Microsoft Word - 30-2537_s Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3822-3825 3822 www.etasr.com Reddy & Chaganti: Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS Nune Madan Mohan Reddy Department of Mechanical Engineering BITS Pilani Hyderabad Campus Telangana, India madan008phd@gmail.com Phaneendra Kiran Chaganti Department of Mechanical Engineering BITS Pilani Hyderabad Campus Telangana, India phaneendrakiran@yahoo.co.in Abstract—AISI 420 martensitic stainless steel is used for making gas and steam turbine blades, steel balls and medical instruments, due to its anti-corrosive properties. Turning of AISI 420 SS would be a worthy procedure specifically in manufacturing high surface finish parts. In this work, effort has been made to investigate the cooling and lubricating performance of SiO2 (silicon dioxide) nanoparticles at different weight concentrations of 0.1g, 0.5g and 1g mixed in a novel developed base fluid (synthetic). The performance of optimum SiO2 based cutting fluid is evaluated based on the turning process with output responses like surface finish and cutting temperature. Taguchi technique was used with standard L9(3**4) orthogonal array. The responses, surface roughness, and cutting temperature were analyzed using S/N (signal-to-noise) and ANOVA (analysis of variance). This analysis identifies the significant input parameter combination to obtain minimum surface roughness and temperature. Keywords-Taguchi; SiO2 nanoparticles; ANOVA; orthogonal array; cutting fluid I. INTRODUCTION Water was firstly proposed as a cutting fluid to reduce temperature and enhance surface finish and tool life in metal cutting process [1]. Onwards, many cutting fluids were introduced and used by the machining industry. Recently, industries realized the cost, environmental and health issues in the use of these cutting fluids [2-4]. The industry expects a cutting fluid with minimal cost, more output and best quality [5]. Few attempts were made in the past to develop such a cutting fluid by including nanoparticles in it and it was named as nanolubricant, which includes a combination of metallic or non-metallic nanometer sized particle in the cutting fluid. In the past decade more attention was paid to nanolubricants due to their enhanced thermal properties like thermal conductivity and convective heat transfer coefficient [6]. Most commonly used nanoparticles in the base fluid were titanium oxide, molybdenum disulphide and silicon dioxide. Compared to others, SiO2 nanoparticles have shown a significant improvement in machining and thermal properties and a reasonable improvement in lubrication effect [7-11]. These SiO2 nanoparticles impinge between metal surfaces and create rolling action enhancing lubrication [12]. Another study on SiO2 nanolubricant [13] shows 62.67% and 30.86% lower forces in dry machining and application of conventional oil based cutting fluid. Turbine blade materials like AISI 420, do have a high chromium percentage (i.e. 13 to 14%). Turning of these high hardness materials raises high temperature at the cutting zone and more surface roughness on the machined surface. The surface finish of these materials is critical as poor surface finish leads to surface cracks and to fail at high centrifugal forces [14]. There are a few studies in the literature on the machinability of AISI 420 using nanolubricants [8-10] and some of them used DOE (design of experiments) [15]. Frequently used DOE are response surface and Taguchi methods. Response surface method is expensive compared to Taguchi method. Taguchi method suggests a few experiments while keeping the analysis at par with other methods [16]. This work aims to minimize the surface roughness of machined AISI 420 material using SiO2 nanolubricant. The control factors were speed, feed, depth of cut and the weight of SiO2 nanoparticles in the base fluid. The responses considered were surface finish of the workpiece and cutting temperature in the cutting zone. The experiments were planned and conducted using L9 orthogonal array. II. MATERIALS AND METHODS A. Workpiece, Tool and Nanoparticles The experiments were carried out on a CNC lathe machine (HMT Praga model) as shown in Figure 1. The workpiece material considered for turning was AISI 420 with 50mm diameter and 120mm length. Fig. 1. Cutting temperature measurement with thermocouple. A HSS (high speed steel) uncoated carbide insert (CNMG 120408) with 0.8mm nose radius was used. The Corresponding author: N. M. M. Reddy Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3822-3825 3823 www.etasr.com Reddy & Chaganti: Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS workpiece and the cutting tool properties are given in Table I. Each experiment was conducted using a new cutting edge. The size of the SiO2 nanoparticles used in the cutting fluid was 521.4nm (Table II). Adding SiO2 nanoparticles in the cutting fluid may improve the convective heat transfer coefficient and net heat carrying capacity, which is essential requirement for a cutting fluid. TABLE I. WORKPIECE AND TOOL MATERIAL PROPERTIES Material Properties Workpiece Cutting tool Type AISI420 martensitic stainlesssteel Uncoated HSS carbide insert Conductivity (W/mK) 24.9 105 Density (kg/m 3 ) 7800 15000 Modulus (GPa) 200 620 Poisson’s ratio 0.28 0.22 Specific heat Cp (J/kg o C) 460 670 TABLE II. SiO2 NANOPARTICLE PROPERTIES Properties SiO2 Nanoparticles Physical structure Amorphous crystalline powder Conductivity (W/cm K) 0.015 Density (g/cm3) 2.1 B. Plan of Experiments All the experimental procedures used Taguchi L9 orthogonal array. Considered control parameters were speed, feed, depth of cut and SiO2 nanoparticle concentration in the base fluid. All parameters were kept at 3 levels: low, medium and high. The responses measured were surface roughness and temperature at the cutting zone. Each experiment was repeated thrice to ensure repeatability and the average value of these three experiments is given below. The list of planned experiments is specified in Table III. TABLE III. DESIGN OF EXPERIMENTS AND CONTROL FACTORS # Orthogonal array L9 (3**4) Speed-A (m/min) Vc Feed-B (mm/rev) f Depth of Cut- C (mm) ap SiO2 *-D (g) A B C D 1 1 1 1 1 150 0.10 0.10 0.1 2 1 2 2 2 150 0.15 0.20 0.5 3 1 3 3 3 150 0.20 0.30 1 4 2 1 2 3 175 0.10 0.20 1 5 2 2 3 1 175 0.15 0.30 0.1 6 2 3 1 2 175 0.20 0.10 0.5 7 3 1 3 2 200 0.10 0.30 0.5 8 3 2 1 3 200 0.15 0.10 1 9 3 3 2 1 200 0.20 0.20 0.1 * concentration III. RESULTS AND DISCUSSION The responses obtained were analyzed with S/N ratio and significant factors were also identified. The smaller-the- better S/N ratio equation was chosen for the responses surface roughness and cutting temperature: ������ � ��� ��� � �10� �� � � ∑ ��� ������� (1) The responses and corresponding S/N ratios are given in IV. ANOVA was done to find significant parameters and the optimal concentration of SiO2 nanoparticle in the based fluid to get least surface roughness and cutting temperature. TABLE IV. EXPERIMENTAL AND S/N RESULTS # Vc f ap SiO2 (g) Surface Roughness Cutting Temperature Ra (µm) Ra. S/N (dB) T ( o C) T. S/N (dB) 1 150 0.10 0.1 0.1 0.47 6.55 169 -44.5 2 150 0.15 0.2 0.5 0.35 9.11 162 -44.1 3 150 0.20 0.3 1 0.26 11.7 148 -43.4 4 175 0.10 0.2 1 0.49 6.19 153 -43.6 5 175 0.15 0.3 0.1 0.64 3.87 178 -45.0 6 175 0.20 0.1 0.5 0.53 5.51 167 -44.4 7 200 0.10 0.3 0.5 0.58 4.73 173 -44.7 8 200 0.15 0.1 1 0.29 10.7 164 -44.2 9 200 0.20 0.2 0.1 0.61 4.29 182 -45.2 A 5% level of significance was considered for ANOVA. The ANOVA results for surface roughness, cutting temperature are discussed in the following sections. A. Surface Roughness The S/N ratios of surface roughness varied from 3.87dB to 11.7dB. Optimal combination of factors for minimum surface roughness can be obtained from the plot in Figure 2. This shows that 150m/min cutting speed, feed 0.15mm/rev, 0.1mm depth of cut and 1g SiO2 nanoparticle combination gives minimum surface roughness. Among all 4 factors the concentration of SiO2 nanoparticles was having an S/N ratio of 9.55dB. This emphasizes the importance of SiO2 nanoparticles in controlling the surface roughness of the parts produced. Detailed S/N ratios of all input parameters for different levels are presented in Table V. Fig. 2. Optimum combination of control factors for minimum surface roughness (µm). ANOVA for surface roughness was analyzed and the results are given in Table VI. These results show 50.65% contribution from concentration of SiO2 nanoparticles showing its significance. Regarding the other factors, speed Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3822-3825 3824 www.etasr.com Reddy & Chaganti: Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS is contributing 37.66%, feed 7.14% and depth of cut 4.55% in reducing surface roughness. The interaction plot for the control factors is shown in Figure 3. In general, the parallel lines in the interaction plot show no interactions and the intersecting lines represent the presence of interactions between control factors. Figure 3 shows that most of the factors were having a two way interaction in influencing the output surface roughness. TABLE V. RATIO OF S/N RESPONSE FOR SURFACE ROUGHNESS Level Cutting Speed Feed Depth of cut SiO2 concentration 1 9.12 5.82 7.60 4.90 2 5.19 7.91 6.53 6.45 3 6.59 7.16 6.76 9.55 ∆ 3.93 2.08 1.07 4.64 Rank 2 3 4 1 TABLE VI. ANOVA FOR SURFACE ROUGHNESS Sources Degrees of freedom Sum of squares Mean of squares Contribution percentage (%) Cutting speed 2 0.058 0.029 37.66 Feed 2 0.011 0.005 7.14 Depth of cut 2 0.007 0.003 4.55 SiO2 nanoparticle concentration 2 0.078 0.039 50.65 Error 0 Total 8 0.154 100 Fig. 3. Control factors interaction for surface roughness (µm). Fig. 4. Optimum combination of control factors for lower cutting temperature ( o C). B. Cutting Temperature A similar study was carried out considering cutting temperature. Optimal parameter combination for minimal temperature may be interpreted from Figure 4. The S/N ratio for cutting temperature ranged from -45.2dB to -43.3dB and is given in Table IV. The S/N ratio response table at different levels of input parameters is given in Table VII. The optimal parameter combination was chosen based on the S/N ratio value at the particular level. The optimal parameter condition for minimum cutting temperature is 150m/min speed (- 44.05dB), 0.1mm/rev feed (-44.34dB), 0.2mm depth of cut (- 44.38dB) and 1gram (-43.80 dB) SiO2 nanoparticle concentration. As before, for the cutting temperature the nanoparticle concentration is having the highest S/N ratio, proving its significance among other factors. The ranking of the parameters obtained by the value of S/N ratio was specified in Table VII. The ANOVA results for the cutting temperature are given in Table VIII. The results show that concentration of SiO2 nanoparticle contributed 70.53% in the temperature variance. Among the other factors, cutting speed contributes 27.97% and the contribution from depth of cut and feed are negligible. TABLE VII. S/N RESPONSE FOR CUTTING TEMPERATURE Level Cutting Speed Feed Depth of cut SiO2 concentration 1 -44.05 -44.34 -44.44 -44.94 2 -44.39 -44.50 -44.38 -44.47 3 -44.77 -44.37 -44.39 -43.80 ∆ 0.72 0.16 0.06 1.14 Rank 2 3 4 1 TABLE VIII. ANOVA FOR CUTTING TEMPERATURE Sources Degrees of freedom Sum of squares Mean of squares Contribution percentage (%) Cutting speed 2 281 140 27.97 Feed 2 14 7 1.40 Depth of cut 2 1 0 0.10 SiO2 nanoparticle concentration 2 708.7 354.3 70.53 Error 0 Total 8 1004.7 100 Interaction effect for the factors is shown in Figure 5. In this, most of the lines representing the factors are non- parallel showing the two way interactions. In Figure 5, irrespective of other factors, the temperature at the 1-gram concentration of SiO2 nanoparticle is lower. This can be attributed to the increased heat carrying capacity and thermal conductivity of nanoparticles in the base fluid. The general regression equations were obtained based on the experimental results and are given below: 130 0 273 10 0 167 24 1� � . . . . o C T C V f ap X= + × + × − × − × (2) 0 144 0 00267 0 467 0 317 0 253� � . . . . . C R m V f ap Xα µ = + × − + × − × (3) where VC is speed used, f is feed, depth of cut is ap and the concentration of SiO2 nanoparticles is X. The R-square values obtained for cutting temperature and surface roughness are 98% and 73.3% respectively. The given regression polynomial equations are useful for predicting the cutting temperature and surface roughness. 0.200.150.10 0.30.20.1 1.00.50.1 0.60 0.45 0.30 0.60 0.45 0.30 0.60 0.45 0.30 Cutting speed (m/min) Feed (mm/rev) Depth of cut (mm) SiO2 Concentration (gram) 150 175 200 (m/min) speed C utting 150 175 200 (m/min) speed C utting 150 175 (m/min) speed C utting 0.10 0.15 0.20 (mm/rev) F eed 0.10 0.15 0.20 (mm/rev) F eed 0.1 0.2 0.3 cut (mm) Depth of Interaction Plot for Surface roughness (um) Data Means Engineering, Technology & Applied Science Research Vol. 9, No. 1, 2019, 3822-3825 3825 www.etasr.com Reddy & Chaganti: Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS Fig. 5. Control factors interaction for cutting temperature ( o C). IV. CONCLUSIONS In the present work, an optimal level of SiO2 nanoparticles concentration in a base fluid for minimal cutting temperature and surface roughness was determined. The machining experiments were conducted on AISI 420. Speed, feed, depth of cut and SiO2 nanoparticle concentration were considered as factors and Taguchi L9 orthogonal array was used to design the experiments. Based on the responses measured from the experiments the following observations were made: • Minimum surface roughness was observed on the machined surface of the workpiece for 1g of SiO2 nanoparticles in base fluid, 150m/min cutting speed, 0.15mm/rev feed and 0.1mm depth of cut. • Minimum cutting temperature was observed for 1g of SiO2 nanoparticles in base fluid, 150m/min cutting speed, 0.10mm/rev feed and 0.2mm depth of cut. • Based on ANOVA it was observed that SiO2 nanoparticles contribute 50.65% in reducing surface roughness and 70.53% in obtaining minimum cutting temperature. • A polynomial equation was proposed to obtain a relationship between input parameters and responses, i.e. cutting temperature and surface roughness. 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