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Technical Brief

Design Exploration for Determining the Set Points of Continuous Casting Operation: An Industrial Application

[+] Author and Article Information
Rishabh Shukla

Tata Consultancy Services,
Pune 411013, India
e-mail: rishabh.shukla1@tcs.com

Sharad Goyal

Tata Consultancy Services,
Pune 411013, India
e-mail: sharad.goyal@ceat.in

Amarendra K. Singh

Tata Consultancy Services,
Pune 411013, India
e-mail: amarendra.singh@tcs.com

Jitesh H. Panchal

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: panchal@purdue.edu

Janet K. Allen

The Systems Realization Laboratory @ OU,
The University of Oklahoma,
Norman, OK 73019
e-mail: janet.allen@ou.edu

Farrokh Mistree

The Systems Realization Laboratory @ OU,
The University of Oklahoma,
Norman, OK 73019
e-mail: farrokh.mistree@ou.edu

1Present address: Research and Development Centre, CEAT Tires, Vadodara, India.

2Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received June 20, 2014; final manuscript received February 6, 2015; published online March 2, 2015. Assoc. Editor: Xiaoping Qian.

J. Manuf. Sci. Eng 137(3), 034503 (Jun 01, 2015) (5 pages) Paper No: MANU-14-1334; doi: 10.1115/1.4029786 History: Received June 20, 2014; Revised February 06, 2015; Online March 02, 2015

To compete with other materials and/or contribute toward light-weighting of vehicles, newer grades of steel are continuously invented and experimented upon. Due to the costs and time involved in such developments, manufacture of new grades of steel at an industrial scale is difficult. We propose a method that is useful for steel manufacturers interested in producing a steel product mix with new grades of steels by predicting the required change in the operating set points of each unit operation in the manufacturing chain of products with the new grade of steel. Here, we demonstrate a method to determine the set points of one unit operation, continuous casting which is measured in terms of conflicting objectives including productivity, quality, and production costs. These parameters are sensitive to the operating set points of casting speed, superheat, mold oscillation frequency, and secondary cooling conditions. To ensure targeted performance and address the challenges of uncertainty and conflicting objectives, an integrated computational method based on surrogate models and the compromise decision support problem (cDSP) is presented. The method is used to explore the design space available for casting operations and determine operating set points to meet requirements imposed on the caster from subsequent downstream processes. This method is of value to the steel industry and enables the rapid and cost effective production of a product mix with a new grade of steel.

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References

Matlock, D. K., and Speer, J. G., 2009, “Third Generation of AHSS: Microstructure Design Concepts,” Microstructure and Texture in Steels, Springer, London, pp. 185–205. [CrossRef]
Pal, D., Patil, N., Zeng, K., and Stucker, B., 2014, “An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling,” ASME J. Manuf. Sci. Eng., 136(6), p. 061022. [CrossRef]
Gupta, A., Goyal, S., Padmanabhan, K. A., and Singh, A. K., “Inclusions in Steel: Micro–Macro Modelling Approach to Analyse the Effects of Inclusions on the Properties of Steel,” Int. J. Adv. Manuf. Technol. (in press). [CrossRef]
Singh, A. K., Goyal, S., Kumar, P., Reddy, N., Padmanabhan, K. A., Palai, P., Javed, Y., Tripathy, P. K., Mahashabde, V., and Venugopalan, T., 2013, “Prediction and Control of Center-Line Segregation in Continuously Cast Slabs,” Proceedings of the ISISTM Conference, Jamshedpur, India.
Singh, A. K., Pardeshi, R., and Goyal, S., 2011, “Integrated Modeling of Tundish and Continuous Caster to Meet Quality Requirements of Cast Steels,” Proceedings of the First World Congress on Integrated Computational Materials Engineering, Vol. 1, pp. 81–85.
Mistree, F., Hughes, O. F., and Bras, B. A., 1993, “The compromise Decision Support Problem and Adaptive Linear Programming Algorithm,” Structural Optimization: Status and Promise, M. P.Kamat, ed., AIAA, Washington, DC, pp. 247–286.
Mauder, T., Sandera, C., Stetina, J., and Masarik, M., 2012, “A Fuzzy Logic Approach for Optimal Control of Continuous Casting Process,” Proceedings of Metal 2012 Conference, Brno, Czech Republic.
Han, Z., and Zhang, J., 2011, “An Advanced Dynamic Secondary Cooling Control Model for Bloom Castings,” Adv. Mater. Res., 154–155, pp. 171–178. [CrossRef]
Mauder, T., Sandera, C., Stetina, J., and Seda, M., 2011, “Optimization of the Quality of Continuously Cast Steel Slabs Using the Firefly Algorithm,” Mater. Technol., 45(4), pp. 347–350.
Lally, B., Henein, H., and Biegler, L. T., 1988, “Prediction of Optimal Operating Parameters for Continuous Casting of Billets,” Department of Chemical Engineering, Carnegie Mellon University, Paper No. 99, pp. 1055–1069.
Camisani-Calzolari, F. R., Craig, I. K., and Pistorius, P. C., 1998, “Specification Framework for Control of the Secondary Cooling Zone in Continuous Casting,” ISIJ Int., 38(5), pp. 447–452. [CrossRef]
Chen, W., Zhang, Y. Z., Zhu, L. G., Zhang, C. Z., and Wang, B. X., 2010, “Optimization for Secondary Cooling Parameters in Continuous Casting of High Carbon Chromium Bearing Steel,” Adv. Mater. Res., 83–86, pp. 465–472. [CrossRef]
Van der Spuy, D. D., Craig, I. K., and Pistorius, P. C., 1999, “An Optimization Procedure for the Secondary Cooling Zone of a Continuous Billet Caster,” J. S. Afr. Inst. Min. Metall., 99(1), pp. 49–56.
Chen, W., Allen, J. K., and Mistree, F., 1997, “The Robust Concept Exploration Method for Enhancing Concurrent Systems Design,” Concurrent Eng. Res. Appl., 5(3), pp. 203–217. [CrossRef]
Smith, W. F., Milisavljevic, J., Sabeghi, M., Allen, J. K., and Mistree, F., 2014, “Accounting for Uncertainty and Complexity in the Realization of Engineered Systems,” International Conference on Complex Systems Design & Management, Paris, France, Nov. 12–14, Paper No. 55.
Cramb, A. W., 2010, The Making, Shaping and Treating of Steel-Casting Volume, 11th ed., AIST, Warrendale, PA.
Witherell, P., Feng, S., Simpson, T. W., John, D. B. S., Michaleris, P., Liu, Z. K., Chen, L. Q., and Martukanitz, R., 2014, “Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models,” ASME J. Manuf. Sci. Eng., 136(6), p. 061025. [CrossRef]
Hu, Y., Li, Z., Li, K., and Yao, Z., 2014, “Predictive Modeling and Uncertainty Quantification of Laser Shock Processing by Bayesian Gaussian Processes With Multiple Outputs,” ASME J. Manuf. Sci. Eng., 136(4), p. 041014. [CrossRef]
Shukla, R., Goyal, S., Singh, A. K., Allen, J. K., Panchal, J. H., and Mistree, F., 2014, “An Approach to Robust Process Design for Continuous Casting of Slab,” International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2014, Buffalo, NY, Paper No. DETC2014 34208.

Figures

Grahic Jump Location
Fig. 2

Region depicting set of weights for meeting requirement on (a) CLS, (b) productivity, and (c) variance in productivity

Grahic Jump Location
Fig. 3

Superposition of regions depicting sets of weights for each goal

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