Improving Deposition Quality of Stellite Powder on Valve Seats by Optimized TIG Welding Parameters

Ravindra Karwande; Santosh Bhosle; Ashok Keche

doi:10.52756/ijerr.2024.v45spl.006

Authors

Ravindra Karwande Department of Mechanical Engineering, Maharashtra Institute of Technology, Aurangabad, Maharashtra, India https://orcid.org/0009-0001-6867-340X
Santosh Bhosle Director of JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra, India https://orcid.org/0000-0003-0636-1723
Ashok Keche Department of Mechanical Engineering, Maharashtra Institute of Technology, Aurangabad, Maharashtra, India https://orcid.org/0000-0003-4405-4388

DOI:

https://doi.org/10.52756/ijerr.2024.v45spl.006

Keywords:

Tungsten Inert Gas (TIG) welding, Signal to Noise (S/N) Ratio, Optimization, Process Parameters, Blow holes, Stellite deposited material

Abstract

The quality of the Stellite powder coated on these valve seats depends on the discharge profile and is crucial to the service length and quality of these seats, especially when called upon to work at their maximum capabilities. This research seeks to work out how to minimize blow holes in the TIG welding process when depositing Stellite powder while maintaining other desirable qualities of the weld. In this respect, the work uses the OVAT technique and Taguchi design approach to analyze the effect of the major welding parameters, such as weld speed, feed rate and welding current, on the formation of blow holes. The research shows that it is possible to gain a remarkable increase in weld quality when the parameters mentioned are strictly controlled. In this systematic experimentation, the optimal welding condition found out was the welding speed of 6.5 rpm, feed rate of 120 mm/min, and welding current of 150A. With this combination, these parameters were determined to reduce the chances of blow-holes forming and increase the welds' quality and durability. With respect to the study objectives, the present investigation offers a clear direction to enhance TIG welding of valve seats with special reference to minimize the defects. Analysis done in the study shows that the welding parameters have the greatest impact in reducing the blow holes; the feed rate is 56.61%, weld speed is 22.28% and welding current is 20.17%. The results enhance weld quality, offering rules for eradicating imperfections in the TIG welding of valve seats, which is crucial for high-performance sectors.

References

B., Prakash, D., & Dhemla, P. (2024). Assessment of Cement Mortar Strength Mixed with Waste Copper Mine Tailings (CT) by Applying Gradient Boosting Regressor and Grid Search Optimization Machine Learning Approach. International Journal of Experimental Research and Review, 42, 183–198. https://doi.org/10.52756/ijerr.2024.v42.016

Bansod, A., Shukla, S., Gahiga, G., & Verma, J. (2023). Influence of filler wire on metallurgical, mechanical, and corrosion behaviour of 430 ferritic stainless steel using a fusion welding process. Materials Research Express, 10(3), 036513. https://doi.org/10.1088/2053-1591/acb908

Bharath, R. R., Ramanathan, R., Sundararajan, B., & Srinivasan, P. B. (2008). Optimization of process parameters for deposition of Stellite on X45CrSi93 steel by plasma transferred arc technique. Materials & Design, 29(9), 1725–1731. https://doi.org/10.1016/j.matdes.2008.03.020

Bradley, J. S., Reich, R. D., & Norcross, S. G. (1999). On the combined effects of signal-to-noise ratio and room acoustics on speech intelligibility. The Journal of the Acoustical Society of America, 106(4), 1820–1828. https://doi.org/10.1121/1.427932

Chang, S.S., Wu, H.C., & Chen, C. (2008). Impact Wear Resistance of Stellite 6 Hardfaced Valve Seats with Laser Cladding. Materials and Manufacturing Processes, 23(7), 708–713. https://doi.org/10.1080/10426910802317102

Chaudhary, V., Bharti, A., Azam, S M., Kumar, N., & Saxena, K K. (2021). A re-investigation: Effect of TIG welding parameters on microstructure, mechanical, corrosion properties of welded joints. Elsevier BV, 45, 4575-4580. https://doi.org/10.1016/j.matpr.2021.01.007

Cheemalapati, V., Khan, D., & PVSSR, C. (2024). Digital Watermarking Using Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm (HGOAGA). International Journal of Experimental Research and Review, 42, 278-291. https://doi.org/10.52756/ijerr.2024.v42.024

Das, S., Banerjee, D., & Mukherjee, S. (2017). Evaluation of Work Posture and Postural Stresses of Welders: A Report. Int. J. Exp. Res. Rev., 14, 1-8. https://qtanalytics.in/journals/index.php/IJERR/article/view/1248

Ding, Y., Liu, R., Zhang, Q., Wang, L., & Yao, J. (2017). A composite stellite alloy hard facing with improved laser cladding behavior and wear resistance. International Congress on Applications of Lasers & Electro-Optics, 508. https://doi.org/10.2351/1.5138150

Durakovic, B. (2017). Design of experiments application, concepts, examples: State of the art. Periodicals of Engineering and Natural Sciences (PEN), 5(3). https://doi.org/10.21533/pen.v5i3.145

Dzukey, G. A., & Yang, K. (2019). Process Parameter Optimization for Selective Laser Melting of 316L Stainless Steel Material using Taguchi’s Statistical Design of Experiment Procedure. International Journal of Engineering and Technology, 11(1), 6–13. https://doi.org/10.21817/ijet/2019/v11i1/191101014

Ilie, C. O., Marinescu, M., Barothi, L., Vilau, R., & Lespezeanu, I. (2018). Study the parameters of a petrol engine using analysis of variance. IOP Conference Series: Earth and Environmental Science, 172, 012028. https://doi.org/10.1088/1755-1315/172/1/012028

Jain, S., & Mulewa, A. (2024). Experimental Analysis of Surface Roughness Optimization of EN19 Alloy Steel Milling by the Cuckoo Search Algorithm. International Journal of Experimental Research and Review, 38, 102-110. https://doi.org/10.52756/ijerr.2024.v38.009

John, V., Aggarwal, S., Gupta, D., Anandaram, H., & Joshi, K. (2024). Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations. International Journal of Experimental Research and Review, 38, 61–68. https://doi.org/10.52756/ijerr.2024.v38.006

Kannappan, K. (2014). Application of Design of Experiments (DOE) using Dr.Taguchi -Orthogon.

Kumar, A., Kumar, R., & Kumar, A. (2024). Study of Wear Rate of AA7050-7.5 B4C-T6 Composite and Optimization of Response Parameters using Taguchi Analysis. International Journal of Experimental Research and Review, 39(Spl Volume), 73-81. https://doi.org/10.52756/ijerr.2024.v39spl.005

Kumar, A., Sharma, G., & Dwivedi, D K. (2023). TIG spot weld bonding of 409?L ferritic stainless steel. https://www.sciencedirect.com/science/article/pii/S0143749618301052

Kumari, M., Barma, J. D., & Singh, S. (2021). Parametric observation of TIG welding on AISI 304 stainless steel of thickness 5 mm. Materials, Mechanics & Modeling (NCMMM-2020), 2341, 040013. https://doi.org/10.1063/5.0053278

Lee, L.W., Yeh, S.S., & Lee, J.I. (2017). Application of Taguchi method for determining the best-fitted control parameters of CNC machine tools. IEEE International Conference on Mechatronics and Automation (ICMA), 1676–1681. https://doi.org/10.1109/icma.2017.8016069

Li, B., Nye, T. J., & Metzger, D. R. (2006). Improving the Reliability of the Tube-Hydroforming Process by the Taguchi Method. Journal of Pressure Vessel Technology, 129(2), 242–247. https://doi.org/10.1115/1.2716427

Mascarenhas, L. A. B., Gomes, J. de O., Portela, A. T., & Ferreira, C. V. (2015). Reducing the Development Life Cycle of Automotive Valves and Seat Valves Using a New Workbench for High Temperature Wear Testing. Procedia CIRP, 29, 833–838. https://doi.org/10.1016/j.procir.2015.01.068

Paes, R., & Throckmorton, M. (2017). The installation and setup of a variable speed drive for optimal performance. Petroleum and Chemical Industry Conference Europe (PCIC Europe), 1–8. https://doi.org/10.23919/pciceurope.2017.8015061

Prasad, K. S., Rao, Ch. S., & Rao, D. N. (2011). Prediction of Weld Bead Geometry in Plasma Arc Welding using Factorial Design Approach. Journal of Minerals and Materials Characterization and Engineering, 10(10), 875–886. https://doi.org/10.4236/jmmce.2011.1010068

Ragavendran, U., Ghadai, R. K., Bhoi, A. K., Ramachandran, M., & Kalita, K. (2019). Sensitivity analysis and optimization of EDM process parameters. Transactions of the Canadian Society for Mechanical Engineering, 43(1), 13–25. https://doi.org/10.1139/tcsme-2018-0021

Sahu, N. K., & Andhare, A. (2018). Design of Experiments Applied to Industrial Process. Statistical Approaches with Emphasis on Design of Experiments Applied to Chemical Processes. https://doi.org/10.5772/intechopen.73558

Sawant, M. S., & Jain, N. K. (2017). Investigations on wear characteristics of Stellite coating by micro-plasma transferred arc powder deposition process. Wear, 378–379, 155–164. https://doi.org/10.1016/j.wear.2017.02.041

Selvi, S., Sankaran, S. P., & Srivatsavan, R. (2008). Comparative study of hard-facing of valve seat ring using MMAW process. Journal of Materials Processing Technology, 207(1–3), 356–362. https://doi.org/10.1016/j.jmatprotec.2008.06.053

Shanmugam, R., & Murugan, N. (2006). Effect of gas tungsten arc welding process variables on dilution and bead geometry of Stellite 6 hard faced valve seat rings. Surface Engineering, 22(5), 375–383. https://doi.org/10.1179/174329406x126726

Sharma, D., Singh, P., & Punhani, A. (2024). Sugarcane Diseases Detection using the Improved Grey Wolf Optimization Algorithm with Convolution Neural Network. International Journal of Experimental Research and Review, 38, 246-254. https://doi.org/10.52756/ijerr.2024.v38.022

Sigmund, M. (2021). Heterogenous Weld Heat Resistant Steel with Cobalt Alloy. Manufacturing Technology, 21(5), 700–705. https://doi.org/10.21062/mft.2021.069

Smoqi, Z., Toddy, J., Halliday, H. (Scott), Shield, J. E., & Rao, P. (2021). Process-structure relationship in the directed energy deposition of cobalt-chromium alloy (Stellite 21) coatings. Materials & Design, 197, 109229. https://doi.org/10.1016/j.matdes.2020.109229

Srivastava, V. C., Mall, I. D., & Mishra, I. M. (2008). Optimization of parameters for adsorption of metal ions onto rice husk ash using Taguchi’s experimental design methodology. Chemical Engineering Journal, 140(1–3), 136–144. https://doi.org/10.1016/j.cej.2007.09.030

Tiwari, D., & Srivastava, A. K. (2024). Process Parameter Effects on Powder Mixed EDM Machining Characteristics Using Biocompatible Ti-6Al-4V Alloy. International Journal of Experimental Research and Review, 41(Spl Vol), 1-10. https://doi.org/10.52756/ijerr.2024.v41spl.001

Tong, L., Xu, H., Xu, X., Cheng, H., Feng, L., & Chang, J. (2023). Intelligent Testing and Analysis of Dissimilar Steel Welds for Industrial Throttle Flowmeter. IOS Press, 2023, 1-11. https://doi.org/10.1155/2023/4006715

Uy, M., & Telford, J. K. (2009). Optimization by Design of Experiment Techniques. IEEE Aerospace Conference, 1–10. https://doi.org/10.1109/aero.2009.4839625

Vallejo, G., Fernández, M. P., Ato, M., & Livacic-Rojas, P. E. (2008). A Practical Method for Analyzing Factorial Designs with Heteroscedastic Data. Psychological Reports, 102(3), 643–656. https://doi.org/10.2466/pr0.102.3.643-656

Vora, J., Patel, V. K., Srinivasan, S., Chaudhari, R., Pimenov, D. Y., Giasin, K., & Sharma, S. (2021). Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies. Metals, 11(6), 981. https://doi.org/10.3390/met11060981