MACHINING STUDY OF AL-SICP METAL MATRIX NANO COMPOSITE DEVELOPED BY ULTRASONIC ASSISTED CASTING PROCESS

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 10, October 2018, pp , Article ID: IJMET_09_10_154 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed MACHINING STUDY OF AL-SICP METAL MATRIX NANO COMPOSITE DEVELOPED BY ULTRASONIC ASSISTED CASTING PROCESS Pradyut Kumar Swain School of Mechanical Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India Ratnakar Das * Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Hatia, Ranchi, India Ashok Kumar Sahoo School of Mechanical Engineering, KIIT (Deemed to be University), Bhubaneswar, Odisha, India *corresponding author ABSTRACT Machining performance of Al+SiC nanocomposite is presented in this paper. The composite is developed with ultrasonic assisted casting process. Weight percent of 1%, 1.5% and 2% of SiC nanoparticles are used for making the composite. The different mechanical tests confirm that the composite with 2% by weight of SiC give best property than the other two. The machining of the composite of 2% SiC is carried out with different set of machining parameters levels of cutting speed, feed and depth of cut. The uncoated carbide insert is used for machining at dry environment. Principal Component Analysis (PCA) has been used for optimization of the process parameters for getting lowest surface roughness of the machined surface and the lowest flank wear of the tool. Cutting speed is the most influencing parameter in the cutting process followed by depth of cut and feed. Key words: Nanocomposite, Ultrasonic assisted casting, Machining, PCA and ANOVA. Cite this Article Pradyut Kumar Swain, Ratnakar Das.and Ashok Kumar Sahoo, Machining Study of Al-Sicp Metal Matrix Nano Composite Developed by Ultrasonic Assisted Casting Process, International Journal of Mechanical Engineering and Technology, 9(10), 2018, pp editor@iaeme.com

2 Pradyut Kumar Swain, Ratnakar Das.and Ashok Kumar Sahoo 1. INTRODUCTION Now-a-days, the demand for the new products and innovative technologies in the global economy is soaring. Excellent high strength to weight ratio and mechanical properties of different materials are the interests in various industries and applications. Among the metals, aluminum and its alloys and composites are of greater interest mostly for the automotive, space industries and home appliances.the metal matrix composites (MMC)have gained popularity because of capability of fabrications and improved mechanical properties. The reinforcement materials in aluminum matrix are property dependent. The increase in mechanical properties depends on the proportion ofreinforcement and chemical composition of aluminum metal matrix [1]. Among the reinforcements, silicon carbide particulates (SiCp) add different properties to the composites like low density, high strength, high hardness, wear resistance and low thermal expansion. The size of the SiCp particles plays a major role in deciding the strength and quality of the composite. In metal matrix nano composites, the bonding between reinforcement of SiCpnano particles and aluminum matrix can change the interface and arrangement of the matrix leading to interfacial compounds. These intermetallic compounds affect the mechanical properties and beneficial in improving the ductility and toughness. However, the ductility and fracturetoughness of the composites are considerably lower than that of wrought alloys. The SiCp, as a particle or fiber are reinforced with aluminum to form metal matrix composite (Al-SiCp) and has wide suitable use [2]. The apparent surface area of the nano particles limits the dispersion capability in the liquid state of matrix material and this constrains the weight (%) mixing value at a very low level than that to the micro particles. Further nano-composites offer good thermal, tribological and mechanical properties over the micro composites [3]. Powder metallurgy route and casting methods are well accepted techniques for production of aluminum based composites. The nano reinforcement particles accelerate many properties but the proper mixing of the nano sized particulates is quite challenging. The nano-sized SiCp reinforced for commercial casting of Al alloy matrix was studied by Mazahery and Shabani with commercial casting technique [4]. Dhanashekar and Kumaradapted squeeze casting process to develop aluminum metal matrix composites with SiC reinforcement [5]. The nanocomposites have started getting the attention in recent past two decades [6] because of good resistance to scratch and abrasion. The nano particles like boron carbide (B4C) and SiC are widely use with aluminum matrix material. Because of low cost of SiC, it is widely used as compared to B4C. In general, micrometer size reinforcement particles are common in development of metal matrix composites. Thorough mixing of the nano particles is a major challenge in production of nanocomposites. Ultrasonic assisted casting is a promising process for composite fabrication. The studies of different properties, wear and machinability give indepth information for the degree of acceptance of a nanocomposite material. Gopalakannan and Senthivelan studied electric discharge machining of aluminium silicon carbide nano-composites and found that the pulse current is the most significant factor affecting the MRR, EWR and surface roughness [7]. In this work, the authors have developed Al+SiCp nanocomposite by ultrasonic assisted casting method and some essential mechanical properties are evaluated. Machining (turning process) has been carried out on the developed composite. The effective process parameters of machining process are found out and optimality study has been carried out to establish the relationship between the input process parameters and surface finish of the machined component and flank wear of the tool 2. DEVELOPMENT OF HYBRID AL-SICP NANOCOMPOSITE In the present experimental study, ultrasonic assisted casting method has been used to develop the Al-SiCp metal matrix nanocomposite. The weight percentages of 1%, 1.5% and 2% of SiCp have been used for development of the composite. The SiCpnano particle of the required amount editor@iaeme.com

3 Machining Study of Al-Sicp Metal Matrix Nano Composite Developed by Ultrasonic Assisted Casting Process was supplied to the feeder. The feeder was integrated with sieving system, high frequency mechanical vibrator and aluminium liquid pouring system. Adequate water source was kept around the steel die from the sides of ultrasonic chamber for transmission of ultrasonic waves. The molten aluminium was poured into the pipe keeping the mechanical vibrator on. The ultrasonic generator produces waves of frequency 35 KHz. The high intensity ultrasonic waves in the molten metal liquid generate non-linear ultrasonic effects such as transient cavitations. The liquid metal flow also takes place due to the pressure gradient. These are the reasons of microstructure refinement and homogeneity in composition. The ultrasonic waves also help in removal of entrapped gases in the molten metal in the casting process. Figure 1 shows the schematic of the experimental set up for Al-SiCp nanocomposite development. Figure 1.Schematic of setup for Al-SiC pnano composite 2.1. Characterization of Work piece Material (Al-SiCp) The cast ingot was normally cooled after removal from the casting set up. Compression test specimens of required dimensions (ASTM) were prepared by machining of the cast pieces Compression Test of Al-SiCp The compression test was carried out for the specimen that was prepared from the nano composite cast ingots with an UTM of 100KN capacity at a very low strain rate (10-3 S -1 ) to avoid the strain hardening effect. The yield strength, Young s Modulus and compressive strength were measured.table 1shows the different strength values of different samples. Property Table 1Mechanical properties obtained from the test Specimen-1 Aluminium+ SiCp (1wt. %) Specimen-2 Aluminium+ SiCp (1.5wt. %) Specimen-3 Aluminium+ SiCp (2 wt. %) Yield Strength(MPa) Compressive Strength(GPa) Elastic Modulus (GPa) editor@iaeme.com

4 Pradyut Kumar Swain, Ratnakar Das.and Ashok Kumar Sahoo Hardness Test The micro hardness tests were carried out for samples of nanocomposites with different SiC weight percent usingleica VMHT Auto digital micro hardness Test Machine.The test was carried out with 100 gram load applied for 15 s dwell period. The variations of hardness values were noted for the specimen and the average values are recorded. Table2 shows the average micro hardness values of the developed nanocomposites. The developed nanocomposite with 2% SiCp by weight shows highest hardness value. Literature show that up to 1.5% of SiCnano-particles added to aluminum and the composites developed by using other methods like stir casting techniques show the best result. Higher percentage of SiC particles addition leads to non-uniform distribution and as a result, the strength and quality of the composite reduces. The ultrasonic assistance in the casting process helps in better and uniform mixing of the SiCnano-particles. This helps in providing better strength and hardness at 2% (by weight) of SiC while mixed with aluminum matrix. As reported earlier also, beyond 2% of SiC by weight mixing won t help in improving the quality of the product, in the present case also the ingots were fabricated up to maximum 2% by weight of SiC only. Table 2Micro hardness tests of nano composites Specimens Micro hardness value Specimen-1 [Al+SiC p1 wt%] Specimen-2 [Al+SiC p1.5 wt%] Specimen-3 [Al+SiC p2 wt%] Machining Study of Al+ SiCp nanocomposite Aluminium has a good machinability property when machined in controlled and defined conditions. The earlier research works on machining of aluminum and SiC composites reveal that the micro and larger sizes of SiC after a certain percentage of addition give bad performance. Addition of SiC nano particles increases the strength and other relevant properties of the composite. In the present work, machining study of the developed composite has been carried out with the cast sample-3 (2 % wt. of SiC nano particles). Hard turning under dry cutting environment on CNC lathe (Figure 2) has been carried out on round bar of cast ingot of 44 mm diameter and 65 mm length. Figure 2. CNC lathe used for machining Uncoated carbon alloy tool ( TTR) is used for machining of the work piece. Three process parameters are selected at four different levels using Taguchi L16 orthogonal array. Table 3 shows the process parameters, levels and ranges selected for the design of experiments. Flank wear (VBC) of cutting inserts and average surface roughness (Ra) are the response parameters editor@iaeme.com

5 Machining Study of Al-Sicp Metal Matrix Nano Composite Developed by Ultrasonic Assisted Casting Process The design of experiment is modeled using MINITAB 17. A fixed machining length of 100 mm has been set for each experimental run. Prior to the experiment, rust skin layer was removed by initial cuts. New cutting edge is used for each experimental run. Control limit are (i) maximum flank wear (0.3 mm) and (ii) surface roughness (1.6 µm) are set for machining. After the completion of experiment, the chips samples were collected for the chip morphology study. The surface roughness study and tool flank wear are presented here. Table 4 shows the cutting conditions and tool specifications. Table3 Process parameters level and ranges Parameters/Levels I II III IV Cutting Speed (v); m/min Feed (f); mm/rev Depth of cut (d); mm Table 4 Cutting conditions for hard turning Work piece Cutting environment Tool holder Cutting tool insert geometry Overhang length Cutting inserts Al-SiC pmetal matrix Dry PCLNR2525M12 CNMG for carbide 30 mm Uncoated carbide, Insert CNMG TTR Determination of Surface roughness and Flank wear The quality of the machined surface in terms of roughness is a very important criterion for studying the machinability of the developed nanocomposite.the surface roughness of metal matrix composite is measured after the machining operation. Flank wear takes placeat the tool flanks when it comes in contact with the finished surface due to adhesion and abrasion wear. It is the gradual failure of the cutting tools in which the tool portion with the finished part erodes. It is denoted by VBC and measured under microscope. Table 5 shows the Taguchi L16 orthogonal array of process parameters and response parameter values obtained from experimental runs. Table 5 Input parameters and responses obtained at different runs Process Parameters Response Parameters Depth of cut (d) Cutting Speed (v) Feed (f) R a (µm) V BC (mm) editor@iaeme.com

6 Pradyut Kumar Swain, Ratnakar Das.and Ashok Kumar Sahoo OPTIMIZATION OF RESPONSE PARAMETERS Optimization is a technique to identify and to obtain the best possible outcomes from a given set of results. Principal Component Analysis (PCA) is a technique in which new un-correlated variables are formed through a linear composite of the original variables. The number of original variables depends on maximum number of new variables formed. PCA explains the variance/covariance structure for a given set of variables by linearly combining the original variables. In the present work, this technique has been used to optimizethe output responses of the parameters (multi-objective optimization) and thepurpose is to minimize the flank wear and surface roughness Steps for Calculating Principal Component Analysis PCA is a technique that converts a set of observations of possibly correlated variables into a set of values using an orthogonal transformation called as principal components. The steps involved in the PCA are A. Normalization of process parameters B. Finding out the Pearson s Correlation Coefficient (P). If P value is zero, PCA can t be applied C. Finding out the individual PCA D. Calculation of Multiple Performance Index (MPI). If MPI is negative, CQL will be considered into account E. Calculation of Combined Quality Loss (CQL) Table 6 Optimization of response parameters using PCA Sl.No. Normalized data R a V BC MPI Ideal editor@iaeme.com

7 Machining Study of Al-Sicp Metal Matrix Nano Composite Developed by Ultrasonic Assisted Casting Process 4. RESULTS AND DISCUSSION 4.1. Analysis of Main Effect Plot Figure 3shows the main effect plot diagram of means obtained using PCA where X axis represents the process parameters and Y axis denotes the Multiple Performance Index (MPI). It is observed that the highest points of d, v and f are at 0.1mm, 70 m/min and 0.05 mm/rev respectively. The best combination obtained is , which present first combination (L1) of the settings (Table 5). The corresponding surface roughness andflank wear obtained from these combinations are 0.42 µm and mm respectively. Moreover Multiple Performance Index (MPI) decreases with increase in process parameters i.e. feed, speed and depth of cut. The decrease is due to the fact that during the machining of Al-SiCp composite, semi continuous chips were formed. The formation of these kinds of chips indicates reduced ductility with the increase in wt. % of nano particle in the aluminium metal matrix composite. As a result with the increase in feed, speed and depth of cut, MPI decreases. Figure 3. Main effect plot for MPI 4.2. Analysis of Variance (ANOVA) for Means Analysis of variance (ANOVA)determines the most significant input parameters affecting the response parameters. Table 7 showsthe analysis for variance of Principal Component Analysis and P notifies the probability test. The factor is said to be significant if the P value is < From Table 7, it is observed that the significant parameters affecting the Multiple Performance Index is cutting speed followed by feed and depth of cut. So it can be concluded that the process parameter cutting speed affects surface roughness and flank wear. The Regression coefficient (R 2 ) of 98.2 % obtained from analysis and it also confirms the stability of the model. Table 7 ANOVA table for MPI Source DF Seq SS Adj SS Adj MS F P Remarks Depth of cut (d) <0.05 Significant Speed (v) <0.05 Significant Feed (f) <0.05 Significant Residual Error Total Response Table for MPI Response table decides the rank of the input parameters or matrix. Table 8 depicts the rank of the input parameters obtained after the machining process. From the response table it is concluded editor@iaeme.com

8 Pradyut Kumar Swain, Ratnakar Das.and Ashok Kumar Sahoo that the most significant process parameter in optimizing the responses is cutting speed (Rank 1) followed by depth of cut (Rank 2) and feed (Rank 3). Table 8 Response table for means Level Depth of cut (d) Speed (v) Feed (f) Delta Rank Normal Probability Plot The normal probability plot of the residuals of 16 sets of experiments is shown in Figure 4. The point closet to the normal line satisfies the fitness of the model. It is observed that all the 16 points lies very close to the normal line andconfirm the normal distribution of the data sets. Figure 4 Normal probability plot 5. CONCLUSIONS The machining performance of the Al+SiC nanocomposite developed with the help of ultrasonic assisted casting process is studied. The ultrasonic assistance helped to add the SiC nano-particles up to 2% by weight which also gives better properties. Machining of the developed nanocomposite was carried out with uncoated carbide insert tool with different combinations of speed, feed and depth of cut. The surface roughness and the flank wear are measured. The optimum combination was obtained as (depth of cut-cutting speed and feed). Speed is most significant factor in affecting the output responses followed by depth of cut and feed. REFERENCES [1] Kala, H., Mer, K. K. S. and Kumar, S. A Review on Mechanical and Tribological Behaviors of Stir Cast Aluminum Matrix Composites.Material Science, 6, 2014, pp [2] Gatea, S., Ou, H. and McCartney, G. Deformation and fracture characteristics of Al6092/SiC/17.5p metal matrix composite sheets due to heat treatments.materials and Characterization, 142, 2018, pp editor@iaeme.com

9 Machining Study of Al-Sicp Metal Matrix Nano Composite Developed by Ultrasonic Assisted Casting Process [3] Singh, N., Mir,I. U. H., Raina, A., Anand, A., Kumar, V. and Sharma, S. M. Synthesis and tribological investigation of Al-SiC based nano hybrid composite. Alexandria Enginerring Journal, [4] Mazahery, A. and Shabani, M. O. Nano-sized silicon carbide reinforced commercial casting aluminum alloy matrix: Experimental and novel modeling evaluation. Powder Technology, 217, 2012, pp [5] Dhanashekar, M. and Senthil Kumar, V. S. Squeeze casting of aluminium metal matrix composites - An overview.procedia Engineering, 97, 2014, pp [6] Poovazhagan, L., Kalaichelvan, K. and Rajadurai, A. Preparation of SiC Nanoparticulates Reinforced Aluminum Matrix Nanocomposites by High Intensity Ultrasonic Cavitation Process. Transactions of The Indian Institute of Metals, 67, 2014, pp [7] Gopalakannan, S. and Senthilvelan, T. Application of response surface method on machining of Al-SiC nano-composites. Measurement: Journal of International Measurement Confederation,46(8), 2013, pp editor@iaeme.com