Advances in Materials Science and Engineering

Advances in Materials Science and Engineering / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 5868132 | 10 pages | https://doi.org/10.1155/2019/5868132

Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method

Academic Editor: Antonio Riveiro
Received08 May 2019
Revised27 Jun 2019
Accepted14 Jul 2019
Published19 Sep 2019

Abstract

Stainless steels with unique corrosion resistance are used in applications with a wide range of fields, especially in the medical, food, and chemical sectors, to maritime and nuclear power plants. The low heat conduction coefficient and the high mechanical properties make the workability of stainless steel materials difficult and cause these materials to be in the class of hard-to-process materials. In this study, suitable cutting tools and cutting parameters were determined by the Taguchi method taking surface roughness and cutting tool wear into milling of Custom 450 martensitic stainless steel. Four different carbide cutting tools, with 40, 80, 120, and 160 m/min cutting speeds and 0.05, 0.1, 0.15, and 0.2 mm/rev feed rates, were selected as cutting parameters for the experiments. Surface roughness values and cutting tool wear amount were determined as a result of the empirical studies. ANOVA was performed to determine the significance levels of the cutting parameters on the measured values. According to ANOVA, while the most effective cutting parameter on surface roughness was the feed rate (% 50.38), the cutting speed (% 81.15) for tool wear was calculated.

1. Introduction

Due to its unique corrosion resistance, low heat conduction coefficient, and high mechanical properties, stainless steels are one of the materials most frequently encountered in many areas such as health, food, chemistry, marine, defense, and nuclear power plants, and their use continues to increase every day [13]. The Custom 450 is a martensitic stainless steel grade, which exhibits very good corrosion resistance (up to about 650°C), and its mechanical properties can be significantly increased by heat treatment methods. It exhibits excellent resistance to rusting and pitting in saltwater environment at about 20% [4]. Although many studies are available in the literature on the machinability of stainless steels, publications on Custom 450 grade are relatively limited.

The low heat conduction coefficient and high mechanical properties of stainless steels make it difficult to process [57]. The various mechanical properties of different stainless steels are also important variables that determine machinability [8]. The high shear forces and heat generated during machining result in rapid wear of the tool, difficulties in discharge of the chip, and adhesion of the chip to the tool, resulting in a reduction in surface quality [3]. The feed rate and radius of tip of the cutting tool are significantly effective on the surface roughness. The increase in the depth of cut and the amount of progress causes the surface roughness values to rise [9, 10]. In addition, an increase in the feed rate increases the cutting forces, as expected, and with the increase of cutting speed, the cutting forces tend to get reduced [11]. Material of the cutting tool and its geometry, coating, and processes such as cryogenic processing are also effective on the processing of stainless steels [8, 1215]. Shrinkage of the radius of the insert causes wear to occur faster [16]. In milling operations of 316L stainless steel, notching and breakage occur in the cutting tool and the cutting forces increase considerably [17, 18]. The use of cutting fluid in the processing of stainless steels is another factor that influences the quality of the resultant piece [19]. In addition, the type and amount of cutting fluid used is one of the factors affecting the results in the cutting process [20].

Ciftci examined the effects of the coating of cutting tool, cutting speed, and material of the workpiece on cutting forces and surface roughness in dry turning of the materials AISI 304 and AISI 316. He stated that the type of coating had an effect on cutting forces. He stated that the cutting speed did not cause a significant change in the cutting forces but significantly affected the roughness of the machined surface [12]. Basmaci et al. investigated the effect of feed rate, cutting depth, and cutting tool tip radius on cutting force and surface roughness in turning of the material 17-4 PH stainless steel [21]. Selaimia et al. examined the milling of X2CrNi18-9 austenitic stainless steels in different cutting parameters by means of dry coated carbide tools in terms of surface roughness, cutting force, cutting power, specific cutting force, and chip removal rate. They stated that the amount of feed was effective on the surface roughness and the specific cutting force and that the cutting force and cutting power were affected by the depth of the chip [22]. Varghese et al. used coated carbide cutting tools to examine tool wear performance during dry milling of AISI 304 stainless steel, and they determined that the coating was removed and microcracks were formed at the end of the experiment [18].

Using the Taguchi method, Kuram and Ozcelik subjected AISI 304 stainless steel to micromilling tests by means of end mill cutters with global tips. They examined the effects of spindle speed, feed rate, and cutting depth on tool wear, cutting forces, and surface roughness. In their study, they defined the relationship between independent variables and dependent variables using regression and fuzzy logic. From the results obtained, they stated that both regression and fuzzy logic modelling could be used effectively to predict tool wear, cutting forces, and surface roughness [23]. Lin has determined the cutting speed, feed rate, and cutting depth as input variables in the milling of the stainless steel material. He attempted to optimize machinability in terms of the chip removal rate, surface roughness, and height of the burr formed [24]. In another study, Lin conducted a series of experiments to examine the formation of burrs and tool wear during milling of stainless steel. He stated that the burr height depended on the way of milling and that the changes in the cutting parameters could produce different types of burrs at the output edge. He stated that a large depth of cut caused formation of wave-shaped burrs, the edge was chiselled due to high feed rate, and small material masses could accumulate at high cutting speeds [25]. Shao et al. investigated the wear mechanisms of TiCN/TiN-coated carbide tools and machinability of the material during milling of stainless steel material containing 3% Co–12% Cr. They also examined the relationship between cutting conditions, surface integrity, and tool wear. It was found that cutting tools with 17° rake angle inserts have longer life than that of a rake angle with 28°. SEM and EDX analyses were used to analyse wear and failure mechanism of the tool [26].

Liew explored the wear of PVD-coated carbide and uncoated carbide tools during milling of STAVAX stainless steel materials at low speeds. When the cutting speed was increased from 25 to 50 m/min, no significant change was observed in tool wear. He found out that increasing the hardness of the workpiece from 35 to 55 HRC caused a significant increase in the wear of side surface and a change in the dominant wear mechanism. He stated that the coated tool demonstrated a much higher breaking resistance than the uncoated tool. In addition, he stated that the use of cutting fluid was an important factor in increasing tool life [27, 28]. Nordin et al. examined the tool wear caused by milling of austenitic stainless steel by multi- and single-layer-coated carbide tools. They observed mechanical and chemical wear mechanisms and stated that crack formation was the most effective wear on the tool life. They emphasized that multilayer coatings consisting of TiN and TaN were suitable for milling austenitic stainless steel [29]. Selinder et al. carried out experiments on the type and thickness of the coating of the cutting tools during the milling process of AISI 303 and AISI 304 materials. The results were obtained from the processing tests they carried out with the new coating they had produced, and they stated that it performed better compared to the single-layer PVD and CVD coatings [30].

Junior et al. examined the effects of cutting fluid and lubrication on the machinability of stainless steel materials. They exhibited that cooling and lubricating conditions strongly affected the tool life and tool wear mechanisms in the machining of martensitic stainless steel [31]. Nalbant and Yildiz experimentally examined the effects of cryogenic cooling on cutting forces in milling of AISI 304 stainless steel materials. They stated that cryogenic cooling and cutting speed were effective on cutting forces [32]. Fnides et al. investigated the impacts of factors such as cutting speed, feed rate, and depth of cut on surface roughness and material removed rate when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology. For this purpose, a number of machining experiments based on statistical three factor and three level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. It was determined that cutting speed is the most important parameter affecting the surface roughness [33]. In this study, it is aimed at determining suitable cutting tools and cutting parameters for surface roughness and cutting tool wear in face milling Custom 450 stainless steel. For the Taguchi method, milling tests were carried out using L16 test design. ANOVA was performed to determine the significance levels of the cutting parameters on the values measured.

2. Materials and Methods

2.1. Workpiece and Cutting Tool Materials

Milling experiments were carried out at the Arion IMM-600 CNC vertical machining centre. The mean surface roughness values (Ra) were measured using Mitutoyo SJ-410 profilometre. A microscope with a digital magnification of 5 micron pixel size, 2592 × 1944 resolution, and a brightness reduction of 240 was used to observe tool wear. Tool wear values were measured using AutoCAD software. Custom 450 stainless steel material was cut to size 60 × 60 × 8 mm. The chemical composition of the workpiece and some properties are given in Table 1 [35].


PropertyUnitCustom 450

Chemical composition%C < 0.05, 14–16 Cr, 1.25–1.75 Cu, 75 Fe, Mn < 1, 0.5–1 Mo, 5–7 Ni
Density (20°C)g/cm37.75
HardnessBrinell278
Yield stressMPa814
Tensile stressMPa979
Elasticity modulusGPa200
Poisson’s ratio0.29
Thermal conductivityW/m-K15

The cutting tools were obtained from different manufacturers as a result of identifying user recommendations and literature surveys. Cutting tools with a radius of 0.8 mm were mechanically connected to a single-hole tool holder with an Ø12 mm diameter. The features of cutting tools and tool holders are given in Table 2. The cutting tests were carried out by milling without the use of cutting fluid. The cutting tool was centred on the 8 mm (depth of radial cut) part of the workpiece so that the axial cutting depth would be 2 mm. Three measurements were taken for surface roughness values, and averages were taken. Cutting tool wear was measured after 960 mm3 of the chip were removed.


NoManufacturerInsert codeGradeTool holder code

T1KennametalEDPT10T308PDSRGEKC522M PVD (Al, Ti) N12A01R020A16ED10
T2MitsubishiAOMT123608PEER-HVP15TF PVD (Al, Ti) NKMTAOMT100R 121W16S
T3SandvikR390-11 T308MPM 4240 CVD Ti (C, N) + Al2O3 + TiN)R390-012A16-11L Coromill 390
T4SandvikR390-11 T308MH13A uncoated

2.2. Test Setup and Analysis Methods

In this study where the Taguchi method was used, surface roughness and cutting tool wear, which was formed during milling of Custom 450 stainless steel material, were investigated. The cutting tool (Ct), the cutting speed (Vc), and the feed rate (f) were determined as the control factors, and 4 levels were selected for each control factor. The values of the levels were determined by a series of preliminary experiments based on literature and catalogue values. For the Taguchi method, the L16 vertical sequence was used as the experimental design. In Table 3, control factors and levels are given.


Control factorsUnitCodeLevels
1234

Cutting tool (Ct)AT1T2T3T4
Cutting speed (Vc)m/minB4080120160
Feed rate (f)mm/revC0.050.100.150.20

In order to determine the appropriate levels of control factors, the condition with the lowest quality characteristic values should be determined. For this purpose, the signal/noise (S/N) ratio was calculated using the “smallest best” objective function equation (1). ANAVO was applied for the 95% confidence interval of the test results to determine the effects of control factors on quality characteristics. This was performed on the Minitab 17 program:

3. Results and Discussion

The average surface roughness values and tool wear values that came out during milling of Custom 450 stainless steel and the S/N ratios calculated through equation (1) are given in Table 4.


ExperimentControl factorsExperimental resultsS/N ratios
ACutting (Ct)BCutting speed (Vc)CFeed rate (f)Surface roughness (Ra)Flank wearSurface roughness (Ra)Flank wear

11T114010.050.3750.0538.5193725.5145
21T128020.10.5040.0625.9513924.1522
31T1312030.150.5930.0694.5389123.2230
41T1416040.20.8510.0811.4014121.8303
52T214020.10.5020.0655.9859323.7417
62T228010.050.5710.0604.8672824.4370
72T2312040.21.0540.072−0.4568122.8534
82T2416030.150.7510.0802.4872021.9382
93T314030.150.7040.0653.0485523.7417
103T328040.21.1670.065−1.3414223.7417
113T3312010.050.6520.0663.7150523.6091
123T3416020.10.7860.0802.0915521.9382
134T414040.20.8060.0661.8733023.6091
144T428030.151.1020.063−0.8436324.0132
154T4312020.10.7840.0702.1136823.0980
164T4416010.050.6480.0803.7685021.9382

3.1. Surface Roughness

The main effect graphs obtained from the S/N ratios calculated for the surface roughness values are shown in Figure 1. Also, order of significance of the S/N ratios to control factors for surface roughness values is given in Table 5. When the main effect graphs in Figure 2 and the highest and lowest points of the S/N ratios in Table 5 are analysed, it is seen that while the most significant control factor having an influence on the surface roughness is feed rate (f), the others are cutting tool (Ct) and cutting speed (Vc).


Control factorsS/N ratios (dB)Max-minOrder of significance
Level 1Level 2Level 3Level 4

Ct5.10283.22091.87841.72803.37482
Vc (m/min)4.85682.15842.47772.43722.69843
f (mm/rev)5.21764.03562.30780.36914.84841

When Table 4 is analysed, the lowest surface roughness value is measured as 0.375 μm in milling with the T1 coded cutting tool at a cutting speed of 40 m/min and in 0.05 mm/rev feed rate, and the highest surface roughness value is measured as 1.167 μm in milling with T3 coded cutting tool at a cutting speed of 80 m/min and in 0.2 mm/rev feed rate.

Variance of surface roughness values resulting from the machining of the Custom 450 according to the cutting speed-feed rate interaction is shown in Figure 3 in the form of a surface graph. When the graph is analysed, it is seen that the surface roughness values are increased with an increase in the feed rate and the statistical calculations do verify this.

In order to determine the effect levels of control factors on surface roughness, ANOVA was performed, and the results are given in Table 6. In this table, the degree of freedom (SD), the sum of squares (Ct), the quadratic mean (Ko), F ratio, value indicating the significance level of control factors on Ra, and the contribution rates (%) are given. According to the ANOVA result, a value less than 0.05 is the indicator of statistically significant difference on the surface roughness value of the control factors. When Table 6 is analysed, it is seen that feed rate has an effect on the surface roughness of 50.38%, the cutting tool has 27.63%, and the cutting speed has 17.85%.


Control factorsDegree of freedom (SD)Sum of squares (Ct)Mean squares (MS) values valuesPCR (%)

Ct329.3819.79413.360.00527.63
Vc318.9776.3268.630.01317.85
F353.55817.85324.360.00150.38
Error64.3980.7334.14
Total15106.314100.0
R2% 95.86

It is an expected result that the feed rate is the most effective control factor on the surface roughness value [10, 16]. The second control factor that is statistically significant on the surface roughness is the cutting tool. The surface roughness value resulting from machining is greatly affected by tool geometry, coating, and cutting tool surface [12, 14]. The cutting tools used in the experiments have similar geometry but are supplied from different manufacturers. In addition, experiments were carried out with coated and uncoated cutting tools with the same geometry. Each tool has its own tip forms and chip breaker forms (Table 2) Difference in section that makes the cutting and in the geometry of chip breaker section change the flow of chip formation and cutting geometry, resulting in changes in surface roughness values. It is seen that the third control factor, the cutting speed, was not statistically significant on the surface roughness ().

In the Taguchi method, after determining the levels of control factors which would yield the most appropriate results, validation tests should be performed in order to test the accuracy of the optimization. It is seen in Table 5 and Figure 2 that the levels of the optimum control factors for the surface roughness values according to the S/N ratios calculated with the “smallest best” objective function are A1-B1-C1. These levels are present in the L16 test design, and the results of the test carried out will be used. The predictive minimum surface roughness value for these levels is calculated using equations (2) and (3) and is given in Table 7.


MaterialConfirmation test resultsCalculated valuesDifferences
Ra mes. (N)S/N (ηmes, dB)Ra calc (N)S/N (ηcalc, dB)Ra mesRa calcηmesηcalc

Custom 4500.3758.51940.2589.21210.117−0.6927

Levels of the optimum control factors and results of the tests carried out are evaluated taking into account the confidence interval (CI) calculated using the following equation:

Calculation of test coefficient (), total number of tests, and sum of the degree of freedom of the control factors having a significant effect on the surface roughness value are calculated through equation (5). Fsdt is the average degree of freedom which is always 1, Fsde is the error degree of freedom, and Ve is the error variance.

The value of was determined from the table taking into account the degree of freedom (SD) of error in Table 6. is the total degree of freedom of factors. Error variance in equation (4) (Ve) was figured out with the help of data in Table 6.

The confidence interval (CI) value for the surface roughness value was calculated as 2.9633 dB when the values calculated were replaced at equation (4). The comparison of the values calculated through the results of the validation test at the optimum levels of the control factors to the values calculated with the help of equations (2) and (3) are presented in Table 7.

The fact that difference between the S/N ratios of the results obtained from the validation tests and the S/N ratios of the values calculated using the equations (2) and (3) is −0.6927 dB. When these values are compared, it is seen that the calculated value is quite small from the value of the confidence interval (2.9633 > −0.6927). This result indicates that the optimization for surface roughness is appropriate.

3.2. Cutting Tool Wear

As a result of milling of the Custom 450 material with carbide tools, flank wear occurred in the cutting tool, the width of the wear strip formed was measured, and the extent of wear was measured. The main effect graphs of the control factors, as a consequence of the cutting process, on the wear occurring on the cutting tool are given in Figure 4, and in Table 8, the S/N ratios calculated to determine the optimum cutting conditions for the wear values are presented.


Control factorsS/N ratios (dB)Max-minOrder of significance
Level 1Level 2Level 3Level 4

Ct23.6823.2423.2623.160.523
Vc (m/min)24.1524.0923.2021.912.241
f (mm/rev)23.8723.2323.2323.010.872

It is seen that, in milling experiments, the most important control factor affecting the wear of the cutting tool is cutting speed (Vc) (Figure 5 and Table 8). The feed rate, out of the control factors, is the second, and the cutting tool is the last one (Table 8).

In Figure 5, the change of cutting tool wears due to the cutting speed-feed rate interaction in the form of a surface graph. When the graph is analysed, it is seen that the growth in the wear value is clearly seen with increasing cutting speed. In addition, with the increase of the feed rate, a small amount of increase was seen in wear value as well.

These results do verify statistical data. The energy required for conversion of plastic deformation into heat and the rise of the value of heat that is released due to the friction resulting from the contact of the cutting tool on the surface of the workpiece newly formed with the increase in the cutting speed is a result that is expected and is consistent with the literature [3638]. For this reason, in the process of machining of materials with low thermal conductivity coefficient and high resistance, the cutting speed is an important factor on tool life.

Figure 6 shows SEM and EDX analyses at four different cutting speeds for the T1 cutting tool. When the SEM images and EDX analyses are examined, the effect of the cutting speed on the tool life is clearly seen. Increased cutting speed increases the deformation in the cutting tool. The cutting speed was increased by 160 m/min, and the cutting tool was deformed to negatively affect the cutting process. At the same time, EDX analysis shows the presence of copper and chromium elements from the iron and alloy materials, which are the main material of the workpiece on the cutting tool. This is proof that the workpiece material has a tendency to adhere to the cutting tool although it is not dense on the cutting tool.

Amount of tool wear measurement was determined by using a CAD software to determine a known length on the cutting tool in proportion to the wear size. 2400 mm3 chips were removed in each tool wear test (Figure 7). Surface roughness values resulting from machining processes are influenced by cutting tool geometry, coating, and surface of cutting tool. The geometry of the cutting tools used in this study is the same as the outlines. However, these tools are supplied from different manufacturers: one of the two cutting tools from the same manufacturer is coated, and the other is uncoated. The cutting edges and chip breaker geometries of these tools also differ. The geometry of the cutting edge and chip breaker makes the surface roughness different. In addition, even if the geometry of two different cutting tools is the same, the coating applied to the cutting tool also affects the surface roughness values.

The results of the ANOVA carried out for the cutting tool are given in Table 9. According to Table 9, the cutting speed is effective on the wear of the cutting tool by as much as 81.15%, feed rate 10.39%, and cutting tool 4.02%.


Control factorDegree of freedom (SD)Sum of squares (SS)Mean squares (MS) values valuesPCR (%)

Ct30.65020.21671.810.2454.02
Vc313.11054.370236.520.0081.15
f31.67800.55934.670.05210.39
Error60.71800.11974.44
Total1516.1567100.0
R2% 95.56

In the light of the calculations taken in the surface roughness, verification tests should be carried out to test the validity of wear optimization. As shown in Figure 6 and Table 8, the optimum levels of control factors on the cutting tool wear were determined to be A1-B1-C1. The lowest possible wear value according to this order is calculated with the help of equations (2) and (3). Considering the confidence interval (CI) value with the help of equation (4), the validation test results with the levels of optimum control factors were evaluated. The CI value for cutting tool wear is calculated as 1.20 dB.

Given in Table 10 are the S/N ratios of the results obtained from the validation tests and the S/N ratios and the differences between them. As the difference between the S/N ratios of the results of the validation test and the S/N ratio of the predictive value is smaller than the CI value, it can be statistically said that the optimization is valid. These results verify the levels of optimum cutting factors determined based on the Taguchi optimization method for tool wear.


MaterialConfirmatory test resultsCalculated valuesDifferences
Wearmes (N)S/N (ηmes, dB)Wearcalc (N)S/N (ηcalc, dB)Wearmes−wearcalcηmesηcalc

Custom 4500.05325.51450.05625.0340−0.0030.4805

4. Conclusions

In this study, Custom 450 martensitic stainless steel was subjected to milling experiments in dry conditions at the cutting parameters by means of carbide cutting tools in the face milling method, and the following results were obtained.(i)It was identified that the control factor with the highest significance on average surface roughness was feed rate with 50.38%, and on the cutting tool wear, it was the cutting speed with 81.15%.(ii)An increase was observed, as expected, in the surface roughness value with the increasing feed rates. The lowest surface roughness values were obtained at the lowest feed rates.(iii)It is seen that the cutting tool is also effective on the surface roughness value. It is considered that these results are from the different coating types and geometries.(iv)It was seen that the cutting tool wear was significantly related to cutting speed. With the increased cutting speed, the wear on cutting tool also increased. This phenomenon is attributed to the fact that, with the increase in cutting speed, the temperature rises and asserts an adverse effect on the cutting tool life.

Data Availability

In this study, machining tests of a customized material for the defense industry were carried out. This article contains valuable data for missile systems. The data used to support the findings of the study are included within the article.

Conflicts of Interest

The author declares that there are no conflicts of interest.

References

  1. G. Basmaci, “Optimization of processing parameters of AISI 316 Ti stainless steels,” Academic Platform Journal of Engineering and Science, vol. 6, no. 3–6, pp. 1–7, 2018. View at: Google Scholar
  2. J. D. Darwin, D. M. Lal, and G. Nagarajan, “Optimization of cryogenic treatment to maximize the wear resistance of 18% Cr martensitic stainless steel by Taguchi method,” Journal of Materials Processing Technology, vol. 195, no. 1–3, pp. 241–247, 2008. View at: Publisher Site | Google Scholar
  3. J. C. Outeiro, D. Umbrello, and R. M’Saoubi, “Experimental and numerical modelling of the residual stresses induced in orthogonal cutting of AISI 316L steel,” International Journal of Machine Tools and Manufacture, vol. 46, no. 1, pp. 1786–1794, 2006. View at: Publisher Site | Google Scholar
  4. http://www.spacematdb.com/spacemat/manudatasheets/custom%20450.pdf.
  5. N. R. Baddoo, “Stainless steel in construction: a review of research, applications, challenges and opportunities,” Journal of Constructional Steel Research, vol. 64, no. 11, pp. 1199–1206, 2008. View at: Publisher Site | Google Scholar
  6. H.-P. Zhang, Q.-Y. Zhang, Y. Ren, T. Shay, and G.-L. Liu, “Simulation and experiments on cutting forces and cutting temperature in high speed milling of 300M steel under CMQL and dry conditions,” International Journal of Precision Engineering and Manufacturing, vol. 19, no. 8, pp. 1245–1251, 2018. View at: Publisher Site | Google Scholar
  7. A. Uysal, “Investigation of cutting temperature in minimum quantity lubrication milling of ferritic stainless steel by using multi wall carbon nanotube reinforced cutting fluid,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 32, no. 3, pp. 645–650, 2017. View at: Google Scholar
  8. N. A. Ozbek, A. Cicek, M. Gulesin, and O. Ozbek, “Evaluation of machinability of AISI 304 and AISI 316 austenitic stainless steels,” Journal of Polytechnic, vol. 20, no. 1, pp. 43–49, 2017. View at: Google Scholar
  9. O. Tekaslan, N. Gerger, and U. Seker, “AISI 304 östenitik paslanmaz çeliklerde kesme parametrelerine bağlı olarak yüzey pürüzlülüklerinin araştırılması,” Journal of Balikesir University Institute of Science and Technology, vol. 10, no. 2, pp. 3–12, 2008. View at: Google Scholar
  10. H. Gurbuz, F. Kafkas, and U. Seker, “The effects of tool cutting edge form and chip breaker forms on cutting forces and surface roughness in machining AISI 316L steel,” Batman University Journal of Life Sciences, vol. 1, no. 2, pp. 18–24, 2012. View at: Google Scholar
  11. M. E. Korkmaz, T. Meral, and M. Gunay, “AISI 420 martenzitik paslanmaz çeliğin delinebilirliğinin sonlu elemanlar yöntemiyle analizi,” Gazi Journal of Engineering Sciences, vol. 4, no. 3, pp. 223–229, 2018. View at: Google Scholar
  12. I. Ciftci, “The influence of cutting tool coating and cutting speed on cutting forces and surface roughness in machining of austenitic stainless steels,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 20, no. 2, pp. 205–209, 2005. View at: Google Scholar
  13. O. Tekaslan, N. Gerger, M. Gunay, and U. Seker, “Examination of the cutting forces of AISI 304 austenitic stainless steel in the turning process with titanium carbide coated cutting tools,” Pamukkale University Engineering College Journal of Engineering Sciences, vol. 13, no. 2, pp. 165–144, 2007. View at: Google Scholar
  14. E. Altinkaya and A. Gullu, “The effect of the form of chip breaker on tool wear and surfaces roughness during machining of AISI 316 austenitic stainless steel,” Journal of Polytechnic, vol. 11, no. 1, pp. 13–17, 2008. View at: Google Scholar
  15. A. Mavi and G. Uzun, “The effect of cutting parameters on machinability in turning of duplex 1.4462 stainless steels,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, vol. 5, no. 3, pp. 177–184, 2017. View at: Google Scholar
  16. Y. Kayir, S. Aslan, and A. Ayturk, “Analyzing the effects of cutting tools geometry on the turning of AISI 316Ti stainless steel with Taguchi method,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 28, no. 2, pp. 363–372, 2013. View at: Google Scholar
  17. Y. Fedai and A. Univar, “The effects of machining parameters on the cutting forces and tool wear in milling of AISI 316L austenitic stainless steel,” in Proceedingds of the 6th Ulusal Talaşlı İmalat Sempozyumu Sabancı Üniversitesi, İstanbul, Turkey, 2015. View at: Google Scholar
  18. V. Varghese, D. Chakradhar, and M. R. Ramesh, “Micro mechanical characterization and wear performance of TiAlN/NbN PVD coated carbide inserts during end milling of AISI 304 austenitic stainless steel,” Materials Today: Proceedings, vol. 5, no. 5, pp. 12855–12862, 2018. View at: Publisher Site | Google Scholar
  19. A. Mavi, “Determination of optimum cutting parameters affecting the surface form properties in the ductile stainless steels with grey relational analysis method,” Gazi University Journal of Science Part C: Design and Technology, vol. 6, no. 3, pp. 634–643, 2018. View at: Google Scholar
  20. A. Uysal, “Investigation of surface roughness in MQL milling of 430 ferritic stainless steel by using nano MoS2 particle reinforced cutting fluid,” Dicle University Journal of Engineering, vol. 7, no. 1, pp. 151–158, 2016. View at: Google Scholar
  21. G. Basmaci, M. Ay, and I. Kirbas, “Optimisation of machining parameters in turning 17-4 Ph stainless steel using the grey-based Taguchi method,” Erzincan University Journal of Science and Technology, vol. 10, no. 2, pp. 243–254, 2017. View at: Google Scholar
  22. A. Selaimia, M. A. Yallese, H. Bensouilah, I. Meddour, R. Khattabi, and T. Mabrouki, “Modelling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach,” Measurement, vol. 107, no. 1, pp. 53–67, 2017. View at: Publisher Site | Google Scholar
  23. E. Kuram and B. Ozcelik, “Micro milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling,” Journal of Intellectual Manufacturing, vol. 27, no. 4, pp. 817–830, 2016. View at: Publisher Site | Google Scholar
  24. T. R. Lin, “Optimisation technique for face milling stainless steel with multiple performance characteristics,” International Journal of Advanced Manufacturing Technology, vol. 19, no. 5, pp. 330–335, 2002. View at: Publisher Site | Google Scholar
  25. T. R. Lin, “Experimental study of burr formation and tool chipping in the face milling of stainless steel,” Journal of Materials Processing Technology, vol. 108, no. 1, pp. 12–20, 2000. View at: Publisher Site | Google Scholar
  26. H. Shao, L. Liu, and H. L. Qu, “Machinability study on 3% Co–12% Cr stainless steel in milling,” Wear, vol. 263, no. 1–6, pp. 736–744, 2007. View at: Publisher Site | Google Scholar
  27. W. Y. H. Liew, “Low-speed milling of stainless steel with TiAlN single-layer and TiAlN/AlCrN nano-multilayer coated carbide tools under different lubrication conditions,” Wear, vol. 269, no. 7-8, pp. 617–663, 2010. View at: Publisher Site | Google Scholar
  28. W. Y. H. Liew and X. Ding, “Wear progression of carbide tool in low-speed end milling of stainless steel,” Wear, vol. 265, no. 1-2, pp. 155–166, 2008. View at: Publisher Site | Google Scholar
  29. M. Nordin, R. Sundstrom, T. I. Selinder, and S. Hogmark, “Wear and failure mechanisms of multilayered PVD TiN/TaN coated tools when milling austenitic stainless steel,” Surface and Coatings Technology, vol. 133-134, pp. 240–246, 2000. View at: Google Scholar
  30. T. I. Selinder, M. E. Sjostrand, M. Nordin, M. Larsson, A. Ostlund, and S. Hogmark, “Performance of PVD TiN/TaN and TiN/NbN super lattice coated cemented carbide tools in stainless steel machining,” Surface and Coatings Technology, vol. 105, no. 1-2, pp. 51–55, 1998. View at: Publisher Site | Google Scholar
  31. A. B. Junior, A. E. Diniz, and F. T. Filho, “Tool wear and tool life in end milling of 15–5 PH stainless steel under different cooling and lubrication conditions,” International Journal of Advanced Manufacturing Technology, vol. 43, no. 7-8, pp. 756–764, 2009. View at: Publisher Site | Google Scholar
  32. M. Nalbant and Y. Yildiz, “Effect of cryogenic cooling in milling process of AISI 304 stainless steel,” Transactions of Nonferrous Metals Society of China, vol. 21, no. 1, pp. 72–79, 2011. View at: Publisher Site | Google Scholar
  33. M. Fnides, M. A. Yallese, R. Khattabi, T. Mabrouki, and F. Girardin, “Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts,” International Journal of Industrial Engineering Computations, vol. 8, no. 4, pp. 493–512, 2017. View at: Publisher Site | Google Scholar
  34. https://www.ulbrich.com/uploads/data-sheets/Custom-450-Stainless-Steel-Wire-UNS-S45000.pdf.
  35. http://www.matweb.com/search/DataSheet.aspx?MatGUID=3da8f1cd94994fe8b34.
  36. A. Qasim, S. Nisar, A. Shah, M. S. Khalid, and M. A. Sheikh, “Optimization of process parameters for machining of AISI 1045 steel using Taguchi design and ANOVA,” Simulation Modelling Practice and Theory, vol. 59, no. 1, pp. 36–51, 2015. View at: Publisher Site | Google Scholar
  37. S. Sezek, B. Aksakal, and F. Karaca, “Ortopedik operasyonlardaki kemik delme işlemlerinde sıcaklık dağılım analizleri,” in Proceedings of the 6th International Advanced Technologies Symposium (IATS’11), Elazığ, Turkey, May 2011. View at: Google Scholar
  38. A. Duran, Y. Turgut, and M. Gunay, “Experimental measurement of tool-chip interface temperature with pyrometer in turning,” Journal of Polytechnic, vol. 14, no. 4, pp. 297–301, 2011. View at: Google Scholar

Copyright © 2019 Harun Gokce. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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