Obtaining Optimum Cutting Parameters of Carbon Fiber Reinforced Polymer Composite (CFRP) Material by Using GA and SA Methods during Drilling

doi:10.21203/rs.3.rs-3797971/v1

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Obtaining Optimum Cutting Parameters of Carbon Fiber Reinforced Polymer Composite (CFRP) Material by Using GA and SA Methods during Drilling

https://doi.org/10.21203/rs.3.rs-3797971/v1

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This paper presented a new comprehensive approach to select cutting parameters for surface roughness in drilling of carbonfiber reinforced polymer composite (CFRP) material. The influence of drilling on surface quality of woven CFRP materials was investigated experimentally. The CFRP material (0/90° fiber orientation) was drilled at different cutting parameters and the surface roughness of the hole was measured. Drilling tests were carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds; 2, 3 and 4 flute numbers and 0.2, 0.3, and 0.4 mm/rev feed rates. Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. According to the results of the experimental study and optimization technique, optimum cutting parameters were obtained and started to be changed in order to have the best surface quality.

CFRP

drilling

surface roughness

simulated annealing

genetic algorithm

Carbonfiber reinforced plastic composite (CFRP) materials have many desirable properties, including high strength-to-weight ratio, high stiffness-to-weight ratio, high corrosion resistance, and low thermal expansion. These properties make CFRP suitable for usage in structural components in aerospace applications. Drilling is the most common machining process applied to CFRP laminates, and it is difficult due to the extremely abrasive nature of the carbon fibers and low thermal conductivity of CFRP. It is a challenge for manufacturers to drill CFRP materials without causing any delamination on the work part while considering the economics of the process. The subject of this study is drilling of fabric woven type CFRP laminates which are known to be more resistant to delamination than unidirectional type CFRP laminates. The objective of this study is to investigate the influence of cutting parameters on surface roughness of CFRP material.

Zu et.al. [1] investigated the drilling of CFRP. They improved a method for optimizing the CFRP drilling process, reduce the defects in the drilling process, improve the quality of drilling holes, and provide reference for drilling process of other composite materials. Isik [2] demonstrated research results on the machining of unidirectional glass fiber reinforced composite (UD-GFRP) and advised optimum cutting parameters to obtain better surface quality. Arul et al. [3] studied on optimization of machining of GFRP material. They analyzed data on the thrust force, torque, and tool life by using a group method data handling algorithm. Also, Palanikumar [4] worked on finding optimum cutting parameters for surface roughness using Taguchi’s method. He said the advantages of using Taguchi’s method. He expressed the advantages of Taguchi’s method within this study. It submits a basic and systematic approach to optimize the design. By applying this technique, one can drastically decrease the number of experiments and required time for experimentation. He also with his coworkers [5] investigated optimum machining parameters of GFRP materials. They elaborated the problem as a multiple performance optimization problem and this is the only study that multiobjective optimization is considered in machining of GFRP materials up to now. Ogawa et al. [6] addressed the relation between the cutting force and the surface roughness of a drilled hole wall in small-diameter drilling of GFRP. An et al. [7] investigated the machinability of GFRP whereby tool materials and geometries. By proper selection of cutting geometries, they accomplished perfect machining of the workpiece. Aoyama et al. [8] investigated the drilling of GFRP. Aoyama et al. [8] studied on drilling of GFRP. They refer that the fiber bundle thickness of glass cloth and the relative angle between cutting direction and fiber existed as the factors which influences damage width around the drilled hole. Khasbaba [9] investigated the influence of drilling and material variables on thrust force, torque, and delamination of GFRP composites experimentally. He implied that the presence of sand filler in continuous-winding composites not only raised the values of cutting forces and push-out delamination but also increased their values with increasing cutting speed. Capello [10] analyzed the differences in delamination mechanisms when drilling of GFRP with and without a support placed under the workpiece. Zhong et al. [11] investigated drilling of CFRP/Al/CFRP co-cured materials. The experiments were taken to prove the effect of the TiAlN coating on the thrust force of the drill bit. Finally, the results show that it helps reduce the maximum thrust force, and improving hole edge quality, surface morphology and roughness during the drilling process of CFRP/Al/CFRP co-cured materials. Davim et al. [12] aimed at studying of the cutting parameters (cutting velocity and feed rate) under specific cutting pressure, thrust force, damage, and surface roughness in GFRPs. Davim et al. [13] investigated the influence of cutting parameters (vc and f) and the matrix under the specific cutting force (kc), delamination factor (Fd), and surface roughness (Ra) on two FRPs. Singh and Bhatnagar [14] investigated the influence of drilling-induced damage on the residual tensile strength of the unidirectional glass fiber-reinforced plastic composite (UD-GFRP) laminates with drilled holes for a variety of solid carbide drillpoint geometries under cutting conditions, varying speed, and feed. Singh and Bhatnagar [15] presented one such attempt to quantify the drilling-induced damage and to correlate it with different drill-point geometries and the drilling process parameters for four-layered UD-GFRP laminates. Arul et al. [16] carried out experimental study on the drilling of GFRP using 6-mm diameter high-speed steel (HSS), TiN coated HSS, and tipped tungsten carbide drills. Velayudham and Krishnamurthy [17] studied the influence of point geometry on thrust and delamination. Drilling tests were carried out for GFRP using carbide drills with different point geometries. Khashaba et al. [18] investigated the effects of the drilling parameters, speed, and feed on the required cutting forces and torques in drilling chopped composites with different fiber volume fractions. Mohan et al. [19] outlined a methodology to optimize process parameters in drilling of GFRP for minimum delamination damage. Abrao et al. [20] focused on the investigation of the effect of the cutting tool geometry and material on the thrust force and delamination produced when drilling a glass fiber-reinforced epoxy composite. Singh et al. [21] investigated drilling of UD-GFRP.

2.1. Material

In the experiments, a single hopping (plain) mesh oriented carbon fiber-reinforced composite materials produced by POLITEK Co. (Turkiye) are used. 95% epoxy resin fiber composite material mixing ratio of 5%, 10 mm thick (30 layers) were prepared in the form of plates. Plate size 200x100x10 mm with CFRP produced single hopping (plain) has a mesh structure (Fig. 1). Mechanical properties of the CFRP plate are shown in Table 1.

Table 1

Mechanical Properties of The CFRP material
Properties	Values
Resin Type	Epoxy E201(hot cured)
Laminate Intensity [g/cm³]	1.55
Fiber Rate [%]	55–60
Tensile Strength [MPa]	760
Elasticity Module [GPa]	70
Bending Strength [MPa]	780
Shear Module [GPa]	55
Transition Temperature of Resin [^oC]	140
Thickness of Laminate Floor [mm]	0.63

2.2. Cutting tools

During the experimental work that has been manufactured by 8 mm coated carbide cutting tools are used with different flute numbers (Fig. 2).

2.3. Cutting Conditions

In the experiments, different cutting parameters have been used such as cutting speed, feed, tip angle, and flute number. The cutting parameters are given in Table 2.

Table 2

The cutting parameters
Cutting Speed, Vc Vc [m/min.]	Feed, f [mm/rev.]	Tip angle, Ψ	Flute number
50	0.2	60	2
70	0.3	90	3
90	0.4	120	4

An CNC milling machine (TMC 500V) for the experiments was used. Countertop installation specifications required depending on the rigidity, vibration and balance was founded in. The experimental setup is shown in Fig. 3.

The methodology of this study composed of two steps. These steps are generation of network files predicting the surface roughness values and finding the optimum surface machining parameter values yielding the minimum surface roughness value. These steps were explained below in a detailed manner. Also, the proposed methodology was shown in Fig. 4.

3.1. Generation of Network Files

A network files predicting the surface roughness values belonged to the plain carbon fiber reinforced polymer reinforced polymer was generated in this step by using the measurements data obtained in previous step. The generation of network files step composed of three substeps as generation of input file, generation of target file and generation of network file.

3.1.1. Generation of Input File

Input file is composed of the parameters of surface machining process. As it mentioned in measuring the surface roughness values step, 81 measurements were carried out. Also, four parameters were used for surface machining of the plain carbon fiber reinforced polymer composite. In that case, an input file having 81 rows and 4 columns was generated for the plain carbon fiber reinforced polymer composite.

3.1.2. Generation of Target File

Target file is composed of the results of surface roughness measurements. It was mentioned that 81 measurements were carried out and just one result was obtained for each measurement. Hence, an input file having 81 rows and 4 columns was generated for the plain carbon fiber reinforced polymer.

3.1.3. Generation of Network File

After the input files and the output files were generated, the network file was generated for the plain carbon fiber reinforced polymer. These network files were generated by using nftool command in MATLAB software. A two layer feed forward neural network was used in order to obtain approximate function of structural analyses. The network was trained with Levenberg-Marquardt backpropagation algorithm (trainlm), unless there is not enough memory, in which case scaled conjugate gradient back propagation (trainscg) was used. The parameters used in the generation of neural network files were shown in Table 3.

Table 3

Parameters used in the generation of neural network files
Parameters	Value
Number of Measurement	81
Number of Parameters	4
Number of Parameter Level	3

3.2. Finding the Optimum Parameters Value for Surface Machining

This is the final step of the proposed methodology. In this step, the network files generated in previous step were used with two different optimization algorithms in order to obtain the optimum parameters value for surface machining process. These algorithms are Genetic Algorithm (GA) and Simulated Annealing (SA). Since two algorithms were used to get the optimum parameters, some information was given about these algorithms.

3.2.1 Optimization:

Optimization may be defined as the process of maximizing or minimizing a desired objective function while satisfying the prevailing constraints [10]. An optimization problem can be stated as follows:

Min F(x)

gj(x) ≤ 0, j = 1, 2, ..., m (1)

hi(x) = 0, i = 1, 2, ..., n (2)

x={x1, x2, ….., xp} (3)

It is to find the values of x={x1, x2, ….., xp} minimizing the function of F(x) while dealing with the constraints of gj (x) ≤ 0, j = 1, 2, ..., m and hi (x) = 0, i = 1, 2, ..., n. In Eq. 2.1, F (x) is an objective function. gj (x), hi (x) are inequality and equality constraints, respectively. x={x1, x2, ….., xp} is set of design parameters being optimum values of the optimization problem.

3.2.1.1 Simulated Annealing:

The Simulated Annealing optimization algorithm is inspired of the annealing process. It aims to find global optimum point simulating the annealing process. The explanations regarding Simulated Annealing below were taken from the book of Singiresu S. Rao. [22] “The simulated annealing is an optimization method based on annealing process. Annealing process is heating any material or metal up to annealing temperature then cooling slowly. This process is applied in order to soften the material, optimize the internal structure of the material and eliminate the internal stresses. The simulated annealing method simulates the process of slow cooling of molten metal to achieve the minimum function value in a minimization problem.” Details can be found in Engineering Optimization: Theory and Practice [22]. Modified versions of simulated annealing have been used successfully in different types of research areas by Kirkpatrick et al. (1983) [23], Wong et al. (1988) [24], Drexl (1988) [25] Wasserman and Schwartz (1988) [26], Telly et al. (1987) [27] and Carnevali et al. (1985) [28].

3.2.1.2 Genetic Algorithm:

The Genetic Algorithm, which is based on the Darwin’s Evolution Theory, was invented by John H. Holland. [29] However, until Goldberg, who is student of Holland, published a book regarding GA named Genetic Algorithms in Search, Optimization, and Machine Learning; it had not been a popular optimization method. [30] Because of the increasing competition in engineering and the contribution of the increasing computer technology the GA became more popular optimization. The advantages of the GA compared to the traditional optimization methods are as follow:

The GA uses a population of points for starting phase in place of single point. Thus, the possibility to be stuck into a local optimum is minimized.

In GA it is not necessary to have continuous objective function. It can be applied to the optimization problems having continuous or discrete variables.

In GA, better design vectors are tried to be obtained via the probabilistic GA operators. In each iteration, GA tries to find new design vectors having better fitness value. In spite of these operators work randomly, they use the information based on the fitness values.

As it mentioned above, GA is based on Evolution Theory. So it uses the principles of the Evolution Theory like Reproduction, Crossover and Mutation. The GA uses these principles as probabilistic tools in order to get better individuals the same as in the Evolution Theory. More information can be provided from the book of Rao [22].

3.2.2. Finding the Optimum Parameters Value for Surface machining with GA

There are several studies combining the ANN and GA in literature but any application to find out optimum cutting parameter has not been seen. For detailed information of the used methodology, study of Salajegheh et. al. [31] can be examined.

In this study, there are four parameters in the optimization of surface roughness belonged to the plain carbon fiber reinforced polymer. These parameters are feed rate, cutting speed, tip angle and flute number. The upper bounds and the lower bounds of these parameters were shown in Table 4.

Table 4

Upper and Lower Bounds for the Surface machining Parameters
Parameters	Upper Bound	Lower Bound
Feed	0.4 mm/rev.	0.2 mm/rev.
Cutting Speed	90 m/min.	50 m/min.
Tip Angle	\({120}^{^\circ }\)	\({60}^{^\circ }\)
Flute Number	4	2

To find the optimum surface machining parameters GA and the network file generated for predicting the surface roughness value for plain material were used together. The network file was used as an objective function in GA and the surface machining parameters yielding the minimum surface roughness value were found.

3.2.3. Finding the Optimum Parameters Value for Surface machining with SA

There are lots of applications of SA in optimization studies or of ANN in modeling in literature but only very few studies combining SA and ANN are found. For detailed information, the study combining ANN and SA [32] can be viewed.

The parameters used in the optimization with SA are same as the parameters used in the optimization with GA. So the parameters and their upper and lower bounds can be seen in Table 4. The network file generated for predicting the surface roughness value for plain material and SA were used together in order to find the optimum surface machining parameters. The network file was used as an objective function in SA as it used in GA and the surface machining parameters yielding the minimum surface roughness value were found.

The results obtained within the study are given in two main titles as experimental results and numerical results.

4.1. Experimental Results

The experimental results are shown in Figs. 5, 6 and 7.

In Fig. 5–7, according to the results of surface roughness decreases with increasing number of mouths and four large mouth best results in the team achieve high cutting speeds and low values that we have seen progress. Considered by the open end of the nozzle angle increases small increases in surface roughness is observed but with a low angle of 60^o is observed that the best results are obtained.

4.2. Numerical Results

4.2.1. Results for Network Files:

It was mentioned that a network file was generated in order to predict the surface roughness value for the plain carbon fiber reinforced polymer. This network file named “netplain” was generated by using the correlation between the surface machining parameters and the surface roughness value obtained in 81 measurements. Mean error value and maximum error value for the network file were shown in Table 5.

Table 5

Maximum and Mean Error for the Network File
Network File	Max. Error (%)	Mean. Error (%)
netplain	4.1714	0.1792

Figure 8 shows the correlation between the predicted and experimental values for training and testing set. As it seen in Table 5 and Fig. 8, it can be said that the ANN results are very successful. The mean error and the maximum error belonged to the training and the testing patterns of the network files are 4.1714 and 0.1792, respectively.

4.2.2. Optimum Parameter Results:

It was stated that optimum surface machining parameters and minimum surface roughness value for the plain material with GA and SA respectively. These results were shown is Table 6 and Table 7 below.

Table 6

Minimum Surface Roughness Value Found by GA and SA
Minimum Surface Roughness Value	GA	SA
Minimum Surface Roughness for plain CFRP	0.8746	0.8705

According to Table 6, it can be said that GA gave better results compared to SA. However, the difference between the minimum surface roughness values of GA and SA is not significant. Hence, both optimization algorithms can be preferred for the proposed method.

Table 7

Optimum surface machining parameters found by GA and SA
Parameters	GA	SA
Feed rate for plain CFRP	0.2011	0.2000
Cutting speed for plain CFRP	89.9983	89.9996
Tip angle for plain CFRP	119.9998	119.9977
Flute number for plain CFRP	4	4

As such in Table 6, the optimum parameter values for GA and SA are so close in Table 7. This observation is strengthened the thought saying that both optimization algorithms can be preferred for the proposed method.

For one-hopping (plain) and polymer matrix composites;

Increase of cutting speed reduces surface roughness values in the hole.
Surface roughness values in the hole drops when increasing number of mouth.
Surface roughness values of increasing have seen as increasing of progress in the hole.
In case of increase of the angle of the tip reduced the surface roughness values.

When these results are taken into consideration while;

If cutting speed is low and progression is rise, roughness values in all of our experiments is high.
If surface roughness values is low, the number of mouth must be 4, progressing must be low and cutting speed must be high.
Four-bladed tool with cutting speed of 90 m/min, feed 0.2 mm / 120° angle of giant and cutting tools should be selected.

In general, the following conclusions were reached as a result of the experimental study;

If progress is increase, surface roughness is increase.
If cutting speed is increase, surface roughness is reduced
As number of mouth is increase, the surface roughness is decrease
Cutting headsets tip angle is increase, the surface roughness is reduced.

Within this study, drilling of CFRP is carried out using feed rate, cutting speed, tip angle and flute number as cutting parameters. As an output, surface roughness value is obtained;

The cutting parameters are optimized in order to get good quality surface roughness values in drilling of CFRP.
To predict the surface roughness value a network named “netplain” was generated. The prediction ability of the network is very high as it can be understood from its error values which are 4.1714 and 0.1792 are about the maximum and the mean error values, respectively.
For the optimization of drilling process, hybrid method of ANN-GA and hybrid method of ANN-SA were used. The optimum results obtained by ANN-GA and ANN-SA are as follow, respectively; 0.8746 and 0.8705. These results are so close each other.
Optimum results for ANN-GA are 0.2011 as feed rate, 89.9983 as cutting speed, 119.9998 as tip angle and 4 as flute number. On the other hand, optimum results for ANN-SA are 0.2000 as feed rate, 89.9996 as cutting speed, 119.9977 as tip angle and 4 as flute number. The optimum cutting parameters obtained via ANN-GA and ANN-SA are so close as well.
In conclusion, the hybrid optimization methods proposed within this study has given successful results. It is observed that production of good quality holes in drilling of CFRP can be achieved using the proposed methodology.

Author contribution M.S.G.: conceptualization, methodology, formal analysis, investigation, writing-original draft, writing-review and editing, visualization, funding acquisition B.I.: methodology, investigation, software, writing-original draft, writing-review and editing, visualization. M.B.J.: conceptualization, resources, review and editing, supervision

Funding This study has been supported by Scientific Research Project, University of Karabuk (Project Number: KBU-BAP-11/2-YL-035). We also would like to thank Marmara University, Faculty of Engineering, Department of Mechanical Engineering for their contributions.

Data availability The raw data required to reproduce the fndings of this work are available from the corresponding author upon reasonable request.

Competing interests The authors declare no competing interests.

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Obtaining Optimum Cutting Parameters of Carbon Fiber Reinforced Polymer Composite (CFRP) Material by Using GA and SA Methods during Drilling

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Version 1

Abstract

Figures

1. Introduction

2. Experimental Study

2.1. Material

2.2. Cutting tools

2.3. Cutting Conditions

3. Methodology

3.1. Generation of Network Files

3.1.1. Generation of Input File

3.1.2. Generation of Target File

3.1.3. Generation of Network File

3.2. Finding the Optimum Parameters Value for Surface Machining

3.2.1 Optimization:

3.2.1.1 Simulated Annealing:

3.2.1.2 Genetic Algorithm:

3.2.2. Finding the Optimum Parameters Value for Surface machining with GA

3.2.3. Finding the Optimum Parameters Value for Surface machining with SA

4. Results and Discussion

4.1. Experimental Results

4.2. Numerical Results

4.2.1. Results for Network Files:

4.2.2. Optimum Parameter Results:

5. Conclusion

Declarations

References

Status:

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