Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach

  • Suraj R. Marathe Research Scholar, Mechanical Department, ASSAM DON BOSCO University, INDIA
  • Dr. Carmo E. Quadros Professor, Mechanical Department, ASSAM DON BOSCO University, INDIA
Keywords: High Pressure Die Casting (HPDC), Parameters, ProCAST, Simulation, Six Sigma Quality

Abstract

A numerical simulation approach is proposed to predict the optimal parameter setting during high pressure die casting. The contribution from the optimal parameters, the temperature, showed more influence on the casting quality than the other parameters. This study’s outcome was beneficial for finding the solution for casting defects that occurs due to incorrect setting of process parameters in die casting. Thus, a combination of numerical optimisation techniques and casting simulation serves as a tool to improve the casting product quality in die casting industries. This paper aims to analyse and optimise critical parameters like injection pressure, molten metal temperature, holding time, and plunger velocity, contributing to the defects. In this research paper, an effort has been made to give optimal pressure, temperature, holding time, and plunger velocity parameters using ProCAST simulation software that uses finite element analysis technology. Numerical analysis for optimising the parameters by varying the temperature of molten metal, injection pressure, holding time, and plunger velocity,  concerning solidification time at hot spots, is an essential parameter for studying the defect analysis in the simulated model.

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References

Domkin, K., Hattel, J., & Thorborg, J. (2009). Modelling of high temperature and diffusion controlled die soldering in high aluminium pressure die casting. Journal of Material Processing Technology, 209(8), 4051-4061.

Fiorese, E., Richiedei, D., & Bonollo, F. (2016). Improving the quality of die castings through optimal plunger motion planning: analytical computation and experimental validation. International Journal of Advanced Manufacturing Technology, 88, 1475–1484.

Fu, J. & Wang, K. (2014) Modelling and simulation of the die casting process for A356 semi-solid alloy. Procedia Engineering, 81, 1565–1570.

Jorstad, J. & Apelian, D. (2009). Pressure assisted processes for high integrity aluminium castings - part 1. International Journal of Metal Casting, 250-254.

Kumar, S., Gupta, A., & Chandna, P. (2012). Optimisation of process parameters of pressure die casting using taguchi methodology. World Academy of Science, Engineering and Technology, 6, 590-594.

Lattanzi, L., Fabrizi, A., Fortini, A., Merlin, M., & Timelli, G. (2017). Effects of microstructure and casting defects on the fatigue behaviour of the high-pressure die-

cast AlSi9Cu3 (Fe) alloy. Procedia Structural Integrity, 7, 505–512.

Mohanty, C. & Jena, B. (2014). Optimisation of aluminium die casting process using artificial neural network. International Journal of Emerging Technology and Advanced Engineering, 4(7), 146-149.

Syrcos, G. (2002). Die casting process optimisation using taguchi methods. Journal of Material Processing Technology, 135, 68-74.

Wang, L., Turnley, P., & Savage, G. (2011) Gas content in high pressure die castings. Journal of Materials Processing Technology, 211, 1510–1515.

Zhang, M., Xing, S., Xiao, L., Bao, P., Liu, W., & Xin, Q. (2008). Design of process parameters for direct squeeze casting. Journal of University of Science and Technology, 15(3), 339-343.

Zhang, X., Xiong, S., & Xu, Q. (2006). Numerical methods to improve the computational efficiency of solidification simulation for the investment casting process. Journal of Materials Processing Technology, 173, 70–74.

Published
2021-02-27
How to Cite
Suraj R. Marathe, & Dr. Carmo E. Quadros. (2021). Analysis and Optimisation of High Pressure Die Casting Parameters to Achieve Six Sigma Quality Product Using Numerical Simulation Approach. International Journal of Engineering and Management Research, 11(1), 97-109. https://doi.org/10.31033/ijemr.11.1.15