Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access February 9, 2023

Optimization of machining Nilo 36 superalloy parameters in turning operation

  • Gültekin Basmacı ORCID logo EMAIL logo
From the journal Open Chemistry

Abstract

In this study, the effects of cutting speed, tool geometry, and machining parameters on surface roughness and cutting forces in the turning of Nilo 36 superalloy have been investigated. An experimental design of the turning process was made with the Taguchi technique. In this way, optimum values were estimated with a small number of experiments. The grey correlation analysis method was used to determine the best parameter levels and plausible factors. While the most ideal value for the cutter with traditional geometry was achieved with experiment number 3, indicated by the GRA (grey relationship analysis) Rank value no. 1, the worst result was obtained with experiment number 9, and while the most ideal value for the cutter with wiper geometry was achieved with experiment number 2, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 6. Results showed that experiments made by GRA-based parameters give better results on surface roughness and cutting forces.

1 Introduction

Superalloys have a wide area of use in the manufacturing sector with features such as high corrosion resistance, high mechanical properties, and low thermal conductivity [1]. The use of superalloys, due to these remarkable features, has been widening in various areas such as food, health, chemistry, electronics, defense industry, nuclear plants, automotive, and aerospace industry [2]. The materials are hard to machine because of their low thermal conductivity and high mechanical properties and cause the heat and the cutting forces to increase earlier tool wear, harder chip removing, and bad surface roughness [3]. The energy that is used to cut the workpiece in turning is converted to heat in the first deformation zone. However, the heat generated while deformation is closely related to not only deformation energy but also tool–chip interface friction and cutting forces that are varied according to tool geometry and machining parameters [4]. Numerous experimental studies about the effects of cutting parameters on surface roughness and cutting forces, which occur during the machining of varied stainless steels, have been carried out. It has been found that feed rate is the most effective parameter on surface quality among parameters such as cutting speed, feed rate, and depth of cut, in their approach using multiple regression and artificial neural network on AISI 1040 steel [5]. The effects of feed, cutting speed, and cutting angle on surface roughness (R a) and vibrations (axial, radial, and tangential) in the experiments on turning AISI 5140 steel alloy were measured, and it was determined that the most effective parameter on surface roughness was the feed rate [6]. On the other hand, the effect of variable cutting parameters as well as cutter tool radius value on surface roughness was measured, using mathematical modeling, in hard turning of AISI D2 steel with cubic boron nitride cutting tools. It revealed that as the tool radius increases, a higher quality surface is obtained [7]. In a study on turning of CPM 10V steel, it has been found that the depth of cut was more effective on the main and radial force, while the feed and cutting speed were more effective on the feed force [8]. In another experimental study on turning 16MnCr5 steel using the polycrystalline cubic boron nitride tool, it has been found that the cutting speed causes a decrease in passive force, depending on the temperature increase in the cutting zone; it has been found that passive force and feed force components also increase, depending on the growth in the feed rate. In addition, it has been concluded that for low-value parameters, the passive force component would be much higher than the main force component [9]. In another experimental study, dry turning, liquid lubricant turning, and solid particle lubrication turning operations of AISI 4340 steel were compared, and it was found that machining with solid particle lubrication achieved approximately 38% better surface quality than machining with liquid particles [10]. In an experimental study and finite element method analysis on Nimonic 80A superalloy, it has been found that the most effective parameter on main force and feed force is the depth of cut, and the most effective parameter on radial force is the feed rate [11]. It has been noted that using a cutting tool, which has wiper geometry, while turning has good effects on surface roughness. If a wiper tool is used, it has been reported that no second processing is required, machining steps reduce thus processing time shortens, and surface quality of the workpiece gets better [12,13]. Nonetheless, more wiper surface causes cutting forces to increase. Additionally previous works have emphasized that wiper tool affects surface quality positively in a study comparing traditional and wiper tools [13].

Manufacturing time, amount, and economy are not enough for manufacturing process to be sustainable. Along with these, it should be directly related to human health and environmental pollution. Chip-removing process has been carried out with the environmental pollution and human health-friendly cooling techniques. These alternative techniques have better performances than traditional cooling techniques [14]. It has been pointed out that MQL (minimum quantity lubrication) technique that improves surface quality by reducing the heat between workpiece-chip and tool-chip has significant outputs [15]. Nitrogen and carbon dioxide were used to cool the cutting area in an experimental study, and it has been specified that edge wear decreased 55% after cooling with liquid nitrogen [16]. It has been reported that machining with MQL improves surface quality [17]. It has been pointed out that the heat between tool-chip and workpiece-chip, and in the cutting area has good effects on tool wear and surface quality [18]. A turning experiment has been carried out on Inconel X-750 (UNS N07750/W. Nr 2.4669) nickel-based superalloy under dry, MQL, cryogenic CO2, and cryogenic LN2 conditions and in various cutting speeds. As a conclusion, it is obtained that cryogenic CO2 method has the best surface quality, cutting forces, tool wear, and MQL technique increased surface hardness although it has the best chip formation [19]. The effects of cutting speed, cooling system, and cutting methods on cutting forces and surface roughness on turning of Nilo 36 steels have been investigated. In order to determine the effect of each parameter on the results obtained, the parameters were evaluated with the analysis of variance method. The most effective factors on surface quality are MQL (57.82%) and feed rate (31.95%); besides, the most influential parameters on cutting force are cutting speed (63.17%) and feed rate (27.84%). In this study, the effects of feed rate, depth of cut, cooling method on surface roughness, and cutting forces on turning of Nilo 36 superalloy with traditional cutters and cutters that have wiper geometry were studied. Taguchi grey analysis technique has been used in order to optimize the turning process. In this way, the effects of cutting speed, tool geometry, and machining conditions on surface roughness and cutting forces have been determined in the turning of Nilo 36 superalloy material [20].

2 Materials and methods

Nilo 36 superalloy (Table 1) with a length of 140 mm and a diameter of 50 mm was used in this experiment. Traditional cutters and wiper tools that are produced by Sandvik Company, named CNMG 12 04 08, were used in the chip-removing process. Experiments were performed on Johnford TC 35 CNC Fanuc OT xz axis lathe. M1-type perthometer by Mahr Company for surface roughness detector, KISTLER 9121-type force sensor, KISTLER 5019b-type load amplifier, and DynoWare analysis program were used (Figure 1).

Table 1

Material properties

Alloy Ni% Fe% Others
NILO alloy 36 36.0 64.0
Density
 Alloy g/cm3 lb/in3
 NILO alloy 36 8.11 0.293
Thermal conductivity at 20°C (68°F)
 Alloy W/m°C Btu in/ft2 h °F
 NILO alloy 36 10.0 69.3
Electrical resistivity
 Temperature (µΩ cm (ohm.circ mil/ft))
  °C °F NILO alloy 36
  20 68 80 (481)
Typical mechanical properties of NILO alloy 36
Yield strength (0.2% offset) Tensile strength
MPa Ksi MPa ksi
240 35.0 490 71.0
Elastic modulus data
Elastic modulus
 Alloy GPa 103 ksi
 NILO alloy 36 140 20.3
Figure 1 
               Experimental setup.
Figure 1

Experimental setup.

Three different cutting speeds and cooling methods has been crossed andexperimented (Table 2).

Table 2

Test parameters

Parameters Level 1 Level 2 Level 3
Cutting speed (m/min) 100 140 180
Cooling system Dry MQL CO2

3 Taguchi design

Hence, the design of the experiment was made with Taguchi mixed design technique, and it is possible to obtain more comprehensive results with fewer experiments. Thus, time was saved and costs were reduced. While determining quality characteristics,since the surface roughness and cuttingforce to be as low as possible, means best quality. The parameter levels of the experiments performed according to the Taguchi L9 orthogonal array are given in Table 3.

Table 3

Taguchi L9 experiment design

Experiment no. Cutting speed (m/min) Cooling system
1 100 Dry
2 100 MQL
3 100 CO2
4 140 Dry
5 140 MQL
6 140 CO2
7 180 Dry
8 180 MQL
9 180 CO2

4 Grey relationship analysis (GRA)

GRA is one of the subheadings of grey modeling. GRA is a method for determining the degree of relationship between each factor in a grey system and the factor series being compared. Each factor is defined as a sequence.

Step 1: Let the reference series of length n be as follows:

(1) x 0 = ( x 0 ( 1 ) , x 0 ( 2 ) , x 0 ( 3 ) , x 0 ( n ) ) .

Step 2: Normalization of the data.

During the normalization of factor series, it should be paid attention which one of the criteria represents the features of the series, “the higher, the better,” “the lower, the better,” or “the most ideal is the best.” In the case of “the higher, the better,” the normalization of the data is as follows:

(2) x i ( k ) = x i 0 ( k ) min x i 0 ( k ) max x i 0 ( k ) min x i 0 ( k ) ,

where X i (k) is the original value in kth order, x i (k) is the value in kth order after the normalization, min x 0(k) is the minimum value in the series, and max x i (k) is the maximum value in the series.

For “the lower, the better”, the normalization of data is as follows:

(3) X i ( k ) = max ( x i 0 ( k ) ) ( x i 0 ( k ) ) max ( x i 0 ( k ) ) min ( x i 0 ( k ) ) .

For “the most ideal is the best”, it is as follows:

(4) x i ( k ) = 1 x i 0 ( k ) x 0 max x i 0 ( k ) x 0 ,

where x 0 symbolizes the wanted ideal value.

Step 3: Let the m number of series to be compared with the x 0 series be defined as follows:

(5) x i = ( x i ( 1 ) , x i ( 2 ) , x i ( 3 ) , , x i ( n ) ) , i = 1 , 2 , 3 , , m ) .

Step 4: Let k indicate the kth order in the n length series ɛ (x 0(k), x i (k)) is the grey relational coefficient at kth point, and it is calculated according to the following equations:

(6) ε ( x 0 ( k ) , x i ( k ) ) = Δ min + ξ Δ min Δ 0 i ( k ) + ξ Δ max ,

(7) Δ 0 i ( k ) = x 0 ( k ) x j ( k ) ,

(8) Δ min = min j min k x 0 ( k ) x j ( k ) ,

(9) Δ max = max j max k x 0 ( k ) x j ( k ) .

It is a coefficient in ξ∈ (0,1). The function of j = 1,2,…m; k = 1,2,…,n. ξ is to set the difference between Δ 0 j and Δ max. Studies show that ξ value does not affect the ranking that will occur after the grey relational degree.

Step 5: Finally, the grey relational degree is calculated by the following equation:

(10) γ ( x 0 , x i ) = 1 n k = 1 n ε ( x 0 ( k ) , x i ( k ) ) ,

where γ(x 0, x i ) in a grey system x i series, and x 0 reference series is an n measure of geometric similarity. The size of grey relational degree is an n indication that there is a strong relationship between x i and x 0. If the two series to be compared are identical, the grey relational degree value is 1. The grey relational degree indicates how similar the series is to the reference series.

If weights of each criterion are given, the grey correlation coefficient of the criterion can be multiplied by the weight value of the criterion to find the degree of significance of the criterion. This is calculated according to the following equation:

(11) γ ( x 0 , x i ) = 1 n k = 1 n ε ( x 0 ( k ) , x i ( k ) . ( W i ( k ) ) ) .

In the decision-making process, if the reference series is selected as the highest, lowest, and most ideal values for the criteria, the grey relational degree of the factor series to be calculated in accordance with the reference series will be an indication of the capture level of criteria. In other words, the factor series with the highest grey relational degree (alternative) will constitute the best decision alternative in the decision-making problem.

5 Results and discussion

5.1 Evaluation of surface roughness results

In general, the obtained roughness value was between 0.405 and 2.618 µm, which meets the expectations. The surface roughness values obtained as a result of those 18 experiments are shown in Figures 2 and 3. As can be seen in the graph and the figure, the surface roughness values as a result of machining with the wiper tool were better than the results obtained with the conventional insert. This result also coincides with that of the literature [13,14]. Here, the insert geometry affects not only the surface quality but also the other machining parameters such as feed, depth of cut, and cooling system.

Figure 2 
                  Surface roughness results.
Figure 2

Surface roughness results.

Figure 3 
                  Cutting force results.
Figure 3

Cutting force results.

5.2 Evaluation of cutting force results

In general, the obtained cutting force value was between 104.1 and 259.76N, which meets the expectations. The cutting force values obtained as a result of those 18 experiments are shown in Figure 3. As can be seen in the graph, the cutting force values after machining with the traditional insert were lower than the values obtained with the wiper insert. This result also coincides with that of the literature [13,14]. Here, the insert geometry affects not only the surface quality but also the other machining parameters such as feed, depth of cut, and cooling system, which has an effect. Other studies show that depth of cutting and cutting forces have maximum effects on surface quality after geometry effect, which confirms our experimental results [15,17,21,22,23].

5.3 Optimization of parameters with grey correlation analysis method

The GRA values obtained after the GRA between the results obtained from the experiments with conventional and wiper tools are given in Tables 4 and 5. According to this, for the cutter with conventional geometry, while the optimum value was obtained with experiment number 3, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 9 (Table 3).

Table 4

Normalized data, delta values, and grey relational grade for conventional insert tool

Experiment no Cutting force (N) Surface roughness (µm) Normalization values Aggregate values Grey relational degree
Cutting force Surface roughness Cutting force Surface roughness GRA values GRA rank
1 104.46 0.427 0.992 0.902 0.984 0.836 0.910 2
2 122.733 0.488 0.585 0.629 0.547 0.574 0.560 4
3 104.1 0.42 1.000 0.933 1.000 0.882 0.941 1
4 129.46 0.619 0.436 0.045 0.470 0.344 0.407 7
5 132.8 0.629 0.361 0.000 0.439 0.333 0.386 8
6 145.66 0.405 0.075 1.000 0.351 1.000 0.675 3
7 129.1 0.567 0.444 0.277 0.473 0.409 0.441 5
8 132.06 0.586 0.378 0.192 0.446 0.382 0.414 6
9 149.03 0.622 0.000 0.031 0.333 0.340 0.337 9
Table 5

Normalized data, delta values, and grey relational grade for wiper insert tool

Experiment no Cutting force (N) Surface roughness (µm) Normalization values Aggregate values Grey relational degree
Cutting force Surface roughness Cutting force Surface roughness GRA values GRA rank
1 210.03 1.377 0.319 0.629 0.423 0.574 0.499 6
2 103.63 0.811 1.000 0.915 1.000 0.855 0.928 1
3 259.76 1.468 0.000 0.583 0.333 0.545 0.439 7
4 181.93 1.094 0.498 0.772 0.499 0.687 0.593 4
5 191.1 1.402 0.440 0.616 0.472 0.566 0.519 5
6 234.1 2.618 0.164 0.000 0.374 0.333 0.354 9
7 138.8 1.004 0.775 0.818 0.689 0.733 0.711 3
8 130 0.644 0.831 1.000 0.747 1.000 0.874 2
9 257.8 1.551 0.013 0.541 0.336 0.521 0.429 8

As can be seen in Table 5, while the optimum value for the cutter with wiper geometry was obtained with experiment number 1, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 5.

The grey relational degrees related to each experiment result were calculated, and the experiment results were ranked in order from highest grey relational degree to present in Table 6 for conventional insert tool.

Table 6

Grey relational degrees of the factor levels for conventional insert tool

Levels Speed (m/min) Cooling system
Level 1 0.804 0.586
Level 2 0.489 0.454
Level 3 0.397 0.651

As shown in Table 6, feed: 0.1 mm/rev, depth of cut: 0.5 mm, and cooling system (carbon dioxide) were selected as the optimal parameter levels on the results. The optimal parameter levels will represent the lowest surface roughness and cutting force value.

The grey relational degrees related to each experiment result were calculated, and the results of the experiments were ranked in order from highest grey relational degrees to present in Table 6 for the wiper insert tool.

As shown in Table 7, feed: 0.1 mm/rev, depth of cut: 0.5 mm, and cooling system (dry) were selected as the optimal parameter levels on the results. The optimal parameter levels will represent the lowest surface roughness and cutting force value.

Table 7

Grey relational degrees of the factor levels for wiper insert tool

Levels Speed (m/min) Cooling system
Level 1 0.622 0.601
Level 2 0.488 0.773
Level 3 0.671 0.407

In Figure 4, experiment numbers and corresponding GRA values are shown as line graphs. According to this, while the optimum value for the cutter with traditional geometry was achieved with experiment number 3, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 9, and while the optimum value for the cutter with wiper geometry was achieved with experiment number 1, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 5.

Figure 4 
                  GRA for experiment chart.
Figure 4

GRA for experiment chart.

6 Conclusions

In this study, Nilo 36 superalloy that has many uses in many sectors such as the defense and aerospace industry was machined on a lathe. Machining parameters such as feed, depth of cut, cooling system, a cutting tool with conventional and wiper geometry, and cutting forces and surface roughness values during machining were measured, and the main parameters that have an effect on these values were determined using statistical methods [24,25]. In different materials for different purposes, a number of different works are available in the literature [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46].

First, Taguchi experiment design was made and the number of measurements and parameter values required for a small number of experiments was determined. Optimum values were determined by using the GRA method to determine the best parameter levels after surface roughness and cutting force measurements.

As a result of the experiments carried out with the cutting tool with conventional and wiper geometry, surface roughness values varying between 0.405 and 2.618 µm and cutting force values of 220.19–1866.75N were obtained.

For the cutter with conventional geometry, while the optimum value was obtained with the experiment number 3, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 9.

While the optimum value for the cutter with wiper geometry was obtained with experiment number 1, indicated by the GRA Rank value no. 1, the worst result was obtained with experiment number 5.

  1. Funding information: There is no funding for this study.

  2. Conflict of interest: The author declares no conflict of interest.

  3. Ethical approval: The conducted research is not related to either human or animals use.

  4. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

[1] Kosa T, Ronald P. Machining of stainless steels. In ASM Handbook Committee. Machining. Vol. 16, 9th edn. Ohio: American Society for Metals (ASM) International; 1989; p. 103–43.Search in Google Scholar

[2] M’Saoubi R, Outeiro JC, Changeux B, Lebrun JL, Morão Dias A. Residual stress analysis in orthogonal machining of standard and resulfurized AISI 316L steels. J Mater Process Technol. 1999;96:225–33. 10.1016/s0924-0136(99)00359-3.Search in Google Scholar

[3] Maranhão C, Paulo Davim J. Finite element modelling of machining of AISI 316 steel: Numerical simulation and experimental validation. Simul Model Pract Theory. 2010;18:139–56. 10.1016/j.simpat.2009.10.001.Search in Google Scholar

[4] Korkut I, Boy M, Karacan I, Seker U. Investigation of chip-back temperature during machining depending on cutting parameters. Mater Des. 2007;28:2329–35. 10.1016/j.matdes.2006.07.009.Search in Google Scholar

[5] Asiltürk İ, Çunkaş M. Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl. 2011;38:5826–32. 10.1016/j.eswa.2010.11.041.Search in Google Scholar

[6] Kuntoğlu M, Aslan A, Pimenov DY, Giasin K, Mikolajczyk T, Sharma S. Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel. Materials. 2020;13:4242. 10.3390/ma13194242.Search in Google Scholar PubMed PubMed Central

[7] Patel VD, Gandhi AH. Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool. Measurement. 2019;138:34–8. 10.1016/j.measurement.2019.01.077.Search in Google Scholar

[8] Aleksic A, Rodic D, Sekulic M, Gostimirovic M, Savkovic B. Effect of cutting parameters on surface roughness in turning of CPM 10V steel. 2022 21st International Symposium Infoteh-Jahorina (Infoteh). IEEE; 2022.10.1109/INFOTEH53737.2022.9751329Search in Google Scholar

[9] Kundrák J, Szabó G, Markopoulos AP. Experimental and numerical ınvestigation of the ınfluence of cutting speed and feed rate on forces in turning of steel. Mater Sci Forum. 2016;862:270–7. 10.4028/www.scientific.net/msf.862.270.Search in Google Scholar

[10] Sharma R. The effect of cutting fluid on surface roughness of AISI 4340 steel during turning operation. Int J ChemTech Res. 2018;5:227–30. 10.20902/ijctr.2018.110325.Search in Google Scholar

[11] Korkmaz ME, Yaşar N, Günay M. Numerical and experimental investigation of cutting forces in turning of Nimonic 80A superalloy. Eng Sci Technol Int J. 2020;23:664–73. 10.1016/j.jestch.2020.02.001.Search in Google Scholar

[12] Ay M, Basmaci G. Investigation of the effects of conventional and wiper coated carbide tools with dry cutting on cutting forces, surface roughness and material hardness in turning 17-4 PH stainless steel. Online J Sci Technol. 2016;6:33–9.Search in Google Scholar

[13] Basmaci G, Ay M. Optimization of cutting parameters, condition and geometry in turning AISI 316L stainless steel using the grey-based Taguchi method. Acta Phys Polonica A. 2017;131:354–9. 10.12693/aphyspola.131.354.Search in Google Scholar

[14] Basmaci G. Tornalamada minimum miktarda yağlama (mql) tekniğinin takım ve iş parçası üzerine etkilerinin incelenmesi; 2012.Search in Google Scholar

[15] Han R, Liu J, Sun Y. Research on experimentation of green cutting with water vapor as coolant and lubricant. Ind Lubr Tribol. 2005;57:187–92. 10.1108/00368790510614154.Search in Google Scholar

[16] Stanford M, Lister PM, Morgan C, Kibble KA. Investigation into the use of gaseous and liquid nitrogen as a cutting fluid when turning BS 970-80A15 (En32b) plain carbon steel using WC–Co uncoated tooling. J Mater Process Technol. 2009;209:961–72. 10.1016/j.jmatprotec.2008.03.003.Search in Google Scholar

[17] Koklu U, Basmaci G. Evaluation of tool path strategy and cooling condition effects on the cutting force and surface quality in micromilling operations. Metals. 2017;7:426. 10.3390/met7100426.Search in Google Scholar

[18] Basmaci G, Yoruk A, Koklu U, Morkavuk S. Impact of cryogenic condition and drill diameter on drilling performance of CFRP. Appl Sci. 2017;7:667. 10.3390/app7070667.Search in Google Scholar

[19] Tasbasi M, Ay M, Etyemez A. Quality in turning of Inconel X-750 superalloy. Emerg Mater Res. 2020;9:1154–62. 10.1680/jemmr.20.00194.Search in Google Scholar

[20] Basmaci G, Kirbaş İ, Ay M. Modelling of cutting parameters for Nilo 36 superalloy with machine learning methods and developing an interactive interface. Int Adv Res Eng J. 2021;5:79–86. 10.35860/iarej.805124.Search in Google Scholar

[21] Ay M, Altunpak Y, Hartomacıoğlu S. The grey-based Taguchi method: Optimisation of drilling of hybrid aluminum matrix composites. Acta Phys Polonica A. 2017;131:551–5. 10.12693/aphyspola.131.551.Search in Google Scholar

[22] Ay M, Etyemez A. Optimization of the effects of wire EDM parameters on tolerances. Emerg Mater Res. 2020;9:1–5. 10.1680/jemmr.20.00076.Search in Google Scholar

[23] Sreenivasulu R, Ch. Rao S. Optimization of machining parameters during end milling of super alloys using Grey based Taguchi method coupled with entropy measurement technique. J Mech Energy Eng. 2020;4:47–56. 10.30464/jmee.2020.4.1.47.Search in Google Scholar

[24] Salem A, Hegab H, Kishawy HA. An integrated approach for sustainable machining processes: Assessment, performance analysis, and optimization. Sustain Prod Consum. 2021;25:450–70. 10.1016/J.SPC.2020.11.021.Search in Google Scholar

[25] Wei K, Yang Q, Ling B. Mechanical properties of Invar 36 alloy additively manufactured by selective laser melting. Mater Sci Eng A. 2020;772:138799. 10.1016/J.MSEA.2019.138799.Search in Google Scholar

[26] Arbouz H. Modeling of a tandem solar cell structure based on CZTS and CZTSe absorber materials. Int J Comput Exp Sci Eng. 2022;8(1):14–8. 10.22399/ijcesen.843038.Search in Google Scholar

[27] Iskender A. Effective atomic numbers for Fe–Mn alloy using transmission experiment. Chin Phys Lett. 2007;24:2812–4. 10.1088/0256-307X/24/10/027.Search in Google Scholar

[28] Caymaz T, Çalışkan S, Botsalı AR. Evaluation of ergonomic conditions using fuzzy logic in a metal processing plant. Int J Comput Exp Sci Eng. 2022;8(1):19–24. 10.22399/ijcesen.932994.Search in Google Scholar

[29] ALMisned G, Baykal DS, Kilic G, Susoy G, Zakaly HMH, Ene A, et al. Assessment of the usability conditions of Sb2O3-PbO-B2O3 glasses for shielding purposes in some medical radioisotope and a wide gamma-ray energy spectrum. Appl Rheolo. 2022;32(1):178–89. 10.1515/arh-2022-0133.Search in Google Scholar

[30] Ural A, Kilimci ZH. The prediction of chiral metamaterial resonance using convolutional neural networks and conventional machine learning algorithms. Int J Comput Exp Sci Eng. 2021;7(3):156–63. 10.22399/ijcesen.973726.Search in Google Scholar

[31] Akkurt I. Effective atomic and electron numbers of some steels at different energies. Ann Nucl En. 2009;36(11–12):1702–5. 10.1016/j.anucene.2009.09.005.Search in Google Scholar

[32] Çilli A, Beken M, Kurt N. Determination of Theoretical Fracture Criteria of Layered Elastic Composite Material by ANFIS Method from Artificial Intelligence. Int J Comput Exp Sci Eng. 2022;8(2):32–9. 10.22399/ijcesen.1077328.Search in Google Scholar

[33] ALMisned G, Baykal DS, Susoy G, Kilic ,G, Zakaly HMH, Ene A, et al. Determination of gamma-ray transmission factors of WO3–TeO2–B2O3 glasses using MCPX Monte Carlo code for shielding and protection purposes. Appl Rheol. 2022;32:1–177. 10.1515/arh-2022-0132.Search in Google Scholar

[34] Demir N, Kıvrak A, Üstün M, Cesur A, Boztosun İ. Experimental study for the energy levels of europium by the clinic LINAC. Int J Comput Exp Sci Eng. 2017;3(1):47–9.Search in Google Scholar

[35] Karaali R and Keven A. Evaluation of four different cogeneration cycles by using some criteria. Appl Rheol. 2022;32(1):122–37. 10.1515/arh-2022-0128.Search in Google Scholar

[36] Waheed F, İmamoğlu M, Karpuz N, Ovalıoğlu H. Simulation of neutrons shielding properties for some medical materials. Int J Comput Exp Sci Eng. 2022;8(1):5–8. 10.22399/ijcesen.1032359.Search in Google Scholar

[37] Sarihan M. Simulation of gamma-ray shielding properties for materials of medical interest. Open Chem. 2022;20(1):81–7. 10.1515/chem-2021-0118.Search in Google Scholar

[38] Arslankaya S, Çelik MT. Prediction of heart attack using fuzzy logic method and determination of factors affecting heart attacks. Int J Comput Exp Sci Eng. 2021;7(1):1–8. 10.22399/ijcesen.837731.Search in Google Scholar

[39] Safiddine S, Amokrane K, Debieb F, Soualhi H, Benabed B, Kadri E. How quarry waste limestone filler affects the rheological behavior of cement-based materials. Appl Rheol. 2021;31(1):63–75. 10.1515/arh-2020-0118.Search in Google Scholar

[40] Şen Baykal D, Tekin H, Çakırlı Mutlu R. An investigation on radiation shielding properties of borosilicate glass systems. Int J Comput Exp Sci Eng. 2021;7(2):99–108. 10.22399/ijcesen.960151.Search in Google Scholar

[41] Tan T, Zhao Y, Zhao X, Chang L, Ren S. Mechanical properties of sandstone under hydro-mechanical coupling. Appl Rheol. 2022;32(1):8–21. 10.1515/arh-2022-0120.Search in Google Scholar

[42] Tekin HO, Cavli B, Altunsoy EE, Manici T, Ozturk C, Karakas HM. An investigation on radiation protection and shielding properties of 16 Slice computed tomography (CT) facilities. Int J Comput Exp Sci Eng. 2018;4(2):37–40. 10.22399/ijcesen.408231.Search in Google Scholar

[43] Etyemez A. Structural, physical, and mechanical properties of the TiO2 added hydroxyapatite composites. Open Chem. 2022;20(1):272–6. 10.1515/chem-2022-0140.Search in Google Scholar

[44] Oruncak B. Gamma-ray shielding properties of Nd2O3 added iron-boron-phosphate based composites. Open Chem. 2022;20(1):237–43. 10.1515/chem-2022-0143.Search in Google Scholar

[45] Özseven A. Assessment of using electronic portal imaging device for analysing bolus material utilised in radiation therapy. Open Chem. 2022;20(1):61. 10.1515/chem-2022-0126.Search in Google Scholar

[46] Salima B, Seloua D, Djamel F, Samir M. Structure of pumpkin pectin and its effect on its technological properties. Appl Rheol. 2022;32(1):34–55. 10.1515/arh-2022-0124.Search in Google Scholar

Received: 2022-12-04
Revised: 2022-12-25
Accepted: 2023-01-02
Published Online: 2023-02-09

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 24.2.2024 from https://www.degruyter.com/document/doi/10.1515/chem-2022-0276/html
Scroll to top button