OPTIMIZATION OF PROCESS PARAMETERS IN DIRECT METAL DEPOSITION TECHNIQUE USING TAGUCHI METHOD

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 7, Issue 3, May June 2016, pp , Article ID: IJMET_07_03_022 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication OPTIMIZATION OF PROCESS PARAMETERS IN DIRECT METAL DEPOSITION TECHNIQUE USING TAGUCHI METHOD Subodh Kumar Department of Production Engineering, B. I. T. Sindri, Dhanbad, Jharkhand, India Ajit Kumar Singh Choudhary Department of Mechanical Engineering, Manav Rachna International University, Faridabad, India Jamshed Anwar Department of Production Engineering, B. I. T. Sindri, Dhanbad, Jharkhand, India Vinay Sharma Department, Production Engineering, B.I.T. Mesra, Ranchi, Jharkhand, India ABSTRACT Direct Metal Deposition (DMD) process is an important component in many industrial operations. The DMD parameters are the most important factors affecting the quality, productivity and cost of metal depositin. This paper presents the influence of DMD parameters like Laser power, laser scan speed, powder mass flow rate on height deposition in DMD. A plan of experiments based on Taguchi technique has been used to acquire the data. An Orthogonal array, signal to noise (S/N) ratio and analysis of variance (ANOVA) are employed to investigate the DMD characteristics & optimize the height deposition. Finally the conformations tests have been carried out to compare the predicated values with the experimental values confirm its effectiveness in the analysis of penetration. Key words: Direct Metal Deposition, optimization, orthogonal array, S/N ratio, Software Minitab15, Taguchi method 240

2 Optimization of Process parameters in Direct Metal Deposition Technique using Taguchi method Cite this Article Subodh Kumar, Ajit Kumar Singh Choudhary, Jamshed Anwar and Vinay Sharma, Optimization of Process parameters in Direct Metal Deposition Technique using Taguchi method. International Journal of Mechanical Engineering and Technology, 7(3), 2016, pp INTRODUCTION Direct metal deposition (DMD) combines powder metallurgy, laser, nozzle and numeric control technologies. Similar to SLS and SLM, laser metal deposition uses a high-power laser beam for layer fabrication. However, instead of dispensing beds of powder over a movable platform inside a containing chamber, the powder is delivered remotely to a metallic substrate via a supply nozzle. This characteristic implies that the powder, same as the laser beam, can be freely delivered in any orientation, be it vertical, horizontal or inclined. A robotic arm can be used for these purposes [1]. Moreover, layer fabrication can be carried out over a flat or round substrate. As the powder does not need to be accommodated into a carefully-laid flat powder bed inside an enclosed chamber, the process can be well-fitted for the processing of large-size components [2,3]. As DMD is carried out on a solid substrate, the issues of melt pool control, such as capillary flow through voids and balling effect that occur in SLM are not encountered. As a result, most commercially available metallic powders can be processed. Moreover, blends of different powder materials can be supplied during layer fabrication in order to create graded materials or even to create in-situ alloys, which is an unparalleled capability of DMD [4]. Figure.1 Illustrates the basic working principle of DMD. A high power laser beam is made to scan over a metal base. As the laser beam generates a small melt pool on the substrate, the powder delivered through a nozzle is melted and fused to the melt pool and bonded to the substrate as a line or track of newly added material. The process continues with the laser scanning according to pre-defined programming of the CNC system or robotic arm without the need for intermediate operations of Figure 1 Schematic Diagram of the DMD process 2. TAGUCHI S DESIGN METHOD: Taguchi Technique is applied to plan the experiments. The Taguchi method has become a powerful tool for improving productivity during research and development, so that high quality products can be produced quickly and at low cost. Dr.Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on" ORTHOGONAL ARRAY" experiments which hgives much reduced "variance" for the experiment with "optimum settings" of control parameters. Thus the marriage 241

3 Subodh Kumar, Ajit Kumar Singh Choudhary, Jamshed Anwar and Vinay Sharma of Design of Experiments with optimization of control parameters to obtain best results is achieved in the Taguchi Method."Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments and Dr. Taguchi's Signal-to-Noise ratios (S/N), which are log functions of desired output, serve as objective functions for optimization, help in data analysis and prediction of optimum results[5] DMD PARAMETERS AND THEIR LVELS Table 2.1 Factors and their levels Symbol Factor Level 1 Level 2 Level 3 P Laser Power(kw) U Laser scan speed(m/min) M Powder feed rate(g/min) L9 3 Level Taguchi Orthogonal Array Table 2.2 L Orthogonal array of Taguchi 9 Trial P U M 3. ANALYSIS OF S/N RATIO In the present investigation L 9 orthogonal array was selected and it has 9 rows and 3 columns. The selection of the orthogonal array is based on the condition that the degree of freedom of the orthogonal array should be greater than, or equal to the sum of the variables. Each variable and the corresponding interactions were assigned to a colum defined by Taguchi method. In the study of prediction of height deposition in the direct metal deposition is carried out by selecting laser power (p), laser scan speed (u) and mass flow rate (m), as control variables. The control variables and their levels are shown in table 1.1 and table 1.2 shows the standard L 9 orthogonal array. The first column was assigned to laser power (p), the second column to laser scan speed. The response variable to be studied is height deposit. The experiments were conducted by the other researcher[7], and the value of response variable is calculated through mathematical model developed by the author. Since the model has been validated through experimentally [ruam et al] and once the value of dimeensionless constant C has been considered in the developed model, it may be used as virtual experiments. The virtual experiments were conducted based on the rank generated by Taguchi model and the results were obtained. The analysis of virtual experiments was carried out using MINITAB 15 software, which is specially used in DOE applications. The experimental results were transformed to into Signal to Noise (S/N) ratio. S/N 242

4 Optimization of Process parameters in Direct Metal Deposition Technique using Taguchi method ratio is defined as the ratio of the mean of the signal to the standard deviation of the noise. The S/N indicates the degree of predictable perfomance of a process in the presence of noise factor. The S/N ratio is calculated using larger the better characteristics, which can be calculated as a logrithmic transformation of the loss function, and is given in the equation 1.1 n 1 1 i 10log10 2 n i 1 yi where, i is S/N ratio at the i th (1.1) trial or experimental run, yi is observed response or quality value at the i th trial or experimental run, and n is the number of trials at the same parameter level. 1 2 f cp Tm u h m( p pc ) Z (1.2) The hypothetical experiments were conducted (usnig model eqn. 1.2)developed by the author[7] as per orthogonal array and the height deposit results obtained from model for various combinations of parameters are shown in Table 1.2. the calculated values from model were transformed into S/N ratios for measuring the quality characteristics using MINITAB 15. The S/N ratio obtained from all calculation (hypothetical experiments) are shown in Table 1.3. SI. NO. Table 3.1 Height and S/N ratio obtained as per Taguchi L Orthogonal Array 9 Laser Power(KW) Laser Scan Speed(m/min) 1 4 Powder Feed Rate(g/min Height(mm) Deposit from the model S/N Ratio

5 Subodh Kumar, Ajit Kumar Singh Choudhary, Jamshed Anwar and Vinay Sharma Figure 3.1 Main Effects Plots for Means 3.1. Response table for s/n ratio Figure 3.2 Main Effects Plots for S-N Ratio Table 3.2 Response Table for S-N ratio larger is better (deposit height) Level Laser Power(KW) Laser scan speed(m/min) Mass flow rate(g/min) Delta Rank The influence of control parameters such as laser power, laser scan speed and mass flow rate on height deposit has been evaluated using S/N ratio response analysis. The control parameters with the strongest influence was determined by the difference between the maximum and minimum value of the mean of the S/N ratios. Higher the difference between the mean of S/N ratios, the more influential was the control parameter. The S/N ratio response analysis presented in table 1.4 shows that among all the factors, laser scan speed was the most influential and significant parameter followed by mass flow rate and laser power. Figure 1.1 shows the mean of height deposit graphically and figure 1.2 depicts the main effects plot for means of S/N ratio 244

6 Optimization of Process parameters in Direct Metal Deposition Technique using Taguchi method for height deposit. From the analysis of these results, it can be inferred that, parameter combination of laser scan speed (u) = 0.3 m/min, mass flow rate (m) = 11 g/min and laser power (p) = 1.50 KW gave the maximum height deposit for the range of parameters tested. 4. ANOVA AND EFFECTS OF PARAMETERS ON HEIGHT DEPOSIT Analysis of variance (Anova) was used to determine the design parameters significantly influencing the height deposit (response). The table shows the results of Anova for height deposit. This analysis was evaluated for a confidence level of 95%, that is for significance level of = Table 4.1 ANOVA Result for Height Source DF Adj SS Adj MS F-Value P-Value Laser Power(KW) Scan Speed(m/min) Mass flow rate(g/min) Error Total Notes: DF, Degrees of freedom; Adj SS, Adjusted sum of squares; Adj MS, Adjusted mean Squares S= R-sq =99.38% R-sq (adj)= 97.53% R-sq(pred)= 87.48% It can be observed from the results obtained in the table 1.5 that laser scan speed was the most significant parameter having the highest statistical influence (P=0.007) and the mass flow rate(p=0.111) followed by laser power(p=0.203). When the P- value for this model is less than 0.05, then the parameter can be considered as statistically significant. This is desirable as it demonstrates that the parameter in the model has a significant effect on the response. The coefficient of determination (R 2 ) is defined as the ratio of the explained variation to the total variation. It is a measure of the degree of the fit. When R 2 approaches unity a better response model results and it fits the actual data. The value of R 2 calculated for this model was 0.994, i.e., very close to unity, and thus acceptable. It demonstrates that of the variability in the data can be explained by this model. Thus, it is conferred that this model provides reasonably good explanation of the relationship between the independent factors and the response. 5. MULTIPLE LINEAR REGRESSION MODEL A multiple linear regression analysis attempts to model the relationship between two or more predictor variables and a response variable by fitting a linear equation to the observed data. Best on the virtual experimental results, multiple linear regression models were developed using MINITAB15. Regression equations thus generated establish correlation between the significant terms obtained from ANOVA, namely laser scan speed, mass flow rate and laser power

7 Subodh Kumar, Ajit Kumar Singh Choudhary, Jamshed Anwar and Vinay Sharma Table 5.1 Prediction required to generate regression equation Predictor Coef SE Coef T P Constant Laser Power(KW) Laser scan speed(m/min) Mass flow rate (g/min) S = R-Sq = 92.3% R-Sq (adj) = 87.7% The regression equation generated from the table 1.6 is as: Height deposit (mm) = Laser Power (KW) Laser scan Speed (m/min) mass flow rate (g/min) (1.3) Since regression equation for height deposit is a function of the parameters like laser power, laser scan feed and mass flow rate. But from the table 1.6, it is found that laser power parameter has P-value 0.269, which is non-significance. So this parameter has lesser effect on the height deposit (response). The equation (1.3) can be used to predict the height deposit in the DMD. The constant in the equation is the residue. The regression coefficient (R 2 ) obtained for the model was The coefficient associated with laser power (P) in the regression equation is positive and it indicates that as the laser power increases, the height deposit in DMD is also increases. The coefficient associated with the laser scan speed (u) is negative and this suggest that the height deposit decreases with increase in laser scan speed. Similarly the positive coefficient associated with mass flow rate indicates that as the mass flow rate (m) increases, the height deposit also increases. 6. THE CONFIRMATION TEST In order to provide the regression model, confirmation height deposit tests were conducted with parameter levels that were different from those used for analysis. The different parameters levels chosen for the confirmation tests are shown in table 1.7. The results of the confirmation test were obtained and a comparison was made between the virtual experimental height deposit and the computed values obtained from the regression model(table 1.8). The error associated with the relationship between the virtual experimental values and the computed values of the regression model for DMD was very less. Hence the regression model developed demonstrates a feasible and effective way to predict the height deposit in the DMD. Table 6.1 Parameters used for confirmation test Test No. Laser Power(KW) Laser Scan Speed(m/min) Mass flow rate(g/min)

8 Optimization of Process parameters in Direct Metal Deposition Technique using Taguchi method Table 6.2 Confirmation test results Test No. Experiment Model of equations Error (%) CONCLUSION This chapter deals with the statistical analysis carried on the DMD to predict the height deposit for different control variables. Control variables selected are laser power, laser scan speed and mass flow rate. Taguchi s Design of Experiments is used for this purpose. The analysis is carried out by varying the control variables upto three levels. ANOVA and S/N ratio analysis is carried out to predict the influential parameter contributing to the height deposit in the DMD. Multiple linear regression analysis is carried out to build height deposit model which can be used to predict height deposit in DMD for different control variables. Finally confirmation test are carried out to validate the height deposit model. REFERENCES [1] Nowotny, S., Scharek, S., Kempe, F., and Beyer, E. COAXn: Modular system of powder nozzles for laser beam build-up welding in 22nd International Congress on Applications of Lasers and Electro-Optics Jacksonville, FL, USA. [2] Fraunhofer_Iws. Laser cladding/ build-up welding. [Internet] Available from: chnologies/laser_cladding/service_offers.html. [Cited July 2012]. [3] Laser_Cladding_Technology_Ltd. Laser cladding process. [Internet] Available from: Process.aspx. [Cited July 2012 [4] Levy, G.N., Schindel, R., and Kruth, J.P., Rapid manufacturing and rapid tooling with layer manufacturing (LM), state of the art and future perspectives. CIRP Annals - Manufacturing Technology, (2): pp. [5] Post Graduate Student, 2Professor, Mechanical Engineering, Rajarambapu Institute of [6] Technology, Sakharale , Maharashtra, India, International Journal of Advanced Engineering Research and Studies E-ISSN ] [7] Mondol, Subrata, Bandhopadhya Asis, Kumar Pal Pradip., An Artificial Neural Network approach for the process prediction of laser cladding using co 2 laser, Global trends and challenges in design and manufacturing proc of the 3 rd intl.and 24 AIMTDR Conf, 2010 pp [8] Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, Turning Parameters Optimization for Surface Roughness by Taguchi Method. International Journal of Mechanical Engineering and Technology, 4(3), 2012, pp

9 Subodh Kumar, Ajit Kumar Singh Choudhary, Jamshed Anwar and Vinay Sharma [9] Raj Kumar Yadav Rahul Kankane and Sandhya Yadav, Parametric Optimisation of Heat Exchanger with the Help of Taguchi Method A-Review. International Journal of Mechanical Engineering and Technology, 6(10), 2015, pp [10] R. R. Deshmukh and V. R. Kagade, Optimization of Surface Roughness In Turning High Carbon High Chromium Steel by Using Taguchi Method. International Journal of Mechanical Engineering and Technology, 3(1), 2012, pp [11] Kumar S, Sharma V, Kumar, Choudhary AKS, et al (2013) Determination of layer thickness in direct metal deposition using dimensional analysis: Int J Adv Manuf Technol, 67: