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Article

Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes

by
Birhan Isik
1,*,
Mehmet Sah Gultekin
2,
Ismail Fidan
3,* and
Martin Byung-Guk Jun
1
1
Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Vocational School, Siirt University, Siirt 56100, Türkiye
3
Manufacturing and Engineering Technology, Tennessee Tech University, Cookeville, TN 38505, USA
*
Authors to whom correspondence should be addressed.
Processes 2024, 12(7), 1477; https://doi.org/10.3390/pr12071477
Submission received: 2 June 2024 / Revised: 12 July 2024 / Accepted: 12 July 2024 / Published: 15 July 2024
(This article belongs to the Section Manufacturing Processes and Systems)
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Versions Notes

Abstract

:
This paper presents a unique approach to generate a number of cutting knowledge blocks for the surface roughness analysis of the drilling process for carbon-fiber-reinforced polymer composite (CFRP) materials. The influence of drilling on the 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. A set of tests was 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. The Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. Based on the experimental findings and optimization techniques applied, optimal cutting parameters were derived, which were subsequently adjusted to enhance surface quality. Overall, the cutting parameters are carefully optimized to achieve good surface roughness quality in the drilling of CFRP.

1. Introduction

CFRP materials have many desirable properties, including high strength-to-weight ratio, high stiffness-to-weight ratio, high corrosion resistance, and low thermal expansion [1]. The inherent properties of CFRP materials render them well-suited for integration into structural components within the aerospace industry. Drilling is one of the most common machining processes applied to CFRP laminates, and it is a difficult manufacturing process due to the extremely abrasive nature of the carbon fibers and the low thermal conductivity of CFRP [2]. It is a challenge for the manufacturers to drill CFRP materials without causing any delamination on the workpiece. The drilling of CFRP materials presents a multifaceted challenge due to the mixed nature of the material composition, characterized by the presence of carbon fibers embedded within a polymer matrix in a complex paradigm. It has been made clear by several studies that the complex nature of these composites demands accurate machining settings to achieve high-quality outcomes.
Zu et al. [3] investigated the drilling process of CFRP. They enhanced a method for optimizing the CFRP drilling process, minimizing defects, and ultimately elevating the surface finish quality of drilling operations. Isik [4] demonstrated research findings on the machining of unidirectional-glass-fiber-reinforced polymer (UD-GFRP) composite and generated optimum cutting parameters for obtaining better surface qualities. Arul et al. [5] studied on optimization of the machining of GFRP material. They analyzed a high number of tests on the thrust force, torque, and tool life by using a group method data handling algorithm. Also, Palanikumar [6] worked on finding optimum cutting parameters for surface roughness using Taguchi’s method. He presented the advantages of using Taguchi’s method in his research. This research proposes a forthright and systematic approach to optimize design, significantly reducing both the number of experiments and experimentation time required. This research also investigated the optimum machining parameters of GFRP materials [7]. They elaborated the problem as a multiple performance optimization problem, and, to date, this was the only study in which multi-objective optimization was considered in the machining of GFRP materials. Ogawa et al. [8] explored how cutting force relates to surface roughness in small-diameter drilling of GFRP. An et al. [9] investigated the machinability of GFRP considering the tool material and geometry factors. With the proper selection of cutting geometries, they presented good-quality machining of the workpieces. Aoyama et al. [10] investigated the drilling damage of GFRP composites considering the thickness of the glass cloth fiber bundles and the relative angle between the cutting direction and fiber influence. Khasbaba [11] investigated experimentally the influence of drilling and material variables on thrust force, torque, and delamination of GFRP composites. The research 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 [12] analyzed the differences in delamination mechanisms when drilling GFRP with and without a support placed under the workpiece. Zhong et al. [13] investigated the drilling of CFRP/Al/CFRP co-cured materials. The experiments were carried out to investigate the effect of the TiAlN coating on the thrust force of the drill bit. The results show that this coating helps reduce the maximum thrust force and improve hole edge quality and surface morphology and roughness during the drilling of CFRP/Al/CFRP co-cured materials. Davim et al. [14] aimed to study the cutting parameters (cutting velocity and feed rate) under specific cutting pressure, thrust force, damage, and surface roughness in GFRPs. This research team also 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 [15]. Singh and Bhatnagar [16] investigated the influence of drilling-induced damage on the residual tensile strength of UD-GFRP composite laminates with drilled holes for a variety of solid carbide drill point geometries under different cutting conditions and varying speeds and feeds. They also presented an attempt to quantify drilling-induced damage and to correlate it with different drill-point geometries and the drilling process parameters for four-layered UD-GFRP laminates [17]. Arul et al. [18] carried out an 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 [19] 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. [20] 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. [21] outlined a methodology to optimize process parameters in the drilling of GFRP for minimum delamination damage. Abrao et al. [22] 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. [23] performed an analytical investigation on UD-GFRP. Their finite element analysis findings were found to be in good agreement with their experimental findings.
Some recent investigations studied the drilling of CFRP [24,25]. In these studies, analytical models were constructed and experimental studies were performed to quantify the interfacial friction coefficients. The friction-induced temperature and the temperature promoted at the tool work interface during the drilling of CFRP composites were carefully measured utilizing the embedded thermocouple technique, and their results were compared and correlated with the input cutting parameters. Xu et al. [26] summarized the state-of-the-art research advances in studying drilling-induced damage for CFRP composites reported in the open literature, including delamination, burrs, tearing, surface cavities, glass transition failure, etc. Xu et al. [27] investigated the state-of-the-art progress in the mechanical drilling of CFRP composites through a rigorous literature survey. The survey covers the crucial aspects of drilling CFRP laminates, including drilling mechanisms, thermomechanical responses, drilling-induced damage, and the effects of various process conditions. The fundamental chip removal and damage formation modes of CFRPs are discussed. Xu et al. [28] investigated the drilling behaviors of woven GFRP composites under varying cutting speeds and feed rates. Machining studies were conducted using two different diamond-coated special tools: a double-point angle drill and a dagger drill. The drilling machinability of GFRPs was comprehensively analyzed in terms of cutting forces, machining temperatures, drilling-induced damage, dimensional accuracy, and hole wall morphologies. Xu et al. [29] presented a study that proposed a series of adjusted damage criteria, allowing a reliable quantification of the extent of the most critical defects in drilling CFRP laminates including burrs, tearing, and delamination. To verify the applicability of the proposed evaluation criteria, a number of drilling tests were performed on T800/X850 CFRP laminates using three types of drills, namely, brad spur drills, twist drills, and dagger drills. Xu et al. [30] presented a study that characterized the variation and evolution of temperature during CFRP drilling using diamond-coated candlestick and step tools. The progression of the composite drilling temperatures was recorded using an infrared thermography camera, and the hole quality was assessed in terms of surface morphologies and hole diameters. The study by Geier et al. [31] aimed to critically review and discuss challenges and recent expertise and experience gained in the area of edge-trimming CFRPs. Conventional and advanced edge-trimming technologies are reviewed and compared and advanced cutting tools are presented and discussed. Poor et al. [32] investigated the damage mechanism of CFRP. In their study, burr formation mechanisms, burr measurement methods, and burr parameters are critically reviewed, compared, and discussed. The main advantages and disadvantages of burr measures are highlighted, and their possible future applications and prospects are also considered. Xu et al. [33] presented a study to address the wear characteristics of two types of special tools, namely, a candlestick drill and a step drill when machining CFRPs. The novelty of the current work lies in identifying the underlying mechanisms controlling the wear development of the special tools and in quantifying their wear effects on the composite machining responses such as delamination extents, hole wall morphologies, and dimensional accuracy.
Lately, machine-learning techniques for the drilling of different types of FRP materials have been studied [34]. Although the studies have provided a number of quality and process outcomes, it is evident that the difficulty of collecting a large number of data sets is a barrier [35]. Another cohesive approach has been applied to predict and optimize multiple performance characteristics, namely, optimum thrust force, torque, hole entry delamination, and hole exit delamination, in the drilling process of CFRP materials. The study utilized multi-response optimization of the drilling process using a back propagation neural network–particle swarm optimization method [36]. The authors’ proposed method was effective and acceptable as the relative errors between prediction and experiment confirmation were less than 5%. Recently, a research study was conducted on the drilling quality of woven CFRP [37].
In this study, a single-hopping (plain) woven CFRP material was processed with different cutting parameters during drilling operations, and surface roughness measurements of the hole were made. The optimum cutting parameters required to obtain the best surface roughness values have been obtained both experimentally and theoretically. The SA and GA methods were preferred as optimization methods.

2. Materials and Methods

2.1. Materials

In the experimental studies, single-hopping (plain) mesh-oriented CFRP materials were used. Samples of 95% epoxy resin fiber composite material at a mixing ratio of 5% with a thickness of 10 mm (30 layers) were prepared in the form of plates. Plate size was 200 × 100 × 10 mm. The CFRP produced through this single-hopping (plain) approach has a mesh structure as can be seen in Figure 1. The mechanical properties of the CFRP plate are shown in Table 1.

2.2. Cutting Tools

During the experimental work, a variety of 8 mm coated carbide cutting tools with different flute numbers were used, as shown in Figure 2.

2.3. Cutting Conditions

In the experiments, different cutting parameters have been used such as cutting speed, feed, tip angle, and flute number. As a result of the combination of cutting parameters, 81 surface roughness values were obtained. The cutting parameters are given in Table 2.
A CNC milling machine (TAKSAN TMC 500V) was used for the experiments [38]. Countertop installation specification related to rigidity, vibration, and coplanarity was maintained. The tests were repeated three times to obtain reliable results. The experimental setup is shown in Figure 3.

2.4. Methodology

The methodology of this study comprised two steps, namely, the generation of network files to predict the surface roughness values and finding the optimum surface machining parameters yielding the minimum surface roughness values. These steps are explained below in detail. Also, the proposed methodology is shown in Table 3.

2.4.1. Generation of Network Files

A set of network files predicting the surface roughness values belonging to the plain CFRP material was generated in this step using the measurements. The generation of network files comprises three substeps, namely, generation of the input file, generation of the target file, and generation of the network file.

Generation of Input File

The input file is composed of the parameters of the surface machining process. As mentioned in the measuring the surface roughness values step, 81 measurements were carried out. In addition, four parameters were used for surface machining of the plain CFRP composite. Hence, an input file having 81 rows and 4 columns was generated for the plain samples.

Generation of Target File

The target file is composed of the results of surface roughness measurements. As mentioned above, 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 CFRP material.

Generation of Network File

After the input and the output files were generated, the network file was generated for the plain CFRP material. These network files were generated with the use of the nf tool command in MATLAB 2024a software [39]. A two-layer feed-forward neural network was used in order to obtain the approximate function of structural analyses [40]. The network was trained with the Levenberg–Marquardt backpropagation algorithm (trainlm) [41], unless there was not enough memory, in which case, scaled conjugate gradient back-propagation (trainscg) [42] was used. The parameters used in the generation of neural network files are shown in Table 4.

2.4.2. 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 the previous step were used with two different optimization algorithms in order to obtain the optimum parameters for the surface machining process. These algorithms are GA and SA. Since two algorithms were used to obtain the optimum parameters, some information is given about these algorithms in the following section.

Optimization

Optimization may be defined as the process of maximizing or minimizing a desired objective function while satisfying the prevailing constraints. Optimization of the drilling process is crucial to achieving the best possible surface roughness values, which directly impact the performance and longevity of the drilled components. By fine-tuning the process parameters such as cutting speed, feed rate, and tip angle, manufacturers can usually minimize surface imperfections, reduce tool wear, and enhance the overall quality of the finished product. Optimal surface roughness ensures a better fit and function of the parts, decreases the likelihood of defects, and improves the aesthetic appeal of the finished product. Therefore, optimization plays a crucial role in enhancing productivity, reducing costs, and ensuring high standards in manufacturing processes.

Simulated Annealing

The SA optimization algorithm is inspired by the annealing process. It aims to find a global optimum point simulating the annealing process. The explanations regarding SA given below were taken from Rao [43]. The SA is an optimization method based on the annealing process. An annealing process is heating any material or metal up to its annealing temperature and then cooling it slowly. This process is applied in order to soften the material, optimize the internal structure of the material, and eliminate internal stresses. The SA method simulates the process of slow cooling of molten metal to achieve the minimum function value in a minimization problem. Further details can be found in Engineering Optimization: Theory and Practice. Modified versions of simulated annealing have been used successfully in different research areas by Kirkpatrick et al. [44], Wong et al. [45], Drexl [46], Wasserman et al. [47], Telley et al. [48], and Carnevali et al. [49].

Genetic Algorithm

The GA, which is based on Darwin’s Evolution Theory, was invented by Holland [50]. However, it was not until Goldberg, who was a student of Holland, published a book regarding GA named “Genetic Algorithms in Search, Optimization, and Machine Learning” that it became a popular optimization method [51]. Because of increasing competition in engineering and the contribution of increasingly powerful computer technology, the GA became more popular. The advantages of the GA compared to the traditional optimization methods are as follows:
  • The GA uses a population of points for the starting phase in place of a single point. Thus, the possibility of becoming stuck into a local optimum is minimized.
  • In GA, it is not necessary to have a continuous objective function. Thus, it can be applied to optimization problems having continuous or discrete variables.
  • In GA, better design vectors are sought via the probabilistic GA operators. In each iteration, GA tries to find new design vectors having better fitness values. Despite these operators working randomly, they use information based on the fitness values.
As was mentioned above, GA is based on Evolution Theory. So, it uses the principles of Evolution Theory, like reproduction, crossover, and mutation. The GA uses these principles as probabilistic tools in order to obtain fitter individuals, as in Evolution Theory. More information can be taken from the book of Rao [43].

Finding the Optimum Parameter Values for Surface Machining with GA

There are several studies combining the ANN and GA in the literature but this approach has not been applied to finding optimum cutting parameters. For detailed information on the used methodology, the study by Salajegheh et al. [52] can be examined.
In this study, four parameters are used for the optimization of the surface roughness of the plain CFRP. These parameters are feed rate, cutting speed, tip angle, and flute number. The upper bounds and the lower bounds of these parameters are shown in Table 5.
To find the optimum surface machining parameters, GA and the network file generated for predicting the surface roughness value for the 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.

Finding the Optimum Parameters Value for Surface Machining with SA

There are many applications of SA in optimization studies or of ANN in modeling in the literature but very few studies that combine SA and ANN. For detailed information, [32], a study that combines ANN and SA, can be viewed.
The parameters used in the optimization with SA are the 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 in GA and the surface machining parameters yielding the minimum surface roughness value were found.

3. Results

The results obtained are given in two main categories, namely, experimental results and numerical results.

3.1. Experimental Results

The experimental results are shown in Figure 4, Figure 5 and Figure 6.
Based on Figure 4, Figure 5 and Figure 6, it can be seen that the surface roughness value increases as the feed rate increases, and conversely, as the cutting speed increases, the surface roughness decreases.
The tip angle factor significantly affects the surface roughness with the two-flute tool. In addition, although there is a slight change with the three-flute tool, there is almost no change with the four-flute tool. It can be seen that the surface roughness value decreases as the tip angle increases with the two-flute tool.

3.2. Numerical Results

3.2.1. Results for Network Files

It was mentioned above 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. The mean error value and maximum error value for the network file are shown in Table 6.
Figure 7 shows the correlation between the predicted and experimental values for the training and testing sets. As seen in Table 6 and Figure 7, it can be said that the predicted results are very successful. The mean error and the maximum error of the training and the testing patterns of the network files are 4.1714 and 0.1792, respectively.

3.2.2. Optimum Parameter Results

As stated above, the optimum surface machining parameters and minimum surface roughness value for the plain material were obtained with both GA and SA. These results are shown in Table 7 and Table 8 below.
According to Table 7, 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 are equally applicable to the proposed method.
As in Table 7, the optimum parameter values for GA and SA in Table 8 are also close. This observation strengthens the conviction that both optimization algorithms are applicable to the proposed method.

4. Conclusions

Based on the research study, it is concluded that for both one-hopping (plain) and polymer matrix composites, an increase in cutting speed generally reduces surface roughness values in the hole, while an increase in feed rate increases surface roughness. The tip angle of the tool significantly influences surface roughness, particularly with a two-flute tool, where an increase in tip angle leads to a decrease in surface roughness. However, this effect is not observed with three- or four-flute tools. Among the various tool configurations tested, the four-flute tool operating at a cutting speed of 90 m/min, a feed rate of 0.2 mm/rev, and a tip angle of 120° provided the best surface roughness results. 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, the surface roughness value is obtained. The key findings of the study are summarized below:
  • The cutting parameters are optimized to obtain good-quality surface roughness values in the 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 can be understood from its error values, which are 4.1714 and 0.1792 for the maximum and the mean error values, respectively.
  • For the optimization of the drilling process, a hybrid method of ANN-GA and a hybrid method of ANN-SA were used. The optimum results obtained by ANN-GA and ANN-SA are 0.8746 and 0.8705, respectively. These results are so close to each other.
  • The optimum results for ANN-GA are 0.2011 as the feed rate, 89.9983 as the cutting speed, 119.9998 as the tip angle, and 4 as the flute number. Conversely, the optimum results for ANN-SA are 0.2000 as the feed rate, 89.9996 as the cutting speed, 119.9977 as the tip angle, and 4 as the flute number. The optimum cutting parameters obtained via ANN-GA and ANN-SA are close as well.
  • In conclusion, the hybrid optimization methods proposed in this study have provided successful results. It is observed that the production of good-quality holes in the drilling of CFRP can be achieved using the proposed methodology.

Author Contributions

Conceptualization, B.I.; methodology, B.I. and M.S.G.; formal analysis, B.I.; investigation, B.I. and M.S.G.; writing—original draft preparation, ALL; writing—review and editing, ALL; supervision, I.F. and M.B.-G.J.; project administration, I.F. and M.B.-G.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study can be obtained from the corresponding authors upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Processes 12 01477 g001
Figure 1. The CFRP material. (a) Plate photo, (b) Carbon fiber cloth (1 × 1 plain weave).
Figure 1. The CFRP material. (a) Plate photo, (b) Carbon fiber cloth (1 × 1 plain weave).
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Figure 2. Geometry of the cutting tools.
Figure 2. Geometry of the cutting tools.
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Figure 3. The experimental setup. (a) Photo, (b) Schema.
Figure 3. The experimental setup. (a) Photo, (b) Schema.
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Figure 4. Comparison of surface roughness and feed for two-flute coated carbide cutting tool.
Figure 4. Comparison of surface roughness and feed for two-flute coated carbide cutting tool.
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Figure 5. Comparison of surface roughness and feed for three-flute coated carbide cutting tool.
Figure 5. Comparison of surface roughness and feed for three-flute coated carbide cutting tool.
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Figure 6. Comparison of surface roughness and feed for four-flute coated carbide cutting tool.
Figure 6. Comparison of surface roughness and feed for four-flute coated carbide cutting tool.
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Figure 7. Comparison of predicted and experimental data.
Figure 7. Comparison of predicted and experimental data.
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Table 1. Mechanical properties of the CFRP material.
Table 1. Mechanical properties of the CFRP material.
PropertiesValues
Resin TypeEpoxy E201 (hot cured)
Laminate Intensity [g/cm3]1.62
Fiber Rate [%]55–65
Tensile Strength [MPa]740
Elasticity Module [GPa]70
Bending Strength [MPa]760
Shear Module [GPa]62
Transition Temperature of Resin [°C]144
Thickness of Laminate Floor [mm]0.65
Table 2. The cutting parameters.
Table 2. The cutting parameters.
Cutting Speed,
Vc [m/min]		Feed,
f [mm/rev]		Tip Angle,
Ψ [°]		Flute Number
500.2602
700.3903
900.41204
Table 3. The proposed methodology.
Table 3. The proposed methodology.
Generation of Network Files
-
Generating the input file
-
Generating the target file
-
Generating the network file
Optimization
-
Finding optimum result using GA
-
Finding optimum result using SA
-
Comparison of the results from GA and SA
Table 4. Parameters used in the generation of neural network files.
Table 4. Parameters used in the generation of neural network files.
ParametersValue
Number of Measurement81
Number of Parameters4
Number of Parameter Level3
Table 5. Upper and Lower Bounds for the Surface Machining Parameters.
Table 5. Upper and Lower Bounds for the Surface Machining Parameters.
ParametersUpper BoundLower Bound
Feed [mm/rev]0.40.2
Cutting Speed [m/min]9050
Tip Angle [°]12060
Flute Number42
Table 6. Maximum and Mean Error for the Network File.
Table 6. Maximum and Mean Error for the Network File.
Network FileMax. Error [%]Mean. Error [%]
netplain4.17140.1792
Table 7. Minimum Surface Roughness Value Found by GA and SA.
Table 7. Minimum Surface Roughness Value Found by GA and SA.
Minimum Surface Roughness ValueGASA
Minimum Surface Roughness, [µm] 0.87460.8705
Table 8. Optimum surface machining parameters found by GA and SA.
Table 8. Optimum surface machining parameters found by GA and SA.
ParametersGASA
Feed [mm/rev] 0.20110.2000
Cutting speed [m/min]89.998389.9996
Tip angle [°]119.9998119.9977
Flute number44
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Isik, B.; Gultekin, M.S.; Fidan, I.; Jun, M.B.-G. Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes. Processes 2024, 12, 1477. https://doi.org/10.3390/pr12071477

AMA Style

Isik B, Gultekin MS, Fidan I, Jun MB-G. Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes. Processes. 2024; 12(7):1477. https://doi.org/10.3390/pr12071477

Chicago/Turabian Style

Isik, Birhan, Mehmet Sah Gultekin, Ismail Fidan, and Martin Byung-Guk Jun. 2024. "Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes" Processes 12, no. 7: 1477. https://doi.org/10.3390/pr12071477

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