pn =2(p−min(p))

(max(p)−min(p))− 1, (2)

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.

Fig. 5. BPNN architecture

Fig. 6. Correlation graphs of BPNN

Fig. 7. Comparison between experimental data and BPNN data prediction



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 respectively. 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 trial and error method show that two hidden layers with

13 neurons each are needed and producing 0.0361 MSE value,

which is the minimum MSE. Therefore, the configuration of

BPNN network architecture in this study was 3-13-13-4, as seen

in Fig. 5. 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

values 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.

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 of data for training and fifteen percent of data each for

testing and validating respectively.

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 0.97926, 0.94041, 0.95105, and 0.97223 as

shown in Fig. 7. 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. 6. 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 [16]. A similar method for analyzing the correlations

among the output and target values in term of training, testing,

and all data was also conducted by Kumar and Sait [17] and

Soepangkat et al. [13].

B. Optimization using GA

The general steps of optimization using GA are as follows:

• Definition of the fitness function.

• Determination of GA parameters.

• Optimization using GA and confirmation experiment.

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.

Objective function can be used as fitness function directly, or

modified little bit. On the first iteration a number of potential

solutions will be randomly generated, and these potential

solutions will be assessed by utilizing a fitness function [18]. In

this research, those function was developed by combining four

objective functions, where each objective function belonged to

Fz, Mz, SR, and D. Activation function utilized tansig

(hyperbolic tangent sigmoid) transfer 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 [19].

𝑂𝑏𝑗𝑙 = (∑ 𝑤𝑘𝑙 . ((2

1+𝑒−2z𝑧𝑘) − 1)13

𝑙=1 ) + 𝑤0𝑙, (3)

𝑧𝑧𝑘 = (∑ 𝑣𝑗𝑘 . ((2

1+𝑒−2𝑧𝑗

) − 1)13𝑘=1 ) + 𝑣0𝑘, (4)

𝑧𝑗 = (∑ (𝑢𝑖𝑗 . 𝑥𝑖)13𝑗=1 ) + 𝑢0𝑗 , (5)

where:

i = number of drilling parameters.

j = number of neurons in first hidden layer.

TABLE V

Lower and upper limit interval of stainless steel-GFRP drilling process

parameters

Drilling Parameters Unit Limit

Lower Upper

Spindle speed (n) rpm 600 2400

Feed rate (Vf) mm/min 60 120

Drill point angle (PA) - 118 140

TABLE VI

Comparison between BPNN-GA prediction and confirmation experiments

Drilling Parameters Fz (N) Mz (Nm)

n Vf PA pred./exp. Error

(%) pred./exp.

Error

(%)

2343 61 118 215.69/208.25 3.45 1.367/1.310 4.17

Drilling Parameters SR (µm) D

n Vf PA pred./exp. Error

(%) pred./exp.

Error

(%)

2343 61 118 1.178/1.126 4.41 1.097/1.087 1.82

(a)

(b)

Fig. 8. Delamination defects of GFRP observed (a) before and (b) after

optimization



Fig. 9. Resonse graph for (a) thrust force, (b) torque, (c) hole surface roundness, and (d) delamination

k = number of neurons in second hidden layer.

l = number of drilling responses.

𝑂𝑏𝑗𝑙 = objective function, i.e., thrust force, torque, hole surface

roughness, and delamination.

𝑧𝑗 = activation value for each neuron on first hidden layer.

𝑧𝑧𝑘 = activation value for each neuron on second hidden layer.

𝑢𝑖𝑗 = weight value from input layer to first hidden layer.

𝑣𝑗𝑘 = weight value from first hidden layer to second hidden

layer.

𝑤𝑘𝑙 = weight value from second hidden layer to output layer.

𝑢0𝑗 = bias value from the input layer to first hidden layer.

𝑣0𝑘 = bias value from first hidden layer to second hidden layer.

𝑤0𝑙 = bias value from second hidden layer to output layer.

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:

𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓(𝑥) = 𝑂𝑏𝑗1 + 𝑂𝑏𝑗2 + 𝑂𝑏𝑗2 + 𝑂𝑏𝑗4 ,, (7)

where:

𝑂𝑏𝑗1 = Objective function of thrust force.

𝑂𝑏𝑗2 = Objective function of torque.

𝑂𝑏𝑗3 = Objective function of hole surface roughness.

𝑂𝑏𝑗4 = Objective function of delamination.

2) Determination of GA parameters

The GA parameters should be determined properly in order

to achieve the optimum value of responses. The names and

values of GA parameters used in the optimization such as

population size, generation size, selection method, crossover

method, crossover probability, mutation method, and mutation

probability in succession are 100, 100, roulette wheel, two

point, 0.8, uniform, and 0.05.

The lower and upper limit values of the parameters of the

stainless steel-GFRP drilling process showed in Table 5.

3) The result of optimization using GA and confirmation experiment

By solving multi-performance optimization utilizing GA

method, the optimum Fz, Mz, SR, and D could be attained by

combining spindle speed of 2343 rpm and feed rate of 61

mm/min with 118º drill point angle. This process parameters

setting is then used as an input to predict the responses values

using BPNN. The comparison between the predicted Fz, Mz,

SR, and D using BPNN and confirmation experiments are

shown in Table 6. The confirmation experiments using

optimum drilling parameter setting are replicated five times,

and the averages value are shown in Table 6. The values of Fz,

Mz, SR, and D from the results of confirmation experiments are

lower than the prediction by BPNN-GA. Table 6 shows that the

errors between the results of BPNN-GA prediction and



confirmation experiment do not exceed 5% for Fz, Mz, SR, and

D (drilling responses), which mean that BPNN based GA

optimization technique is effective and adequate due to all of

the relative errors are less than or equal to 5%. Therefore, the

minimum values for Fz, Mz, SR, and D could be achieved by

using the parameters of drilling which were obtained by

conducting optimization based on the combination of BPNN

and GA methods. The similar method was also used by

Soepangkat et al. [20] to optimize the tool flank wear, surface

roughness, and metal removal rate in end milling process of

ASSAB XW 42 using cryogenic coolant.

Fig. 8 shows the microscopic delamination photos in hole

exit of GFRP before optimization using spindle speed at 600

rpm, feed rate at 120 mm/min, and 140˚ drill point angle. The

minimum delamination in the hole exit was attained by

combining 118˚ drill point angle, with spindle and feed rate at

2343 rpm and 61 mm/min respectively. The optimum setting

would decrease the delamination as much as 8.4%.

V. EFFECT OF THE DRILLING PROCESS PARAMETERS

The four responses, i.e., thrust force, torque, surface

roughness, and delamination are summarized in Fig. 9. As

shown in Fig. 9 (a), the increasing spindle speed in drilling

stainless steel was found to decrease the thrust force, which is

in accordance with the experimental results of Meral et al. [21],

Neseli [22], and also to what was stated by Armarego [23]. On

the other hand, increasing the feed rate would increase the chip

cross section and hence raising up the thrust force [24]. The feed

rate is the most significant factor affecting the thrust force. The

increase of point angle would increase thrust force slightly,

which is in good agreement to what was stated by Armarego

[23] and also by Stephenson and Agapiou [25].

Fig. 9 (b) shows that torque in drilling stainless steel decrease

with the increase of spindle speed and point angle, but decrease

with the increase of feed rate. Similar to thrust force, an increase

in spindle speed would decrease the torque. This happens

because the high temperatures occurring in the cutting zone

reduce the yield strength of the stainless steel, which is also

stated by Meral et al. [21]. The increase of point angle would

decrease torque slightly, which can also be found in Armarego

[23] and Stephenson and Agapiou [25]. According to Neseli

[22], the cutting speed contributes the most to the torque in

drilling steel.

As can be seen in Fig. 9 (c), the surface roughness value of

stainless steel decreases with increasing cutting speed, and there

would be an increase with increasing feed rate value. This

phenomenon is parallel to the literature which states that the

increase in cutting speed up to a certain point affects the surface

quality positively [26]. The increasing point angle would

decrease surface roughness due to less chip removal area, in the

tool per revolution. This phenomenon was also reported by

Demir [27].

Fig. 9 (d) depicts that hole delamination in GFRP drilling

could be minimized by applying high spindle speed, low feed

rate and low degree of point angle. It can be seen that the

increase of spindle speed would decrease hole exit

delamination. The increase of the spindle speed would decrease

thrust force during the drilling process. Lower thrust force

would produce smaller hole exit delamination. Hence, the

increase of spindle speed would decrease hole exit

delamination. These phenomena are in agreement with

Armarego [23] and Azmi [28].

Fig. 9 (d) also indicates that the decreasing of feed rate would

decrease the hole exit delamination during GFRP drilling

process. The decreasing of feed rate would decrease the thrust

force during the drilling process. Lower thrust force would

decrease the hole exit delamination. Therefore, the decreasing

of feed rate would decrease hole exit delamination. This

phenomenon was also stated by Rochim [29]. Moreover, the

same figure also shows that hole exit delamination increased

with increasing point angle. This result was also mentioned by

Azmi [28] and Kilickap [30].

VI. CONCLUSION

This study introduces an effective multi-performance

optimization technique that combines back propagation neural

network (BPNN) and genetic algorithm (GA) on stainless steel-

GFRP stacks drilling process. The experimental works come up

with the following concluding remarks:

The results of process analysis depict that increasing feeding

speed increases thrust force and delamination considerably,

but increase surface roughness moderately compared to the

increase of spindle speed and point angle. On the other hand,

torque would increase greatly with the increasing of spindle

speed compared to the increase of feeding speed and point

angle. The effect of point angle on the four responses is the

smallest.

BPNN has been utilized to model the responses such as

thrust, torque, surface roughness, and hole exit

delamination. Various BPNN architectures have been

evaluated and 3-13-13-4 configuration (two hidden layer

with thirteen neurons each, three neurons on the input and

four neurons on the output layer) is obtained as the best

architecture. The MSE training of this optimum network

architecture was 0.0361. Trainlm, tansig, and purelin were

the activation functions used on BPNN training, hidden

layer, and output layer subsequently.

BPNN has predicted the minimum thrust force, torque,

surface roughness, and hole exit delamination (Fz, Mz, SR,

and D) successfully after properly trained since the average

error produced are less than 10%.

The minimum thrust force, torque, surface roughness, and

hole exit delamination obtained by utilizing a BPNN based

GA optimization method for the input parameters

investigated. The four responses can be minimized

simultaneously by the usage of high spindle speed of 2343

rpm, low feeding speed of 61 mm/min, and low drill point

angle of 118°.

BPNN based GA optimization method is effective and

acceptable due to all of the relative errors between

prediction and experiments confirmation are not more than

5%.



ACKNOWLEDGEMENT

The authors would acknowledge the BP PTNBH 2017

research grant from Institut Teknologi Sepuluh Nopember,

Surabaya-Indonesia.

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