Advertisement

Modeling and Simulation of Al6082 MMC of Gravity Die Casting for Solidification Time

  • Harendra PalEmail author
  • Dinesh Kumar Kasdekar
  • Sharad Agrawal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)

Abstract

The solidification of cast part remains a process of excellent interest. It immediately impacts the production rate, casting defects, and mechanical property of casting. The phenomenon of solidification of cast part is complicated in foundry industry as well as the modeling and simulation needed in industry in advance than it is far in reality undertaken. This research specializes in the impact of casting method parameters of Al6082 MMC. Modeling and simulation examine the casting solidification time in the foundry in gravity die casting technique. The design of experiment is done with the help of full factorial design (FFD). The casting technique parameters are pouring temperature, pouring rate, and die preheat temperature (DHT) on solidification time that has been studied. This paper explains the optimization of casting method parameters using genetic algorithms. It also gives the information about the generation of optimization models, simulation, and methodology used and obtains the optimum process parameters. The predicted trials have been used to comparatively compare with simulation and experimental results, and the simulated comparison results are observed in a proper way.

Keywords

Gravity die casting Solidification time Click2CAST ANOVA Genetic algorithms 

References

  1. 1.
    Choudhari, C.M., Narkhede, B.E., Mahajan, S.K.: Finite element simulation of temperature distribution during solidification in cylindrical sand casting with experimental validation. In: 4th International and 25th All India Machine Tool Design and Research (AIMTDR), Jadavpur University, Kolkata, India, vol. 1, pp. 3–8 (2012)Google Scholar
  2. 2.
    Choudhari, C.M., Padalkar, K.J., Dhumal, K.K., Narkhede, B.E., Mahajan, S.K.: Defect free casting by using simulation software. Appl. Mech. Mater. 313, 1130–1134 (2013)CrossRefGoogle Scholar
  3. 3.
    DeGarmo, E.P., Black, J.T., Kohser, R.A.: Materials and Processes in Manufacturing. Prentice-Hall Inc., New Jersey (1997)Google Scholar
  4. 4.
    Oza, A.D., Patel, T.M.: Analysis and validation of gravity die casting process using pro-cast. Int. J. Appl. Innov. Eng. Manag. 2(4), 46–52 (2013)Google Scholar
  5. 5.
    Reddy, A.C., Rajanna, C.: Design of gravity die casting process parameters of Al–Si–Mg alloys. J. Mach. Form. Technol. 1(½), 1–25 (2009)Google Scholar
  6. 6.
    Tiwari, S.N.: On fluidity characteristics of metals and alloys. Indian Foundry J. 44(5), 77–82 (1998)Google Scholar
  7. 7.
    Babington, W., Kleppinger, D.H.: Aluminum die castings—the effect of process variables on their properties. In: Proceeding of ASTM, pp. 169–174 (1951)Google Scholar
  8. 8.
    Choudhari, C.M., Narkhede, B.E., Mahajan, S.K.: Methoding and simulation of LM 6 sand casting for defect minimization with its experimental validation. Procedia Eng. 97, 1145–1154 (2014)CrossRefGoogle Scholar
  9. 9.
    Choudhari, C.M., Narkhede, B.E., Mahajan, S.K.: Casting design and simulation of cover plate using auto cast-X software for defect minimization with experimental validation. Procedia Mater. Sci. 4, 786–797 (2014)CrossRefGoogle Scholar
  10. 10.
    Dabade, U.A., Bhedasgaonkar, R.C.: Casting defect analysis using design of experiments (DOE) and computer aided casting simulation technique. Procedia CIRP. 7, 616–621 (2013)CrossRefGoogle Scholar
  11. 11.
    Hussainy, S.F., Mohiuddin, M.V., Laxminarayana, P., Krishnaiah, A., Sundarrajan, S.: A practical approach to eliminate defects in gravity die cast Al-Alloy casting using simulation software. Int. J. Res. Eng. Technol. 04(1), 114–124 (2015)CrossRefGoogle Scholar
  12. 12.
    Anglada, E., Meléndez, A., Vicario, I., Arratibel, E., Aguillo, I.: Adjustment of a high pressure die casting simulation model against experimental data. Procedia Eng. 13, 966–973 (2015)CrossRefGoogle Scholar
  13. 13.
    Venik, A.I.: Analysis of metal flow in die castings. Machinery 99, 1501–1505 (1961)Google Scholar
  14. 14.
    Ravi, B.: Casting simulation and optimization: benefits, bottlenecks, and best practices. Tech. Paper Indian Foundry J. 54, 47 (2008)Google Scholar
  15. 15.
    Hussainy, S.F., Mohiuddin, M.V., Laxminarayan, P., Krishnaiah, A.: A practical approach to eliminate defects in gravity die cast al-alloy casting using simulation software. Int. J. Res. Eng. Technol. 4, 114–124 (2015)Google Scholar
  16. 16.
    Ravi, B.: Casting simulation-best practices. Indian Foundry Congr. 58, 19–29 (2010)Google Scholar
  17. 17.
    Manjunath Swamy, H.M., Nataraj, J.R.: Design optimization of gating system by fluid flow and solidification simulation for front axle housing. Int. J. Eng. Res. Dev. 4(6) (2012)Google Scholar
  18. 18.
    Kushwah, S.S., Kasdekar, D.K., Agrawal, S.: Mathematical and prediction modeling of material removal rate for evaluating the effects of process parameters. In: Ambient Communications and Computer Systems, Springer Nature Singapore, Chapter No. 44 (2018). Book ISBN: 978-981-10-7385-4Google Scholar
  19. 19.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., MA (1989)Google Scholar
  20. 20.
    Soodamani, R., Liu, Z.Q.: GA based learning for a model—based object recognition system. Int. J. Approximate Reasoning 23, 85–109 (2000)CrossRefGoogle Scholar
  21. 21.
    Chen, P.H., Chang, H.C.: Large-scale economic dispatch by genetic algorithm. IEEE Trans. Power Syst. 10(4), 1919–1926 (1995)CrossRefGoogle Scholar
  22. 22.
    Design and Analysis of Experiments. Montgomery John Wiley and Sons, vol. 18, pp. 163 (2001)Google Scholar
  23. 23.
    Palanikumar, K.: Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. Int. J. Adv. Manuf. Technol. 36, 19–27 (2008)CrossRefGoogle Scholar
  24. 24.
    Kasdekar, D.K., Parashar, V., Soni, P.: Optimization of machining parameters in electro discharge machining of Al6061–Cu–Sic–graphite metal matrix composite. Mater. Sci. Forum 860, 61–64 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Harendra Pal
    • 1
    Email author
  • Dinesh Kumar Kasdekar
    • 1
  • Sharad Agrawal
    • 1
  1. 1.Department of Mechanical EngineeringMadhav Institute of Technology and ScienceGwaliorIndia

Personalised recommendations