Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2014, Article ID 293976, 12 pages
http://dx.doi.org/10.1155/2014/293976
Research Article

Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

1Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India
2Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh 491001, India

Received 17 May 2014; Accepted 30 September 2014; Published 27 October 2014

Academic Editor: R. Saravanan

Copyright © 2014 Manjunath Patel Gowdru Chandrashekarappa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. J. Wang, J. Zeng, and D.-W. Zhou, “Determination of temperature difference in squeeze casting hot work tool steel,” International Journal of Material Forming, vol. 5, no. 4, pp. 317–324, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. R. J. Wang, W. F. Tan, and D. W. Zhou, “Effects of squeeze casting parameters on solidification time based on neural network,” International Journal of Materials and Product Technology, vol. 46, no. 2-3, pp. 124–140, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Krimpenis, P. G. Benardos, G.-C. Vosniakos, and A. Koukouvitaki, “Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms,” International Journal of Advanced Manufacturing Technology, vol. 27, no. 5-6, pp. 509–517, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. L. J. Yang, “The effect of casting temperature on the properties of squeeze cast aluminium and zinc alloys,” Journal of Materials Processing Technology, vol. 140, no. 1–3, pp. 391–396, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Maleki, B. Niroumand, and A. Shafyei, “Effects of squeeze casting parameters on density, macrostructure and hardness of LM13 alloy,” Materials Science and Engineering A, vol. 428, no. 1-2, pp. 135–140, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. L. J. Yang, “The effect of solidification time in squeeze casting of aluminium and zinc alloys,” Journal of Materials Processing Technology, vol. 192-193, pp. 114–120, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. H. Lee, H. S. Kim, C. W. Won, and B. Cantor, “Effect of the gap distance on the cooling behavior and the microstructure of indirect squeeze cast and gravity die cast 5083 wrought Al alloy,” Materials Science and Engineering A, vol. 338, no. 1-2, pp. 182–190, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. T. M. Yue, “Squeeze casting of high-strength aluminium wrought alloy AA7010,” Journal of Materials Processing Technology, vol. 66, no. 1–3, pp. 179–185, 1997. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Zhang, W. W. Zhang, H. D. Zhao, D. T. Zhang, and Y. Y. Li, “Effect of pressure on microstructures and mechanical properties of Al-Cu-based alloy prepared by squeeze casting,” Transactions of Nonferrous Metals Society of China, vol. 17, no. 3, pp. 496–501, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Hajjari and M. Divandari, “An investigation on the microstructure and tensile properties of direct squeeze cast and gravity die cast 2024 wrought Al alloy,” Materials and Design, vol. 29, no. 9, pp. 1685–1689, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Maleki, A. Shafyei, and B. Niroumand, “Effects of squeeze casting parameters on the microstructure of LM13 alloy,” Journal of Materials Processing Technology, vol. 209, no. 8, pp. 3790–3797, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Senthil and K. S. Amirthagadeswaran, “Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method,” Journal of Mechanical Science and Technology, vol. 26, no. 4, pp. 1141–1147, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Senthil and K. S. Amirthagadeswaran, “Experimental study and squeeze cast process optimization for high quality AC2A aluminium alloy castings,” Arabian Journal of Science and Engineering, vol. 39, no. 3, pp. 2215–2225, 2013. View at Google Scholar
  14. P. Senthil and K. S. Amirthagadeswaran, “Enhancing wear resistance of squeeze cast AC2A aluminium alloy,” International Journal of Engineering, Transactions A: Basics, vol. 26, no. 4, pp. 365–374, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Vijian and V. P. Arunachalam, “Optimization of squeeze cast parameters of LM6 aluminium alloy for surface roughness using Taguchi method,” Journal of Materials Processing Technology, vol. 180, no. 1–3, pp. 161–166, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. S.-B. Bin, S.-M. Xing, L.-M. Tian, N. Zhao, and L. Li, “Influence of technical parameters on strength and ductility of AlSi9Cu3 alloys in squeeze casting,” Transactions of Nonferrous Metals Society of China (English Edition), vol. 23, no. 4, pp. 977–982, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. M. B. Parappagoudar, D. K. Pratihar, and G. L. Datta, “Forward and reverse mappings in green sand mould system using neural networks,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 239–260, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Benguluri, P. R. Vundavilli, R. P. Bhat, and M. B. Parappagoudar, “Forward and reverse mappings in metal casting—a step towards quality casting and automation,” AFS Transactions—American Foundry Society, vol. 119, pp. 19–34, 2011. View at Google Scholar
  19. P. K. D. V. Yarlagadda and E. C. W. Chiang, “Neural network system for the prediction of process parameters in pressure die casting,” Journal of Materials Processing Technology, vol. 89, pp. 583–590, 1999. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Mandal and P. Roy, “Modeling the compressive strength of molasses-cement sand system using design of experiments and back propagation neural network,” Journal of Materials Processing Technology, vol. 180, no. 1–3, pp. 167–173, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Abhilash, M. A. Joseph, and P. Krishna, “Prediction of dendritic parameters and macro hardness variation in permanent mould casting of Al-12% Si alloys using artificial neural networks,” Fluid Dynamics & Materials Processing, vol. 2, pp. 211–220, 2006. View at Google Scholar
  22. A. B. Sharkawy, “Prediction of surface roughness in end milling process using intelligent systems: a comparative study,” Applied Computational Intelligence and Soft Computing, vol. 2011, Article ID 183764, 18 pages, 2011. View at Publisher · View at Google Scholar
  23. M. B. Parappagoudar, D. K. Pratihar, and G. L. Datta, “Modelling of input-output relationships in cement bonded moulding sand system using neural networks,” International Journal of Cast Metals Research, vol. 20, no. 5, pp. 265–274, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. M. B. Parappagoudar, D. K. Pratihar, and G. L. Datta, “Neural network-based approaches for forward and reverse mappings of sodium silicate-bonded, carbon dioxide gas hardened moulding sand system,” Materials and Manufacturing Processes, vol. 24, no. 1, pp. 59–67, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. J. K. Kittur and M. B. Parappagoudar, “Forward and reverse mappings in die casting process by neural network-based approaches,” Journal for Manufacturing Science and Production, vol. 12, no. 1, pp. 65–80, 2012. View at Google Scholar
  26. G. C. M. Patel, R. Mathew, and P. Krishna, “Effects of squeeze casting process parameters on density of LM20 alloy,” in Proceedings of the 4th International Joint Conference on Advances in Engineering and Technology (AET '13), pp. 776–785, National Capital Region, India, December 2013.
  27. G. C. M. Patel, R. Mathew, P. Krishna, and M. B. Parappagoudar, “Investigation of squeeze cast process parameters effects on secondary dendrite arm spacing using statistical regression and artificial neural network models,” Procedia Technology, vol. 14, pp. 149–156, 2014. View at Google Scholar
  28. G. C. M. Patel, P. Krishna, and M. B. Parappagoudar, “Prediction of squeeze cast density using fuzzy logic based approaches,” Journal for Manufacturing Science and Production, vol. 14, no. 2, pp. 125–140, 2014. View at Google Scholar
  29. G. C. M. Patel, P. Krishna, and M. B. Parappagoudar, “Prediction of secondary dendrite arm spacing in squeeze casting using fuzzy logic based approaches,” Achieves of Foundry Engineering, vol. 15, no. 1, pp. 51–68, 2015. View at Google Scholar