Robust Process Design for Continuous Casting Based on Numerical Simulation and Experimental Study

Abstract:

In order to improve design efficiency and reduce design cost, a new method combining numerical simulation with experimental verification was proposed in this study. Firstly, controllable process parameters such as continuous casting speed and cooling water flow rate, are robustly designed according to the numerical simulation results of flow field, process experiments were subsequently done on a newly developed continuous casting machine of lead slice, then the robust optimal design for the continuous casting process were gained. The results show that the cooling water flow significantly affects axial thickness of the lead slice, while the casting speed determines mainly its circumferential thickness. When the casting speed is between 280L/min and 320L/min, and the cooling water flow rate is between 950r/min and 1100r/min at the same time, the axial thickness and circumferential thickness can been kept respectively in1.0±0.03mm and 1.0±0.1mm, which meet the robust design requirements.

• Periodical: Applied Mechanics and Materials (Volumes 246-247)
• Pages: 341-345
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.246-247.341
• Online since: December 2012
• Authors: Shi Lun Zuo
• Keywords: Continuous Casting, Numerical Simulation, Process Parameter, Robust Optimization

References

[1] ZUO Shilun, WANG Jaxu, LI Taifu, "Soft-sensor for estimation of lead slices thickness in continuous casting process",Advenced Maunfacturing Rsearch,Vol.323, No.40, pp.40-45, 2011.

DOI: 10.4028/www.scientific.net/amr.323.40

[2] Kevin Alam, Musa R.Kamal, "A robust optimization of injection modeling runner balancing", Computers and Chemical Engineering, No.29, pp.1934-1944, 2005.

DOI: 10.1016/j.compchemeng.2005.04.005

[3] B.Srinivasan, D.Bonvin, E.Visser, S.Palanki, "Dynamic optimization of batch process II. Role of measurements in handling uncertainty", Computers and Chemical Engineering, No.27, pp.27-44,2002.

DOI: 10.1016/s0098-1354(03)00005-x

[4] Sivapathasekaran C., Mukherjee S., Ray A., "ANN modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant", Bioresource Technology, No.101, p.2884–2887, 2010.

DOI: 10.1016/j.biortech.2009.09.093

[5] Hans-Georg Beyer, Bernhard Sendhoff, " Robust optimization-A comprehensive survey", Comput. Methods Appl. Mech. Engrg. No.196, pp.3190-3218, 2007.

DOI: 10.1016/j.cma.2007.03.003

[6] SUN Guangyong, LI Guangyao, CHEN Tao, "Sheet Metal Forming Based Six Sigma Robust Optimization Design", CHINESE JOURNAL OF MECHANICAL ENGINEERING, Vol.44, No.11, pp.248-254, 2008.

DOI: 10.3901/jme.2008.11.248

[7] Zheng Zhen-xing, Xia Wei, Zhou Zhao-yao, "Optimization of Process Parameters of Powder Injection Molding Based on Numerical Simulation",Journal of South China University of Technology(Natural Science Edition),Vol.37, No.2, pp.49-53, 2009.

[8] Aubin J, Fletcher D F, Xuereb C, "Modeling turbulent flow in stirred tanks with CFD:the influence of the modeling aooroach, turbulence model and numerical scheme", Experimental Thermal and Fluid Science, Vol.28, No.5, pp.431-445, 2004.

DOI: 10.1016/j.expthermflusci.2003.04.001