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За допомогою aHaMÍ3y поверхн вдуку (RSM) виршуеться задача оптимiзацii шорсткостi поверхт (Ra) та зносу 6Í4HÚÍ поверхт (VB) в про-цеЫ обробки з використанням ридини на основi алое вера (AVCF). Для цього на основi реалiзацii двадцяти циклiв експериментального випробу-вання отримано рiвняння множинноi квадратич-ноi регреси, в якш за вхiдт змшш обрано швид-тсть подачi (f), глибина рiзання (a) та тип AVCF (1, 2 i 3). Отримано оптимальний про-цес з наступними параметрами: f= 0,140 мм/об, a=2,0556 мм, AVCF= 71,8970 cSt, що забезпечують м^мум Ra та f= 0,20 мм/об, a=2,50 мм, а AVCF= = 8,8050 cSt - забезпечують мiнiмум VB
Ключовi слова: Optimization, Aloe Vera, Flank
Wear, Surface Roughness
□-□
С помощью анализа поверхности отклика (RSM) решается задача оптимизации шероховатости поверхности (Ra) и износа боковой поверхности (VB) в процессе обработки с использованием жидкости на основе алоэ вера (AVCF). Для этого на основе реализации двадцати циклов экспериментального испытания получено уравнение множественной квадратичной регрессии, в которой за входящие переменные выбрана скорость подачи (f), глубина резания (a) и тип AVCF (1, 2 и 3). Получено оптимальный процесс со следующими параметрами: f= 0,140 мм/об, a=2,0556мм, AVCF= 71,8970 cSt, которые обеспечивают минимум Ra и f= 0,20 мм/об, A=2,50 мм, а AVCF =8,8050 cSt - обеспечивают минимум VB
Ключевые слова: Optimization, Aloe Vera, Flank Wear, Surface Roughness
UDC 616 058+616 036.86+613.95
|DOI: 10.15587/1729-4061.2018.123223
aloe vera as cutting fluid optimization using response surface
method
Priyagung Hartono
Master of Engineering Department of Mechanical Engineering University of Islam Malang Jalan. M. T. Haryono, 193, Malang, Indonesia, 65144 Е-mail: [email protected] prati kto Doctoral student, Professor* Е-mail: [email protected] Agus Suprapto Doctoral student, Professor Department of Mechanical Engineering University of Merdeka Malang Jalan. Terusan Raya Dieng, 62-64, Malang, Indonesia, 65146 Е-mail: [email protected] Yudy Surya Irawan Doctoral student* Е-mail: [email protected] *Department of Mechanical Engineering Brawijaya University Malang Jalan. Mayjend Haryono, 167, Malang, Indonesia, 65145
1. Introduction
Cutting fluids are used in the turning process to decrease the tool wear rate and surface roughness of the turning product. The most common types of cutting fluids are oil-based and water-miscible fluids. Recently, due to environmental issues, cutting fluid alternatives have been developed, such as vegetable oil-based options. The development of the turning process is accelerating and increasingly complex in line with increasing demand and increasing product quality and decreasing production costs. Factors affecting the cutting process include the type of workpiece material, the type of cutting tool, the cutting conditions (cutting speed, depth of cut and feeding), cutting fluid, type of machining process used. Based on the above, especially the eco-friendly technology challenge, a study of the utilization and potential of aloe vera as a cutting fluid will be developed.
Therefore, the aim of the present study was to optimize variables of the turning process with Aloe Vera as cutting fluid such as feeding, depth of cut and Aloe Vera cutting fluid type using response surface methodology.
2. Literature review and problem statement
The study of vegetable oil-based cutting fluids as an environmentally friendly mode of machining was performed with similar performance obtained using mineral oil-based metal-working fluids in [1]. Bio-oils using in machining with palm oil for turning, mixtures of solid lubricants such as boric acid in palm oil in Ti-6Al-4V alloys were studied at the cutting process. Experimental results on the performance of three cutting conditions such as dry, palm oil, blend of palm oil and boric acid lubricating in terms of surface roughness were presented. The result of optimal cutting parameters with Taguchi methods was studied in [2]. In the next study, vitamin C from a rich lemon fruit extract was used as an antioxi-dant to improve the oxidative stability of the vegetable oils. Based on this study, groundnut oil and palm oil were recommended as alternative lubricants to the mineral oil during machining of mild steel [3]. The observations of uniform flank wear, micro-chipping, thermal cracking, and flaking on the MQL palm oil (MQLPO) were provided in the highspeed drilling of Ti-6Al-4V [4]. In order to reduce pollutant
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emissions in the field of cutting techniques, research on Minimum Quantity Lubrication (MQL) and Cooling (MQC) has been found and developed. This study aims to investigate the machinability of Ti-48Al-2CR-2NB (at. %) alloys. The results of the evaluation of wear and tear of cutting tools and cutting tool life, surface quality, lubricant consumption, and environmental impact are examined with reference to MQL, wet and dry cutting [5]. 100 ml/hr or less is often used for MQL cooling. A discussion of the optimization of process parameters on surface roughness occurring on Ti-6Al-4V with different cooling conditions such as dry, flooded and Minimum Quantity Lubrication, different cutting tools such as CVD coating equipment, PVD coated equipment and un-coated tools using the methodology Taguchi design was provided. The result for the MQL conditions is minimum surface roughness compared to dry and flood conditions. The optimum conditions occur at medium cutting speed, low feed rate and high cutting depth. Observation of wear with optical images shows lower wear compared to dry and stagnant machines [6]. Trends in environmental issues and the development of regulations on contamination and pollution, the challenge of producing renewable and biodegradable cooling fluids are important issues today. Understanding of the type of cutting fluid and machining process parameters is a skill and analysis that must be mastered to maximize the efficiency of cutting fluid in the machining process. Generally, the heat generated occurs in the cutting zone due to friction on the chip-interface interface, and friction between the clearance of the tool surface and the workpiece that determines the surface quality of the workpiece. The growing research on bio-based cooling fluids has significantly reduced the ecological problems caused by mineral-based cutting fluids in [7]. The effect of various levels of cutting fluid and cutting parameters on surface roughness and wear of the chisel has been studied. Optimization by utilizing the Taguchi method is used to minimize the number of experiments. Some research results indicate that feeding becomes the dominant factor contributing 34.3 % to surface roughness. The flow rate of the cutting fluid also shows a significant contribution (33.1 %). However, cutting speed and cutting depth show a little contribution to surface roughness. On the other hand, cutting speed (43.1 %) and cutting depth (35.8 %) were the dominant factors affecting the life of the cutting tool. However, the application of cutting with coolant (13.7 %) shows considerable contribution. Optimum cutting conditions to reduce surface roughness at high cutting speed, intermediate level of cutting depth, low level of feeding and coolant fluid velocity have also been studied intensively [8]. Titanium alloys are very difficult to cut because they have high strength and high resistance to heat. Excessive heat is due to poor thermal conductivity near the cutting edge, thus accelerating the wear of cutting tool. It is required to reduce heat effectively for cooling, in order to implement high cutting speed. The measurement of the high effect and the effect of carbon dioxide snow (CO2-snow) as innovative cooling methodologies in machining of three tempers of b-titanium alloy is described in the paper. As cooling techniques, the following were used: conventional flood emulsion; impingement of jet of CO2-snow at the rake face, the flank face, the rake and flank faces together; and the combination of the CO2-jet and MQL. The evaluation takes place on cutting forces, tool wear, and acoustic emission as an indicator to measure differences in terms of the chip morphology [9]. The research results showed that the MQL condition, which has cooling and lubricating effects, was found to have a more
significant influence in improving the tool life as compared to dry condition. Furthermore, the (nc-AlTiN)/(a-Si3N4) coated tool was confirmed to be more suitable for machining of titanium alloy than (nc-AlCrN)/(a-Si3N4) coated tool under MQL condition, which emphasizes the significance of matching between cutting fluids and coating materials. The slower wear rate of (nc-AlTiN)/(a-Si3N4) coated tool in the MQL condition was obtained than that of the (nc-AlCrN)/ (a-Si3N4) coated tool. As a result, the MQL condition can greatly prolong the tool life of (nc-AlTiN)/(a-Si3N4) coated tool while has minor influence on improving the tool life of (nc-AlCrN)/(fl-Si3N4) coated tool. Adhesive wear was observed to be the main wear type. The MQL technique not only has cooling and lubricating effects on Nano-composite coated tool, but also helps to form a powerful protective layer. In addition, in the MQL condition, the (nc-AlTiN)/(a-Si3N4) coated tool only suffered adhesive wear while the (nc-AlCrN)/(a-Si3N4) coated tool suffered adhesive, diffusion and oxidation wear [10]. The power from a cutting process of the metal is converted to heat at the edges of the cutting tool. To improve the tribological characteristics of the machining process and to dissipate the heat generated, the use of cutting fluids is required. Conventional cutting fluids are still being questioned about adverse impacts on the environment and human health. Therefore, trends are geared towards alternatives such as vegetable oils (VOs). VOs offer a combination of both high bio-degradability and lubrication, environmental friendliness and compatibility with additives, low toxicity and volatility, high flash point and high viscosity index. Impacts of VOs-based cutting fluids, cutting tool materials and working conditions were investigated. Two sets of experimental plans designed consisting of 25 and 27 tests with variance analysis (ANOVA) were used to evaluate the effect of process variables on Ra and wear. In general, Ra surface roughness ranges from 0.56 |im and 1.81 |im and statistical analysis shows that the main factor for Ra is the Process Capability ratio (PCR) or Process Capability Ratio of 94.4 % feeding. A noticeable increase in wear of the edges of the chisel is recorded when high cutting speeds are used [11]. Cutting fluids provide a potential to reduce the environmental effects. This study discusses aloe vera cutting fluids (AVCFs) as an environmentally-friendly alternative solution to using oil-based cutting fluids (OBCFs). Observations by measuring the flank wear of the cutting tool were carried out. The flank wear result of AVCF was 0.311 mm and that of OBCF was 0.284 mm, while that of dry machining was 0.576 mm. Flank wear of the cutting tool using AVCF is almost equivalent to that of the cutting tools using OBCFs. Thus, AVCF has a great potential as an environmentally friendly cutting fluid compared to OBCFs [12]. From several studies on the cutting fluids that have been done, an environmentally friendly alternative cutting fluid is required. The determination of the turning process parameters and the use of alternative liquids from the plant will help optimize the process and in maintaining the wear level of the cutting tool to produce a product with a good surface. The response surface methodology (RSM) method is required in solving modeling and analysis problems responsively to obtain optimal results.
3. The aim and objectives of the study
In the earlier study, aloe vera was investigated as a cutting fluid during the turning process as a challenge study to
reduce the environmental effects. Aloe vera has potential as a standard cutting fluid.
We solved the following tasks to achieve the objective:
- to find the effect of cutting parameters on surface roughness and flank wear;
- to receive the RSM model of surface roughness (Ra) and flank wear (VB);
- to perform a parametric study and predict the optimum parameters of cutting using Aloe Vera as alternative cutting fluid.
4. Materials and methods of research
The research method used in this study was a true experimental research method used to determine the effect of turning process parameters: cutting speed, depth of cut, and type of cutting fluid, which affects the flank wear and surface roughness. The turning process used was wet cutting. The test material used was ST42 steel with the following mechanical properties: yield strength 396.9 MPa and tensile strength 661.0 MPa and chemical properties of ST42 (Table 1, 2). The specimen geometry is a solid cylinder with a diameter of 38 mm and a length of 500 mm (Fig. 1).
End cutting edge angle
Table 1
Mechanical Properties of workpiece
Material St.42
Mechanical properties of the workpiece:
Yield Strength (cv, MPa) 396.9
Tensile Strength (au, MPa) 661.0
Elongation (£, %) 20.50
Reduction of Area (8, % ) 39.58
Table 2
Chemical Properties of workpiece
Name C Si Mn P S Fe
St.42 0.21 % 0.46 % 1.35 % 0.05 % 0.06 % 97.87 %
500 mm
Fig. 1. Geometry of workpiece
A high-speed steel (HSS) cutting tool with a hardness of 67.25 HRC is used (Table 3). The specification of the tool geometry is the following: side relief angle 8°, side rake angle 10°, side cutting edge angle 14°, end cutting edge angle 7°, end relief angle 7°, and back rack angle 10° (Fig. 2).
Table 3
Properties of cutting tool
Name Load Indenter Rockwell Hardness (HRC) On the average HRC
HSS 150 Diamond 67; 65; 67; 66; 67; 70; 67; 65 67.25
Nose radius
Side rake angle
1 6 Work
Side cutting edge angle
—^^/ piece
v
/ AT
Side relief angle
Fig. 2. Turning tool geometry
The design of the experimental method was used to perform the experimental work in a planned manner and to investigate the interaction effect between the various parameters considered. The relationship between output responses (output parameters) and input parameters was determined using the RSM. In this study, surface roughness (Ra) and flank wear (VB) variables were set through a second-order polynomial expression. The factors of feed rate (f), cut depth (a), and aloe vera cutting fluid (AVCF) and the response variables Ra and VB can be related using the expression: Ra = =Raf, a, AVCF) and VB = VB(f, a, AVCF). Table 4 shows factors and levels of the experimental design in this study. The process parameters of turning were selected based on the condition of the turning machine. Cutting fluids derived from aloe vera were processed based on viscosity levels (Table 5).
Table 4
Factors and levels of the experimental design
No. Factor Level -1 Level 0 Level 1
1 f [mm/rev] 0.14 0.16 0.2
2 a [mm] 1.5 2 2.5
3 Aloe Vera CF Types (AVCF Types) Type 1 Type 2 Type 3
Table 5 Plot experiment with the RSM method
No. Parameter Response
F [mm/put] A [mm] AVCF Types (cSt) Ra [Mm] VB [mm]
1 -1.000 -1.000 -1.000 - -
2 -1.000 -1.000 1.000 - -
3 0.000 0.000 0.000 - -
4 0.000 0.000 1.000 - -
5 0.000 -1.000 0.000 - -
6 1.000 -1.000 1.000 - -
7 0.000 0.000 0.000 - -
8 0.000 0.000 0.000 - -
9 1.000 0.000 0.000 - -
10 -1.000 1.000 1.000 - -
11 1.000 1.000 1.000 - -
12 0.000 0.000 -1.000 - -
13 -1.000 1.000 -1.000 - -
14 0.000 0.000 0.000 - -
15 1.000 -1.000 -1.000 - -
16 0.000 0.000 0.000 - -
17 1.000 1.000 -1.000 - -
18 -1.000 -1.000 -1.000 - -
19 -1.000 -1.000 1.000 - -
20 0.000 0.000 0.000 - -
The experiment involved three types of aloe vera (AVCF) intake: Aloe vera gel filtered as a cut-off with 80 mesh. AVCF type 1 is a non-filtered aloe vera liquid with a viscosity value of 71.897 cSt; AVCF type 2 is a filtered aloe vera liquid having a viscosity value of 17.293 cSt; AVCF type 3 is a filtered aloe vera with aloe vera skin with a viscosity value of 8.805 cSt (Fig. 3).
i=
80 70 60 50 40 30 20 10 0
Viscosity in cSt
filtered AVCF with aloe vera skin
TYPE OF CUTTING FLUIDS
71.897
8.805
Fig. 3. Viscosity value by type of cutting fluid
The experimental design was performed following Table 5, for AVCF type by introducing the numerical price of its viscosity value. Measurement of flank wear using microscope tool and Surface Roughness measurement with surface tester were performed, all data were processed by the RSM method (Fig. 4).
5. Experimental data and processing of the obtained results of experiment
Based on the research results, it can be stated that:
Ra = 5.2495 + 0.1536f + 0.1775a - 0.2332AVCF -
- 0.7356f2 + 1.8455a2 - 1.1298AVCF2 +
+ 0.1555fa + 0.1122 fAVCF - 0.4477aAVCF. (1)
From the regression equation for surface roughness (Ra), it can be observed that the effect of the square variable from the depth of cut (a) is very significant to the effect of surface roughness of the workpiece. As for the regression equation:
VB = 0,2681 - 0,0086f + 0,0126a + 0,0081AVCF -
- 0,0134 f2 + 0,0004a2 -0,0296AVCF2 -
- 0,0254fa + 0,0105fAVCF + 0,0069aAVCF.
(2)
The use of Aloe Vera Cutting Fluids (AVCF) has a very significant effect on the occurrence of flank wear. The advantages of this study are aloe vera cutting fluids with the chemical composition, which does not directly harm the environment and human health: there are components of Energy (1.73-2.30 Calories), Protein (0.100.06 gram), Fat (0.05-0.09 grams), Carbohydrate (0.30 gram), Calcium (9.92-19.920 milligrams), Iron (0.060-0.320 milligrams), Vitamin A (2.00-4.460 IU), Vitamin C (0.50-4.20 milligrams), Thiamin (0.003 to 0.040 milligrams), Ribofla-vin (0.001-0.002 milligrams), Niacin (0.038-0.040 milligrams), Fibers (0.30 grams), Ash (0.10 gram), Water content (99.20 gram) [13].
While the lack of the results of this study is not yet investigated emulsion process of aloe vera fluid that occurs for turning process that supports its function as cutting fluid.
Measuring of Heat Transfer and Cutting Fluids
Truning Process
Surface Tester
Stop Wacth
Lap Top and Acquisition Data
Fig. 4. Experimental design
This research needs to be developed, to obtain an aloe vera fluid that matches the required criteria, where the aloe vera fluid can be used as a standard cutting fluid for machining processes.
Table 6 describes the factors and corresponding responses in the cutting process design. From Table 7, a*a has the most significant effect on the surface roughness of the turning samples. Based on the ANOVA results of Table 8, the statistical significance of each parameter can be found by comparing the mean square against an estimate of the experimental error. In this case, two effects, a*a and a*CF, had ^-values <0.05 or a statistical significance at a confidence level of 95 %.
Table 6
Factors and corresponding responses for cutting process design
No. Parameter Response
f (mm/rev) a (mm) AVCF Types (cSt) Ra (|m) VB (mm)
1 0.14 1.5 71.897 5.3621 0.181
2 0.14 1.5 8.805 5.1069 0.172
3 0.16 2 17.293 5.1569 0.202
4 0.16 2 8.805 3.8610 0.233
5 0.16 1.5 17.293 6.0046 0.223
6 0.2 1.5 8.805 4.4535 0.235
7 0.16 2 17.293 4.9823 0.22
8 0.16 2 17.293 4.4201 0.254
9 0.2 2 17.293 4.0536 0.205
10 0.14 2.5 8.805 5.5874 0.273
11 0.2 2.5 8.805 6.7441 0.169
12 0.16 2 71.897 4.0138 0.235
13 0.14 2.5 71.897 4.5759 0.267
14 0.16 2 17.293 4.0746 0.265
15 0.2 1.5 71.897 5.7130 0.232
16 0.16 2 17.293 5.4564 0.246
17 0.2 2.5 71.897 4.7215 0.243
18 0.14 1.5 71.897 4.4723 0.207
19 0.14 1.5 8.805 5.3075 0.228
20 0.16 2 17.293 4.3716 0.338
Table 7
Coefficients of the regression response surface models, estimated regression coefficients for Ra
Term Coef. SE Coef. T P
Constant 5.2495 0.5859 8.96 0
f 0.1536 0.1793 0.857 0.412
a 0.1775 0.1817 0.977 0.352
AVCF -0.2332 0.1673 -1.393 0.194
f2 -0.7356 0.5377 -1.368 0.201
a2 1.8455 0.4717 3.912 0.003
AVCF2 -1.1298 0.8601 -1.314 0.218
fa 0.1555 0.1841 0.844 0.418
fAVCF 0.1122 0.1787 0.628 0.544
aAVCF -0.4477 0.1795 -2.494 0.032
Table 8
Analysis of variance (ANOVA) for Ra
Source* DF Seq SS Adj SS Adj MS F P
Regression 9 7.7378 7.7378 0.8598 2.77 0.064
Linear 3 0.2344 1.1911 0.397 1.28 0.334
f 1 0.0642 0.2278 0.2278 0.73 0.412
a 1 0.0024 0.2961 0.2961 0.95 0.352
AVCF 1 0.1679 0.6023 0.6023 1.94 0.194
Square 3 5.3332 5.7536 1.9179 6.18 0.012
f 2 1 1.0355 0.5806 0.5806 1.87 0.201
a2 1 3.8239 4.7491 4.7491 15.31 0.003
AVCF2 1 0.4739 0.5354 0.5354 1.73 0.218
Interaction 3 2.1701 2.1701 0.7234 2.33 0.136
fa 1 0.214 0.2212 0.2212 0.71 0.418
fAVCF 1 0.0258 0.1223 0.1223 0.39 0.544
aAVCF 1 1.9304 1.9304 1.9304 6.22 0.032
Residual Error 10 3.1029 3.1029 0.3103 - -
Lack-of-Fit 3 1.2605 1.2605 0.4202 1.6 0.274
Pure Error 7 1.8425 1.8425 0.2632 - -
Total 19 10.8407 - - - -
Note: * - Coef (Coefficient); SE Coef (Sum of Error Coefficient); T (Value T); DF (Degree of freedom); Seq SS (sequence sum of squares); Adj SS (adjust sum of squares); Adj MS (adjust Middle of squares); F (value F); P (Probability)
Therefore, the verified model of the surface roughness is shown by the following equation:
Ra = 5.2495 + 0.1536f + 0.1775a - 0.2332AVCF -
- 0.7356f2 + 1.8455a2 - 1.1298AVCF2 +
+ 0.1555fa + 0.1122 fAVCF - 0.4477aAVCF. (3)
Fig. 7 shows the values of the turning process parameters with Aloe vera as the most optimum cutting fluid on the surface roughness response is feed rate (0.140 mm/rev), Cut Depth (2.0556 mm), and Aloe vera with Viscosity (71,8970 cSt).
The effects of the turning process parameters on the surface roughness and flank wear are examined. As the model is built and verified, it can be used to generate plots to show the effect of process factors on the surface roughness and flank wear. These plots are generated and discussed here. However, reading the 3D surface and contour graphs requires some sort of careful attention to not draw any misleading or conflicting conclusions. To simplify interpretation of the results, the effect of each factor will be considered, as it exists separately. Then, the overall interpretation of the whole plot will be done.
The 3D surface plot and the contour plot of the effects of the feed rate (mm/rev), cut depth (mm) and AVCFs (cSt) (response) of the surface roughness are given. As shown by the 3D surface plot, by increasing the feed rate, the surface roughness increased. This roughness increase continues until the feed rate reaches about 0.175 mm/rev, where increasing the feed rate further decreases the roughness.
Therefore, according to this plot, an optimum (maximum) surface roughness setting was achieved with the feed rate value of around 0.175 mm/rev (Fig. 5).
Regarding the effect of the cut depth, as shown in (Fig. 5), increasing the cut depth beyond the lower level of 1.5 mm, the roughness has shown a decreasing trend. This decrease continued until reaching a minimum surface roughness at a cut depth of approximately 2.0 mm, and then it started increasing again until it reached approximately the same starting measurement with a cut depth of 2.5 mm. This is evident from the approximately U-shaped surface of the speed side of the 3D surface plot.
This is supported by the contour plot of (Fig. 6), where it is also evident from the colored contour of blue of the 2.0 mm value, which can be achieved by varying both the cut depth and feed rate. This agrees well with the 3D surface plot finding mentioned above.
Therefore, based on the given feed rate and cut depth ranges, we can conclude that, if a high roughness is required, then it is recommended that the feed rate is maintained at around 0.17 mm/rev. However, this finding needs to be compared with other plot findings, as it might be further refined or confirmed.
Surface Plot of Ra vs a, f
7 -
6-
Ra
2.5
0.150
0.175
0.200
Fig. 5. 3D surface plot of the effects of the cut depth (mm) and feed rate (mm/rev) on the surface roughness
Contour Plot of Surface Roughnes vs Depth of Cut (a), Feed Rate (f)
2.50
2.25
"S 2.00
1.75
1.50
0.14 0.15 0.16 0.17 0.18 0.19 0.20 Feed Rate (f)
Fig. 6. Contour plot of the effects of cut depth (mm) and feed rate (mm/rev) on the surface roughness
As shown in (Fig. 7), the chart predicts that the optimum process parameter settings are 0.140 mm/rev for the feed rate; 2.0556 mm for the cut depth, and 71.8970 cSt for the AVCFs, which would result in a maximum predictable surface roughness of 2.8606 |im.
However, this result is based on the response surface regression model, and the actual measured value could be somewhat different.
Therefore, it is very strongly suggested that this setting is implemented in the future and repeated a few times to confirm or otherwise refine the result. More specifically, the effects of the feed rate and cut depth need to be investigated in depth with smaller ranges to reach a conclusion.
Cptraal . 1,0000 Feed Rate Depth of Cut Aloe Vera 0.20 2.50 71.8970 [0.140] [2.0556] [71.8970] 0.140 1.50 8.8050
Surface MirimLm y - 2.8606 d= 1.0000
Fig. 7. Optimization chart of turning process parameters for surface roughness
From Table 9, f, a has the most significant effect on the flank wear of the turning samples. Based on the ANOVA results of Table 10, the statistical significance of each parameter can be found by comparing the mean square against an estimate of the experimental error.
Therefore, the verified model of the flank wear is shown by the following equation:
VB = 0.2681 - 0.0086f + 0.0126a + 0.0081AVCF -
- 0.0134 f2 + 0.0004a2 -0.0296AVCF2 -
- 0.0254fa + 0.0105fAVCF + 0.0069aAVCF. (4)
Table 9
Coefficients of the regression response surface models, estimated regression coefficients for flank wear
Term Coef. SE Coef. T P
Constant 0.268062 0.03991 6.717 0
f -0.008578 0.01222 -0.702 0.499
a 0.012591 0.01238 1.017 0.333
AVCF 0.008077 0.0114 0.709 0.495
f2 -0.013421 0.03663 -0.366 0.722
a2 0.00036 0.03213 0.011 0.991
AVCF2 -0.029593 0.05858 -0.505 0.624
fa -0.025368 0.01254 -2.023 0.071
fAVCF 0.010499 0.01218 0.862 0.409
a AVCF 0.006962 0.01223 0.569 0.582
5
4
a
f
Table 10
Analysis of variance (ANOVA) for flank wear
Source DF Seq SS Adj SS Adj MS F P
Regression 9 0.014312 0.014312 0.00159 1.1 0.436
Linear 3 0.003362 0.002731 0.00091 0.63 0.611
f 1 0.000291 0.00071 0.00071 0.49 0.499
a 1 0.003048 0.001489 0.001489 1.03 0.333
AVCF 1 0.000023 0.000723 0.000723 0.5 0.495
Square 3 0.00328 0.002215 0.000738 0.51 0.682
f 2 1 0.003025 0.000193 0.000193 0.13 0.722
a2 1 0.000001 0.000367 0.000000 0.00 0.991
AVCF2 1 0.000254 0.00767 0.000367 0.26 0.624
Interaction 3 0.00767 0.00589 0.002557 1.78 0.215
fa 1 0.005905 0.001071 0.00589 4.09 0.071
fAVCF 1 0.001298 0.000467 0.001071 0.74 0.409
a AVCF 1 0.000467 0.014397 0.000467 0.32 0.582
Residual Error 10 0.014397 0.00139 0.00144 - -
Lack-of-Fit 3 0.00139 0.013007 0.000463 0.25 0.859
Pure Error 7 0.013007 0.014312 0.001858 - -
Total 19 0.028709 - - - -
Fig. 10 explained about the value of the turning process parameters with Aloe vera as the most optimum cutting fluid on the flank wear response is feed rate (0.20 mm/rev), Cut Depth (2.50 mm), and Aloe vera with Viscosity (8.8050 cSt).
In (Fig. 8), when increasing the cut depth beyond the lower level of 1.5 mm, the flank wear has shown a decreasing trend. This decrease continued until reaching a minimum flank wear at a cut depth of approximately 1.5 mm, and then it started increasing again until it reached approximately the same starting level with a cut depth of 2.5 mm. This is evident from the approximately U-shaped surface of the speed side of the 3D surface plot.
Surface Plot of Wear Rate (VB) vs Depth of Cut (a), Feed Rate (f)
Wear Rate (VB)
0.30 0.28 0.26 0.24
Depth of Cut (a)
This is supported by the contour plot of (Fig. 9), where it is also evident from the colored green contour at 2.0 mm, which can be achieved by varying both the cut depth and feed rate.
This agrees very well with the 3D surface plot finding mentioned above. Therefore, based on the given feed rate and cut depth ranges, we can conclude that, if a high flank wear is required, then it is recommended that the feed rate is maintained at around 0.17 mm/rev.
Contour Plot of Wear Rate (VB) vs Depth of Cut (a), Feed Rate (f)
2.50
2.25
a 3
1.50
Wear Rate (VB)
< 0.240
0.240 - 0.255
I 0.255 - 0.270
I 0.270 - 0.285
| 0.285 - 0.300
> 0.300
Feed Rate (f)
0.14 0.15 0.16 0.17 0.18 0.19 0.20 Feed Rate (f)
Fig. 8. 3D surface plot of the effects of cut depth (mm) and feed rate (mm/rev) on the flank wear
Fig. 9. Contour plot of the effects of cut depth (mm) and feed rate (mm/rev) on the flank wear
2.00
1.75
2 . 5
0 20
However, this finding needs to be compared with other plot findings, as it might be further refined or confirmed. As shown in (Fig. 10), the chart predicts that the optimum process parameter settings are as follows: 0.20 mm/rev for the feed rate; 2.50 mm for the cut depth, and 8.8050 cSt for the AVCFs, which would result in a maximum predictable flank wear of 0.1785 mm. These results are based on the response surface regression model, and the actual measurable value can be somewhat different.
Qtfmal h D C0r 0.94369 i^. Feed Rate Depth of Cut ,0.20, J .50, [0.20] [2.50] 0.140 1.50 Aloe vera 71.89 70 [8.8050] 8.80 SO
Wear Rate Minimum y = 0.1785 d = 0.943*9 \
Fig. 10. Optimization chart of turning process parameters for flank wear
Thus, it is highly recommended that the setting is applied in the front and is repeated several times to confirm or improve the result. More specifically, again, the effects of the level of the feed rate and the cut depths need to be investigated in depth with a range that is smaller to obtain the optimal surface roughness and flank wear.
6. Discussion of simulation results
The use of Aloe vera cutting fluids (AVCF) has a very significant effect on the occurrence of flank wear. The advantages of this study are aloe vera cutting fluids with the chemical composition, which does not directly harm the environment and human health: there are components of Energy (1.73-2.30 Calories), Protein (0.10-0.06 gram), Fat (0.05-0.09 grams), Carbohydrate (0.30 gram), Calcium (9.92-19.920 milligrams), Iron (0.060-0.320 milligrams), Vitamin A (2.00-4.460 IU), Vitamin C (0.50-4.20 mil-
ligrams), Thiamin (0.003 to 0.040 milligrams), Riboflavin (0.001-0.002 milligrams), Niacin (0.038-0.040 milligrams), Fibers (0.30 grams), Ash (0.10 gram), Water content (99.20 gram) [13]. The limitations of this study are the range of regression equations for experimental variables used for the optimal process, contained in the equation Ra and the VB equation that can be used for estimation in this experiment. For the future study, another range of cutting process parameters can be developed.
7. Conclusions
1. The effects of the turning process parameters on the surface roughness and flank wear were examined. Square of the depth of cut (a2) has the most significant effect on the surface roughness of the turning samples.
2. The verified model of the surface roughness is shown by the following equation:
Ra = 5.2495 + 0.1536f + 0.1775a -
- 0.2332AVCF-0.7356f2 + 1.8455a2 -
- 1.1298AVCF2 + 0.1555fa + 0.1122 fAVCF -
- 0.4477aAVCF.
AVCF and the verified model of the flank wear are shown by the following equation:
VB = 0.2681 - 0.0086f + 0.0126a +
+ 0.0081AVCF - 0.0134 f2 + 0.0004a2 -
- 0.0296AVCF2 -0.0254fa + 0.0105fAVCF +
+ 0.0069aAVCF.
3. The optimum process parameter settings of the surface roughness were 0.140 mm/rev for the feed rate; 2.0556 mm for the cut depth, and 71.8970 cSt for the AVCFs. The optimum process parameter settings on the flank wear were 0.20 mm/rev for the feed rate; 2.50 mm for the cut depth, and 8.8050 cSt for the AVCFs.
References
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2. Ramana M. V., Rao K. M., Rao D. H. Effect of Process Parameters on Surface Roughness in Turning of Titanium Alloy under Different Conditions of Lubrication - Recent // Recent Advances in Robotics, Aeronautical and Mechanical Engineering. 2013. P. 83-91.
3. Sharafadeen K. K., Jamiu K. O. Performance Evaluation of Vegetable Oil-Based Cutting Fluids in Mild Steel Machining // Chemistry and Materials Research. 2013. Vol. 3, Issue 9. P. 35-45.
4. Rahim E. A., Sasahara H. A study of the effect of palm oil as MQL lubricant on high speed drilling of titanium alloys // Tribology International. 2011. Vol. 44, Issue 3. P. 309-317. doi: 10.1016/j.triboint.2010.10.032
5. Effectiveness of Minimizing Cutting Fluid Use when Turning Difficult-to-cut Alloys / Priarone P. C., Robiglio M., Settineri L., Tebaldo V. // Procedia CIRP. 2015. Vol. 29. P. 341-346. doi: 10.1016/j.procir.2015.02.006
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13. Komposisi Kimia Gel Lidah Buaya (Aloe vera L.). Ministry of Health of Republic of Indonesia, 1992.
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На двох промислових доменних печах досл^ джеш шлаковийрежим i нагрiв чавуну при перехо-дi на вдування пиловугшьного палива. Негативним на^дком змтеного шлакового режиму було зни-ження фiзичного нагрiву чавуну. Дослиджено вплив змт у складi шлатв на в'язтсть розплавiв. Вивчено вплив характеристик шлакового режиму - в'язкостi, основностi i стехюметрп на нагрiв чавуну. Суттевий вплив шлаку на нагрiв металу свидчить про необхдшсть врахування цього впли-ву при управлтш доменною плавкою i наступнш переробц чавуну в сталь
Ключовi слова: доменна шч, шлаковий режим,
в'язтсть, основтсть, шлак, нагрiв чавуну □-□
На двух промышленных доменных печах исследованы шлаковый режим и нагрев чугуна при переходе на вдувание пылеугольного топлива. Отрицательным следствием измененного шлакового режима явилось снижение физического нагрева чугуна. Исследовано влияние изменений в составе шлаков на вязкость расплавов. Изучено влияние характеристик шлакового режима -вязкости, основности и стехиометрии на нагрев чугуна. Существенное влияние шлака на нагрев металла свидетельствует о необходимости учета этого влияния при управлении доменной плавкой и последующем переделе чугуна в сталь
Ключевые слова: доменная печь, шлаковый
режим, вязкость, основность, шлак, нагрев чугуна -□ □-
1. Introduction
The transition of blast furnaces in Ukraine to the injection of pulverized coal fuel (PCF) was, as a rule, accompanied by a change in slug mode. Such a change was often characterized by a decrease in slag basicity both in relation to CaO/SiO2 (C/S) and to (CaO+MgO)/SiO2 (C+M)/S. A decrease in slag basicity was supposed to compensate for the negative influence of PCF on permeability of the coke head in the zone of melts flow. As a result, as shown in paper [1], there was a shift in the slag mode in the direction of
UDC 669.162
|DOI: 10.15587/1729-4061.2018.122970|
influence of the
properties of blast
furnace slag on
cast iron heating
at pulverized coal
injection
M. Kuznetsov
Postgraduate student* E-mail: [email protected] G. Kryach ko PhD, Associate Professor* E-mail: [email protected] V. Polye taev PhD, Associate Professor* E-mail: [email protected] N. Valuyeva PhD, Associate Professor Department of Traslation** E-mail: [email protected] *Department of Ferrous Metallurgy** **Dniprovsk State Technical University Dniprobudivska str., 2, Kamianske, Ukraine, 51918
formation of semi-slags, with a decrease in temperature of the produced cast iron. This decrease is known to create certain problems at subsequent converter processing of liquid cast iron. Semi-slags C/S = 1.1-1.2 received their name from the specifics of hardening a sample, consisting of two parts in the fracture: glassy (C/S<1.1), characteristic of acidic, and stone-like (C/S>1.2), characteristic of the basic ones. Prior to the transition to PCF injection, operation on highly-sul-furic Donetsk coke required the formation of base slag. That is why the properties of semi-slags with less sulfur absorbing capacity were not sufficiently studied.
© M. Kuznetsov, G. Kryachko, V. Rolyetaev, N. Valuyeva, 2018
