Abstract
The effects of process parameters on temperature difference (ΔTRECS) of the squeeze casting part were studied by employing artificial neural network (ANN) and Procast software when cooling up to recrystallization temperature, such as interface heat transfer coefficient of metal/cavity die (h1), applied pressure (Ap), interface heat transfer coefficient of metal/male die (h2), die pre-heat temperature (Dt) and pouring temperature (Pt). An ANN model on the relationship between the processing parameters and ΔTRECS was constructed. The test results on performance of the trained network show that the ANN model can predict ΔTRECS with reasonable accuracy. Employing the ANN model the following conclusions could be made from this work: as far as the influence of process parameters on ΔTRECS are concerned, the most important and the secondary parameter are Dt and Ap. H1 and Ap increase within a certain range, ΔTRECS are found to increase. When Ap and h1 are above their respective critical point, the ΔTRECS decreased rapidly. Pt, h2 and Dp increase within a certain range, ΔTRECS are found to decrease.
Similar content being viewed by others
References
Ghomashchi MR, Vikhrov A (2000) Squeeze casting: an overview. J Mater Process Technol 101:1–9
Maleki A, Shafyei A, Niroumand B (2009) Effects of squeeze casting parameters on the microstructure of LM13 alloy. J Mater Process Technol 209:3790–3797
Zhao ZD, Chen Q, Tang ZJ et al (2010) Microstructure evolution and mechanical properties of Al2O3sf/AZ91D magnesium matrix composites fabricated by squeeze casting. J Mater Sci 45:3419–3425
Vosniakos G-C, Galiotou V, Pantelis D et al (2009) The scope of artificial neural network metamodels for precision casting process planning. Robot CIM INT Manuf 25:909–916
Wang RJ, Li XH, Wu QD et al (2009) Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm. Int J Adv Manuf Technol 42:1035–1042
Zhou SJ, Zhou DW, He HJ et al (2000) The liquid die forging process experiment of the die steel forging die. J Hubei Automot Indust Inst 14(2):43–49
Li MH (2007) Numerical simulation of squeeze casting 2007 Master’s Thesis, Wuhan University of Technology, 03
Lee PD, Chirazi A, Atwood RC, Wang W (2004) Multiscale modelling of solidification microstructures, including microsegregation and microporosity, in an Al–Si–Cu alloy. Mater Sci Eng A 365:57–65
Rai JK, Lajimi AM, Xirouchakis P (2008) An intelligent system for predicting HPDC process variables in interactive environment. J Mater Process Technol 203:72–79
Chen YG, Ye WM, Zhang KN (2009) Strength of copolymer grouting material based on orthogonal experiment. J Cent S Univ Tech 16(1):143–148
Wang BH, Jin Y, Luo YG (2010) Parametric optimization of EQ6110HEV hybrid electric bus based on orthogonal experiment design. Int J Automot Tech 12:119–125
Singh SK (2010) Development of ANN model and study the effect of temperature on strain ratio and sensitivity index of EDD steel. Int J Mater Form 3:259–266
Cellere A, Lucchetta G (2010) Identification of crims model parameters for warpage prediction in injection moulding simulation. Int J Mater Form 3(Suppl 1):37–40
Acknowledgment
The work was supported by the Science Fund of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body (30815004).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, R.J., Zeng, J. & ZHOU, Dw. Determination of temperature difference in squeeze casting hot work tool steel. Int J Mater Form 5, 317–324 (2012). https://doi.org/10.1007/s12289-011-1061-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12289-011-1061-8