Abstract
High-pressure die casting is a versatile process for producing engineered metal parts. There are many attributes involved which contribute to the complexity of the process. It is essential for the engineers to optimize the process parameters and improve the surface quality. However, the process parameters are interdependent and in conflict in a complicated way, and optimization of the combination of processes is time-consuming. In this work, an evaluation system for the surface defect of casting has been established to quantify surface defects, and artificial neural network was introduced to generalize the correlation between surface defects and die-casting parameters, such as mold temperature, pouring temperature, and injection velocity. It was found that the trained network has great forecast ability. Furthermore, the trained neural network was employed as an objective function to optimize the processes. The optimal parameters were employed, and the castings with acceptable surface quality were achieved.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Kong LX, She FH, Gao WM (2008) Integrated optimization system for high pressure die casting processes. J Mater Process Technol 201(1–3):629–634 doi:10.1016/j.jmatprotec.2007.11.250
Krimpenis A, Benardos PG, Vosniakos GC, Koukouvitaki A (2006) Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms. Int J Adv Manuf Technol 27(5–6):509–517 doi:10.1007/s00170-004-2218-0
Lee SG, Gokhale AM, Patel GR (2006) Effect of process parameters on porosity distributions in high-pressure die-cast AM50 Mg-alloy. Mater Sci Eng A 427(1–2):99–111 doi:10.1016/j.msea.2006.04.082
Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Pearson Education Limited, Tasmania
Rao KP, Prasad YKDV (1995) Neural network approach to flow stress evaluation in hot deformation. J Mater Process Technol 53(3–4):552–566 doi:10.1016/0924-0136(94)01744-L
Shelesh-Nezhah K, Siores E (1997) An intelligent system for plastic injection molding process design. J Mater Process Technol 63(1–3):458–462 doi:10.1016/S0924-0136(96)02664-7
Lam SSY, Petri KL, Smith AE (2000) Prediction and optimization of a ceramic casting process using a hierarchical hybrid system of neural networks and fuzzy logic. IIE Trans 32(1):83–91
Yarlagadda PKDV, Chiang ECW (1999) A neural network system for the prediction of process parameters in pressure die casting. J Mater Process Technol 89–90:583–590 doi:10.1016/S0924-0136(99)00071-0
Park YW, Rhee S (2008) Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser. Int J Adv Manuf Technol 37(9–10):1014–1021 doi:10.1007/s00170-007-1039-3
Karunakar DB, Datta GL (2007) Prevention of defects in castings using back propagation neural networks. Int J Adv Manuf Technol 39(11–12):1111–1124 doi:10.1007/s00170-007-1289-0
Tentardini EK, Kunrath AO, Aguzzoli C (2008) Soldering mechanisms in materials and coatings for aluminum die casting. Surf Coat Tech 202(16):3764–3771
Zhang ZX, Sun CZ (2000) Neuro-Fuzzy and Soft Computting (In Chinese), 1st edn. Xi’an Jiaotong University, Xi’an, 94–96
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zheng, J., Wang, Q., Zhao, P. et al. Optimization of high-pressure die-casting process parameters using artificial neural network. Int J Adv Manuf Technol 44, 667–674 (2009). https://doi.org/10.1007/s00170-008-1886-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-008-1886-6