An intelligent system for low-pressure die-cast process parameters optimization

International Journal of Advanced Manufacturing Technology

Volumn 65, Issue 1-4, 2013, Pages 517-524

  Zhang, Liqiang ^a,b   Wang, Rongji ^a  

^a CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY   (China)

^b HUNAN UNIVERSITY   (China)

Author keywords

Artificial neural network; Genetic algorithm; LPDC; Numerical simulation; Process parameters

Indexed keywords

ALUMINUM ALLOY PRODUCTS; ANN MODELS; COMPLEX RELATIONSHIPS; COST SAVING; DIE-CAST; EFFECTIVE TOOL; FITNESS FUNCTIONS; LEARNING VECTOR QUANTIZATION; LPDC; OPTIMIZED PARAMETER; OPTIMUM PROCESS CONDITIONS; ORTHOGONAL ARRAY DESIGNS; PART QUALITY; PREPARATION PROCESS; PROCESS CONDITION; PROCESS PARAMETERS; PROCESS PARAMETERS OPTIMIZATIONS; QUALITY INDICES; THIN WALLED COMPONENTS; THIN-WALLED; THIN-WALLED CASTINGS; TRAINING SAMPLE;

BACKPROPAGATION ALGORITHMS; COMPUTER SIMULATION; GENETIC ALGORITHMS; INTELLIGENT SYSTEMS; NEURAL NETWORKS; THIN WALLED STRUCTURES; VECTOR QUANTIZATION;

OPTIMIZATION;

EID: 84874324704     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-012-4190-4     Document Type: Article

References (23)

1. Fu PH, Luo AA, Jiang HY, Peng LM (2008) Low-pressure die casting of magnesium alloy AM50: response to process parameters. J Mater Process Technol 205:224-234
2. Cleary PW (2010) Extension of SPH to predict feeding, freezing and defect creation in low pressure die casting. Appl Math Model 34:3189-3201
3. Zhang LQ, Li LX, Zhu BW (2009) Simulation study on the LPDC process for thin-walled aluminum alloy casting. Mater Manuf Process 24:1349-1353
4. Zhang B, Maijer DM, Cockcroft SL (2007) Development of a 3-D thermal model of the low-pressure die-cast (LPDC) process of A356 aluminum alloy wheels. Mat Sci Eng A 464:295-305
5. Wu SP, Li CY, Guo JJ (2006) Numerical simulation and experimental investigation of two filling methods in vertical centrifugal casting. T Nonferr Metal Soc 16:1035-1040
6. Vijayaram TR, Sulaiman S, Hamouda AMS (2006) Numerical simulation of casting solidification in permanent metallic molds. J Mater Process Technol 178:29-33
7. Dobrzanski LA, Krupinski M, Sokolowski JH (2005) Computer aided classification of flaws occurred during casting of aluminum. J Mater Process Technol 167:456-462
8. Shi HZ, Gao YH, Wang XC (2010) Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48:955-962
9. 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 27:509-517
10. 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
11. Zhang LQ, Li LX, Ju H, Zhu BW (2010) Inverse identification of interfacial heat transfer coefficient between the casting and metal mold using neural network. Energ Convers Manage 51:1898-1904
12. Chen CR, Ramaswamy HS (2002) Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms. J Food Eng 53:209-220
13. Yarlagadda PKDV (2002) Development of an integrated neural network system for prediction of process parameters in metal injection molding. J Mater Process Technol 130-13:315-320
14. Shen CY, Wang LX, Li Q (2007) Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J Mater Process Technol 183:412-418
15. Verran GO, Mendes RPK, Valentina-Dalla LVO (2008) DOE applied to optimization of aluminum alloy die castings. J Mater Process Technol 200:120-125
16. Zhang Y, Zhao SD, Zhang ZY (2008) Optimization for the forming process parameters of thin-walled valve shell. Thin Wall Struct 46:371-379
17. Kuo JH, Hsu FL, Hwang WS (2001) Development of an interactive simulation system for the determination of the pressure-time relationship during the filling in a low pressure die casting process. Sci Technol Adv Mat 2:131-145
18. Park YW, Rhee S (2008) Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation. Int J Adv Manuf Technol 61:1401-1411
19. Han X, Xu D, Liu GR (2003) A computational inverse technique for material characterization of a functionally graded cylinder using a progressive neural network. Neurocomputing 51:341-360
20. Paszkowicz W (2009) Genetic algorithms, a nature-inspired tool: survey of applications in materials science and related fields. Mater Manuf Process 24:174-197
21. Surekha B, Kaushik LK, Panduy AK, Vundavilli PR, Parappagoudar MB (2012) Multi-objective optimization of green sand mould system using evolutionary algorithms. Int J Adv Manuf Technol 58:9-17
22. Coello CAC, Becerra RL (2009) Evolutionary multiobjective optimization in materials science and engineering. Mater Manuf Process 24:119-129
23. Wen JL, Yang YK, Jeng MC (2009) Optimization of die casting conditions for wear properties of alloy AZ91D components using the Taguchi method and design of experiments analysis. Int J Adv Manuf Technol 41:430-439