GREY RELATIONAL ANALYSIS TO OPTIMIZE WELDING PARAMETERS FOR DISSIMILAR SHEETS OF MATERIAL IN RESISTANCE SPOT WELDING

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 6, Issue 11, Nov 2015, pp , Article ID: IJMET_06_11_003 Available online at ISSN Print: and ISSN Online: IAEME Publication GREY RELATIONAL ANALYSIS TO OPTIMIZE WELDING PARAMETERS FOR DISSIMILAR SHEETS OF MATERIAL IN RESISTANCE SPOT WELDING Ramkrishna Parihar M-Tech scholar, Department of Mechanical Engineering, Medi-caps Institute of Technology & Management, Indore (MP), India Sanjay Jathar Prof., Department of Mechanical Engineering, Medi-caps Institute of Technology & Management, Indore (MP), India ABSTRACT Resistance spot welding (RSW) is a very popular technique for joining two or more than two sheets of metal in production industries. This research mainly focuses on the grey relational analysis with Taguchi method for optimizing heat affected zone (HAZ) with tensile shear strength. (HAZ) developed nearby weld nugget diameter. Increase of (HAZ) may cause the changes in microstructure properties, appearance and chemical composition of materials. To avoid such type of defects must be eliminating. By grey relational analysis it can be possible to eliminate such type of defects. Both characteristics (HAZ) and tensile shear strength are analysed by grey relational based Taguchi method. In this research main aim is to decrease (HAZ) and to increase tensile shear strength for weld quality. For experimentation dissimilar sheets of material AISI304 and mild steel were selected. There are four inputs welding parameter weld time, hold time, weld current and electrode force were selected at their three respective level. Orthogonal array L27 is designed for parameter on basis Taguchi method [3]. ANOVA table are calculated for significant parameter affects weld performance of weld joint on base of grey relational method. Grey relational grade have been determine to find out optimal value. Ultimately confirmation test shows improvement in grey relational grade. Improvement in grey relational grade indicates quality characteristics of weld joint. Key words: ANOVA, dissimilar materials, grey relational analysis, (HAZ), tensile shear strength. Cite this Article: Parihar, R. and Jathar, S. Grey Relational Analysis to Optimize Welding Parameters for Dissimilar Sheets of Material in Resistance 23 editor@iaeme.com

2 Ramkrishna Parihar and Sanjay Jathar Spot Welding. International Journal of Mechanical Engineering and Technology, 6(11), 2015, pp INRODUCTION The resistance spot welding process was first used about an hundred years ago. Now days it is regularly using in manufacturing industries. Resistance spot welding is a thermoelectric process. In which two or more sheets of metal are joined in one or more spots by resistance to flow of current through object that are hold by two electrodes under particular force. The spot welds are controlled by the combination of heat, time pressure. The process is generally used copper electrode to impact the pressure on work piece due to resistance of electric current to the work piece. Heat is developed at the faying surface of wok piece. It results in melting the surface of work piece & to become spot weld together at the metal sheets. Figure 1 shows the schematic diagram of welding process. The resistance spot welding (RSW) plays a very important role for its high speed & suitable for automotive industries. There are about a spot weld in automobile vehicles. RSW is significantly plays important role in manufacturing cars, mechanical assemblies, railway structures & in many more structural bodies. The quality of spot weld is best judged by weld joint strength, weld nugget diameter & heat affected zone (HAZ). Figure 1 shows the spot welding process. Figure 1 Spot welding process Aravintham arumugam, et al. [1] have studied that the parameter optimization when spot welding steels with dissimilar thickness & type using grey based Taguchi method the three characteristics that were optimized are weld strength, weld nugget & weld indentation. By ANOVA calculation it is found that weld current is most significant parameter. Ahilan, et al. [2] have studied multi response optimization of CNC turning parameters using grey relational grade is obtained by S/N ratio. Based on grey relational table significant contributions of controlling parameters are estimated using analysis of variance (ANOVA) There are two main of this experiments. The first one is that analysis of (HAZ) & tensile shear strength together to determine weld performance. Second aim is that to design significant parameter that affect experiment process. 2. GREY RELATIONAL METHOD Following steps are important in grey relational analysis. Normalize all experimental turns value editor@iaeme.com

3 Grey Relational Analysis to Optimize Welding Parameters for Dissimilar Sheets of Material in Resistance Spot Welding Perform operation of grey relational generating & to calculate grey relational coefficient (GRC). Averaging the value of grey relational coefficient (GRC) to determine grey relational grade (GRG). Perform ANOVA analysis with grey relational grade (GRG) and to find which parameter significantly affects experimental process. Select optimal level of parameters to determine optimal or prediction value. Conduct confirmation test and validate the prediction value. 3. METHODOLOGY A batch of 160mm 30 1mm dissimilar sheets of AISI304 & mild steel were selected. A schematic specimen is shown in Figure 2. The chemical component of both steels is shown in Table 1 & Table 2. The material of electrode is of copper. Figure 2 Specimen Table 1 Chemical component of AISI304 Component C Cr Fe Mn Ni P S Si Wt % Max Max Max0.045 Max0.03 Max1 Table 2 Chemical component of Mild steel Component C Si Mn S P Wt % max max max The input parameter is selected as weld time, hold time, weld current & electrode force. Desired output parameters are (HAZ) & tensile shear strength. The input parameters are shown in Table 3. For experimentation a pedal operated rocker arm type spot welding machine with the attachment of spot welding timer for controlling welding time, hold time in cycles were used. Total 27 runs are taken & three responses of each run are taken as shown in Figure 3. After runs samples are go through for tensile shear test on universal testing machine (UTM). Diameter of (HAZ) has been measured editor@iaeme.com

4 Ramkrishna Parihar and Sanjay Jathar Table 3 Input parameters Level Weld time Hold time Weld current (ampere) Electrode force (N) (A) (B) (C) (D) Experiment no. Figure 3 Samples of runs 3.1. Grey based Taguchi method A grey based Taguchi method approach is used for experimental analysis. Average shear strength & (HAZ) value of each runs are illustrated in Table Normalized experiment results Following equation (1) & (2) are used when a larger quality characteristics results are desired (larger the better) & when smaller quality characteristics results are desired (smaller the better). Table 4 Average shear strength & (HAZ) & normalized value of each runs Weld time Hold time Weld current (ampere) Electrode force (N) (A) (B) (C) (D) Average Tensile shear strength (KN) Average (HAZ) (mm) Normalized value Tensile shear strength Normalized value (HAZ) editor@iaeme.com

5 Experiment no. Grey Relational Analysis to Optimize Welding Parameters for Dissimilar Sheets of Material in Resistance Spot Welding Weld time Hold time Weld current (ampere) Electrode force (N) (A) (B) (C) (D) Average Tensile shear strength (KN) Average (HAZ) (mm) Normalized value Tensile shear strength Normalized value (HAZ) (1) (2) Where no. of runs) & no. of responses) 3.4. Deviation sequence Deviation sequence value of each are calculated & illustrated in Table 5 Table 5 Deviation sequence value Expt. no. Tensile shear strength (HAZ) Reference sequence editor@iaeme.com

6 Ramkrishna Parihar and Sanjay Jathar Expt. no. Tensile shear strength (HAZ) Where is the deviation sequence of the reference sequence and the comparability sequence) i.e Determination of grey relational coefficient (GRC) & Grey relational grade (GRG) Following formula (4) are used for grey relational coefficient (GRC) Where distinguishing coefficient is was given a value 0.5. Calculation of (GRC), (GRG) & rank of (GRG) are given in TABLE-6 Table 6 The calculated (GRC) & (GRG) and its order in the Optimization process Exp. No. GRC of tensile shear strength GRC of (HAZ) GRG RANKS (3) (4) 28 editor@iaeme.com

7 Grey Relational Analysis to Optimize Welding Parameters for Dissimilar Sheets of Material in Resistance Spot Welding Exp. No. GRC of tensile shear strength GRC of (HAZ) GRG RANKS Response table for GRG are shown in TABLE-7. Table 7 Response table for the grey relational grade Level A B C D Delta Rank editor@iaeme.com

8 GRD Ramkrishna Parihar and Sanjay Jathar A B C D LEVELS OF PARAMETER Figure 4 Graph for GRG with levels of parameter 4. ANALYSIS OF VARIANCE (ANOVA) ANOVA is a statistical analysis tool for data analysis. It includes design parameters that affect significantly output characteristics. In ANOVA method, sum of square (SS), mean square (MS) & F-test values are calculated for deciding significant factors which affecting the process & also percentage contribution contributed by parameters are calculated. ANOVA table for grey relational grade are described in TABLE-8, Table 8- ANOVA table for grey relational grade Source DOF SS MS F-value % C A B C D Error Total RESULTS & DISCUSSIONS Above the Figure 4 shows the Graph for GRG with levels of parameter. Larger value of GRG represents better quality characteristics for both (HAZ) & tensile shear strength. According to GRG graph the levels of parameters to be set for determine optimum value of desired weld quality characteristics is A 1 B 2 C 3 D 1. Most significant parameter according to ANOVA table is weld time which affect the performance of tensile shear strength & heat affected zone (HAZ). After weld time the significant parameter are weld current, hold time, & electrode force. Hold time & electrode force are less effective parameters. The percentage contributed by weld time, weld current, hold time, & electrode force are %, 7.050%,4.112%, & 3.995% editor@iaeme.com

9 Grey Relational Analysis to Optimize Welding Parameters for Dissimilar Sheets of Material in Resistance Spot Welding 6. CONFIRMATION TEST Confirmation test is very important in design of parameters. The aim of confirmation test is to validate the optimal value during analysis is A 1 B 2 C 3 D 1. The confirmation test is done by the specific combination of parameters with their levels which were predicted as A 1 B 2 C 3 D 1. In this experimental procedure the optimal value has been predicted & new experiment is designed to conducting new experiments to get best weld performance for quality characteristics. The predictions for optimum value for GRG are. Predicted mean = = Comparison result of initial welding parameter, predicted and experimental parameter are shown in TABLE-9. The improvement in Taguchi based grey relational grade is So by grey based Taguchi method it is possible to increase in tensile shear strength & decrease (HAZ) characteristics. Results of confirmation test are illustrated in TABLE-9. Table 9 Results of confirmation test Initial welding process parameters Prediction Optimum welding parameter Experimental Levels A 1 B 3 C 3 D 1 A 1 B 2 C 3 D 1 A 1 B 2 C 3 D 1 Tensile shear strength(kn) (HAZ) (mm) Taguchi based grey relational grade Improvement of Taguchi based grey relational grade CONCLUSION This research paper deals with optimization & the affect of factors on heat affected zone (HAZ) & tensile shear strength. ANOVA table determines significant welding parameters. According to ANOVA table welding time are highly affects tensile shear strength & (HAZ) where as welding current are second higher parameter that affects desired weld quality characteristics. Hold time and electrode force are less affective parameters. GRG determines the optimum combination of parameters with their levels or maximizing tensile shear strength & minimizing (HAZ). By conducting confirmation test & its results it is possible to increase tensile shear strength & heat affected zone. The experimental results validate Taguchi method for quality engineering to best performance & optimization of welding parameters in resistance spot welding. REFERENCES [1] Arumugam, A. and Amizi Nor, M. Spot Welding Parameter Optimization To Improve Weld Characteristics For Dissimilar Metals. International journal of scientific & technology research, 4(01), 2015, pp editor@iaeme.com

10 Ramkrishna Parihar and Sanjay Jathar [2] Ahilan, C., Kumaran, S. and Shivkumaran, N. Application Of Taguchi Method In Multi-response Optimization Of Turning Process. Advance In Production Engineering & Management, 5(3), 2010, pp [3] Ross, P. J. Taguchi techniques for quality engineering, 2nd Ed. Tata McGraw Hill, [4] Hasani, H., Tabatabaei, S. A. and Amiri, G. Grey Relational Analysis to Determine the OptimumProcess Parameters for Open-End Spinning Yarns. Journal of Engineered Fibers and Fabrics, 7(2), 2012, pp [5] Datta, S., Bandyopadhyay, A. and Kumar Pal, P. Grey based Taguchi method for optimization of bead geometry in submerged arc bead on plate welding. International Journal of Advanced Manufacturing Technology, 39, 2008, pp [6] Niranjan Kumar, S. and Vijayakumar, Y. Application of Taguchi method for optimization of resistance spot welding of austenitic stainless steel AISI 301L, Innovative Systems Design and Engineering, 3(12), 2012, pp [7] Juang, S. C. and Tarng, Y. S. Process Parameter Selection for Optimization the Weld Pool Geometry in the Tungsten Inert Gas Welding of Stainless Steel. Journal of Material Processing Technology, 122, 2002, pp [8] Jagannatha, N., Hiremath, S. S. and Sadashivappa, K. Analysis and parametric optimization of abrasive hot air jet machining for glass using Taguchi method and utility concept. International Journal of Mechanical and materials engineering, 7(1.9.15), 2012, pp [9] Aslanlar, S. The effect of nucleus size on mechanical properties in electrical resistance spot welding of sheets used in automotive industry. Journal of Materials and Design, 27, 2006, pp [10] Sun, D. Q., Lang, B., Sun, D. X. and Li, J. B. Microstructures and mechanical properties of resistance spot welded magnesium alloy joints. Materials Science and Engineering A, 2007, pp , [11] Gurav, B. D. and Ambekar, S. D. Optimization of the Welding Parameters in Resistance Spot Welding. International Journal of Mechanical Engineering and Technology, 4(5), 2013, pp editor@iaeme.com