An intelligent system for predicting HPDC process variables in interactive environment

Jitender, K.; Lajimi, Amir M.; Xirouchakis, Paul

doi:10.1016/j.jmatprotec.2007.10.011

Cited by 36 publications

(21 citation statements)

References 13 publications

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“…The calculated area of the inner gate is ~430 mm 2 . the calculated cross-sectional area of the runner is 1,290 mm 2 , and the ratio of it to cross-sectional area of the gate is 1:3 [6][7][8][9] . A branch was drawn on the right side of the runner to collect the cold metal liquid and to improve the quality of the casting.…”

Section: Design For Pouring Systemmentioning

confidence: 96%

“…where, A g is the cross-sectional area of the inner gate (mm 2 ); V is the sum of the volume of die casting and overflow groove, which is 650,000 mm 3 ; v is the filling velocity of the molten metal at the gate, which is 25,000 mm·s -1 ; t is the filling time which is 0.06 s [6][7][8][9] . The calculated area of the inner gate is ~430 mm 2 .…”

Section: Design For Pouring Systemmentioning

confidence: 99%

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Finite element analysis for die casting parameters in high-pressure die casting process

Qin

Chen

et al. 2019

China Foundry

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The gating system and the overflow system were designed according to the casting structure during high pressure die casting (HPDC) process. The simulation was carried out by ProCAST software to visualize the injection chamber pre-crystallization and the flow of molten metal. The main work is to research four die casting process parameters, i.e. injection temperature, low-pressure velocity, high-and low-pressure velocity's switching position, and high-pressure velocity. Experimental results show that the higher injection temperature and lowpressure velocity can mitigate the pre-crystallization of the injection chamber. However, when the low-pressure velocity exceeds 0.2 m·s -1 , the air entrapment in the chamber occurs. Besides, when the high-pressure velocity is greater than 2.5 m·s -1 , the overflow channel at the final filling position is covered by the liquid metal too early. Finally, the injection temperature of 650 °C, the low-pressure velocity of 0.2 m·s -1 , the high-and low-pressure velocity's switching position of 320 mm and the high-pressure velocity of 2 m·s -1 are obtained as the optimal parameters by the software simulation, which has been verified by actual production.

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Section: Design For Pouring Systemmentioning

confidence: 96%

Section: Design For Pouring Systemmentioning

confidence: 99%

Finite element analysis for die casting parameters in high-pressure die casting process

Qin

Chen

et al. 2019

China Foundry

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“…In recent years neural networks has been applied for the squeeze casting process to forecast the solidification time, temperature difference and secondary dendrite arm spacing [74] of the squeeze cast components. To avoid the rule of thumb, expert advice, try-error method used in shop floor practice, neural networks has been successfully implemented to predict filling time, solidification time and casting defects ,surface defects [75,76], solidification time [77,78], filling time and porosity , injection time [79,80], of pressure die casting process. To predict interfacial heat transfer coefficients at metal-mould interface [81], compressive strength, secondary dendrite arm spacing [82], mechanical properties [83], permeability [84] of different casting processes the soft computing based neural networks were used.…”

Section: Modelling Using Soft Computing Approachmentioning

confidence: 99%

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Patel¹,

MB²

2015

Adv Automob Eng

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The growing demand in today's competitive manufacturing environment has encouraged the researchers to develop and apply modelling tools. The development and application of modelling tools help the casting industries to considerably increase productivity and casting quality. Till date there is no universal standard available to model and optimize any of the manufacturing processes. However the present work discusses the advantages and limitations of some conventional and non-conventional modelling tools applied for various casting processes. In addition the research effort made by various authors till date in modelling and optimization of the squeeze casting process has been reported. Furthermore the necessary steps for prediction and optimization are high lightened by identifying the trends in the literature. Ultimately this research paper explores the scope for future research in online control of the process by automatically adjusting the squeeze cast process parameters through reverse prediction by utilizing the soft computing tools namely, Neural Network, Genetic Algorithms, Fuzzy-logic Controllers and their different combinations. The present work also proposed a detailed methodology, starting from the selection of process variables till the best process variable combinations for extreme values of the outputs responsible for better product quality using experimental, prediction and optimization methodology. Citation: Manjunath Patel GC, Krishna P, Parappagoudar MB (2015) Modelling in Squeeze Casting Process-Present State and Future Perspectives. Modelling in Squeeze Casting

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“…The BPANN is superior to other methods, such as a statistical method, in simulating and analyzing nonlinear complex systems, especially in solving inexact and fuzzy information process problems. [10,11] This paper proposes a BPANN model, on the basis of a series of CAE modeling experiments, which can relate the process parameters to warpage. Utilizing the obtained BPANN prediction model, it is possible to evaluate the influence of process parameters on warpage, to know the process parameters that contribute to warpage, and to determine the optimum settings for the process parameters to reduce the warpage.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

et al. 2015

Journal of Macromolecular Science, Part B

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A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T me ), mold temperature (T mo ), packing time (t p ), cooling time (t c ), and fill pressure (p f ), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T me . The rest of the process parameters, T mo , t p , t c , and p f , were found to be relatively less influential. Warpage increased with elevating T mo . In contrast, an increase in Pp and T me caused the warpage to decrease.

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An intelligent system for predicting HPDC process variables in interactive environment

Cited by 36 publications

References 13 publications

Finite element analysis for die casting parameters in high-pressure die casting process

Finite element analysis for die casting parameters in high-pressure die casting process

Modelling in Squeeze Casting Process-Present State and Future Perspectives

Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

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