Inverse identification of interfacial heat transfer coefficient between the casting and metal mold using neural network

Zhang, Liqiang; Li, Luoxing; Jin, Hui; Zhu, Biwu

doi:10.1016/j.enconman.2010.02.020

Cited by 58 publications

(16 citation statements)

References 16 publications

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“…Some authors have used neural networks to obtain a high degree of agreement between numerical M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT and experimental results. However, the procedure makes great demands in terms of time and computational resources [15]. Indeed, determining iHTCs is not always an easy task since, in the solid-liquid region above the solidus curve, the temperature change is not only dependent on the heat transfer coefficients but also on other important variables (such as the thermal conductivity of the sand mould) [16].…”

Section: Introductionmentioning

confidence: 99%

Determination of interfacial heat transfer coefficients in a sand mould casting process using an optimised inverse analysis

Palumbo

Piglionico

Piccininni

et al. 2015

Applied Thermal Engineering

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Section: Introductionmentioning

confidence: 99%

Determination of interfacial heat transfer coefficients in a sand mould casting process using an optimised inverse analysis

Palumbo

Piglionico

Piccininni

et al. 2015

Applied Thermal Engineering

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“…Some research works show that the use of ANN as inverse method can remarkably overcome the computational cost. Zhang et al [26] used ANN as inverse method and the estimated IHTC was validated with a commercial software. The use of evolutionary algorithms is computationally expensive as observed in the afore-mentioned literatures.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous estimation of unknown parameters using a-priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt%Pb alloy—an ANN-driven Bayesian approach

2019

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The present methodology focuses on model reduction in which the prevalent one-dimensional transient heat conduction equation for a horizontal solidification of Sn-5wt%Pb alloy is replaced with Artificial Neural Network (ANN) in order to estimate the unknown constants present in the interfacial heat transfer coefficient correlation. As a novel approach, ANN-driven forward model is synergistically combined with Bayesian framework and Genetic algorithm to simultaneously estimate the unknown parameters and modelling error. Gaussian noise is then added to the temperature distribution obtained using the forward approach to represent real-time experiments. The hallmark of the present work is to reduce the computational time of both the forward and the inverse methods and to simultaneously estimate the unknown parameters using a-priori engineering knowledge. The results of the present methodology prove that the simultaneous estimation of unknown parameters can be effectively obtained only with the use of Bayesian framework.

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“…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. To accurately control the quality of the moulding sands [85] and to predict the presence/absence of the casting defects [86] such as hot crack, misrun, scab blow hole and air lock in the sand mould system, NN is 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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Inverse identification of interfacial heat transfer coefficient between the casting and metal mold using neural network

Cited by 58 publications

References 16 publications

Determination of interfacial heat transfer coefficients in a sand mould casting process using an optimised inverse analysis

Determination of interfacial heat transfer coefficients in a sand mould casting process using an optimised inverse analysis

Simultaneous estimation of unknown parameters using a-priori knowledge for the estimation of interfacial heat transfer coefficient during solidification of Sn–5wt%Pb alloy—an ANN-driven Bayesian approach

Modelling in Squeeze Casting Process-Present State and Future Perspectives

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