Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network

Document Type : Article

Authors

1 Institute of Natural Sciences, Sakarya University, Sakarya, Turkey

2 Department of Mechanical Engineering, Bayburt University, 69000, Bayburt, Turkey.

3 Department of Industrial Engineering, Sakarya University, 54187, Sakarya, Turkey.

Abstract

Casting simulation programs are the computer programs that digitally model the casting of an alloy in the sand, shell or permanent mold and then the cooling and solidification processes. However, obtaining consistent results from the casting modeling depends on providing many parameters and boundary conditions accurately. Critical fraction of solid (CFS), which is one of the most important of these parameters, is defined as the point where the solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies depending on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process carried out using low pressure die casting method, is predicted by using artificial neural network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values obtained from this validation range from 33% to 61%.

Keywords

Main Subjects


1. Kay_kc_, R. Comparison of classical and computer aided engineering techniques used in casting a large steel part", J. Fac. Eng. Arch. Gazi Univ., 23(2), pp. 257-265 (2008). 2. Kay_kc_, R. Use of computer modelling in predicting microporosity in commercial aluminum alloy", 66th World Foundry Congress, 1, Istanbul, Turkey, pp. 235- 246 (2004). 3. Stefanescu, D.M. Computer simulation of shrinkage related defects in metal castings - a review", Int. J. Cast Metal Res., 18(3), pp. 129-143 (2005). 4. Hsu, F.-Y., Jolly, M.R., and Campbell, J. Vortex-gate design for gravity casting", Int. J. Cast Metal Res., 19(1), pp. 38-44 (2006). 5. Kay_kc_, R. and Akar, N., Computer Aided Casting Design with Solidcast, DTS, Sakarya, Turkey (2010). 6. ASM International Handbook Committee, Properties and Selection: Nonferrous Alloys and Special-Purpose Materials, ASM International, Ohio (1990). 7. Campbell, J., Castings Practice: The Ten Rules of Castings, Butterworth-Heinemann, Amsterdam (2004). 8. Kay_kc_, R. and C_ olak, M. Investigation of e_ect of grain re_ning on feeding of a sand cast Etial160 aluminium alloy", 5th International Advanced Technologies Symposium, 1, Karabuk, Turkey, pp. 742-748 (2009). 9. Schmidt, D., SOLIDCast Training Course Workbook, Finite Solutions Inc, Wisconsin (2014). 10. Djurdjevic, M.B., Sokolowski, J.H., and Odanovic, Z. Determination of dendrite coherency point characteristics using _rst derivative curve versus temperature", J. Therm. Anal. Calorim., 109, pp. 875-882 (2012). 11. Veldman, N.L.M., Dahle, A.K., StJohn, D.H., and Arnsberg, L. Dendrite coherency of Al-Si-Cu alloys", Metall. and Mat. Trans. A, 32, pp. 147-155 (2001). 12. Akar, N., Kay_kc_, R., and K_sao_glu, A.K. Modelling of critical solid fraction factor depending on mold temperature and grain size of Al-4,3cu alloy poured into permenant mold", Journal of Polytechnic, 17(2), pp. 83-89 (2014). 13. Cain, G., Arti_cial Neural Networks: New Research, Nova Science Publishers, New York (2016). 14. Moghaddam, M.A., Golmezerji, R., and Kolahan, F. Simultaneous optimization of joint edge geometry and process parameters in gas metal arc welding using integrated ANN-PSO approach", Scientia Iranica B, 24(1), pp. 260-273 (2017). 15. Ate_s, H., Dursun, B., and Kurt, H. Estimation of mechanical properties of welded S355J2+N steel via the arti_cial neural network", Scientia Iranica B, 23(2), pp. 609-617 (2016). 16. Soundararajan, R., Ramesh, A., Sivasankaran, S., and Vignesh, M. Modeling and analysis of mechanical properties of aluminium alloy (A413) reinforced with boron carbide (B4C) processed through squeeze casting process using arti_cial neural network model and statistical technique", Materials Today: Proceedings, 4(2), pp. 2008-2030 (2017). 17. Canakci, A., Varol, T., and Ozsahin, S. Arti_cial neural network to predict the e_ect of heat treatment, reinforcement size, and volume fraction on AlCuMg alloy matrix composite properties fabricated by stir casting method", Int. J. Adv. Manuf. Technol., 78, pp. 305-317 (2015). 18. Altinkok, N. Use of arti_cial neural network for prediction of mechanical properties of _-Al2O3 particulate-reinforced Al-Si10Mg alloy composites prepared by using stir casting process", J. Compos. Mater., 40(9), pp. 779-796 (2006). 19. Pham, Q.T. and Phan, T.K.D. Apply neural network for improving production planning at Samarang petrol mine", Int. J. of Intell. Comp. & Cyber., 9(2), pp. 126- 143 (2016). 20. S_enyi_git, E. and Atici, U. Arti_cial neural network models for lot-sizing problem: a case study", Neural Comput. & Applic., 22(6), pp. 1039-1047 (2013). 21. Simeunovi_c, N., Kamenko, I., Bugarski, V., Jovanovi_c, M., and Lali_c, B. Improving workforce scheduling using arti_cial neural networks model", Adv. Produc. Engineer. Manag., 12(4), pp. 337-352 (2017). 3312 C_ . Teke et al./Scientia Iranica, Transactions B: Mechanical Engineering 26 (2019) 3304{3312 22. Ganesan, N., Venkatesh, K., Rama, M.A., and Palani, A.M. Application of neural networks in diagnosing cancer disease using demographic data", Int. J. of Comput. Appl., 1, pp. 76-85 (2010). 23. Chougrad, H., Zouaki, H., and Alheyane, O. Deep convolutional neural networks for breast cancer screening", Comput. Methods Programs Biomed., 157, pp. 19-30 (2018). 24. Shioji, M., Yamamoto, T., Ibata, T., Tsuda, T., Adachi, K., and Yoshimura, N. Arti_cial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women", BMC Res. Notes, 10, pp. 590-595 (2017). 25. Murphy, M.C., Manduca, A., Trzasko, J.D., Glaser, K.J., Huston J. 3rd, and Ehman, R.L. Arti_cial neural networks for sti_ness estimation in magnetic resonance elastography", Magn. Reson. Med., 80(1), pp. 351-360 (2018). 26. Nilsaz-Dezfouli, H., Abu-Bakar, M.R., Arasan, J., Adam, M.B., and Pourhoseingholi, M.A. Improving gastric cancer outcome prediction using single timepoint arti_cial neural network models", Cancer Inform., 16, pp. 1-11 (2017). 27. Tsai, C.F. and Wu, J.W. Using neural network ensembles for bankruptcy prediction and credit scoring", Expert Syst. Appl., 34, pp. 2639-2649 (2008). 28. Ko, P.C. and Lin, P.C. Resource allocation neural network in portfolio selection", Expert Syst. Appl., 35, pp. 330-337 (2008). 29. Haider, A. and Hanif, M.N. Ination forecasting in Pakistan using arti_cial neural networks", Pak. Econ. Soc. Rev., 47(1), pp. 123-138 (2009). 30. Etebari, F. and Naja_ A.A. Intelligent choice-based network revenue management", Scientia Iranica E, 23(2), pp. 747-756 (2016). 31. Davis, J.R., Aluminum and Aluminum Alloys, ASM International, Ohio (1993). 32. ASM International Technical Book Committee, Casting Design and Performance, ASM International, Ohio (2009). 33. Kursun Bahadir, S., Sahin, U.K., and Kiraz, A. Modeling of surface temperature distributions on powered e-textile structures using an arti_cial neural network", Text. Res. J., 89(3), pp. 311-321 (2019). DOI:10.1177/0040517517743689 34. Flores, J.A., Focus on Arti_cial Neural Networks, Nova Science Publishers, New York (2011).