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Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes

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Abstract

This paper reports an intelligent approach for modeling and optimisation of filling system design (FSD) in the case of sand casting process of aluminium alloy. In order to achieve this purpose, physics-based process modeling using finite element method (FEM) has been integrated with artificial neural networks (ANN) and genetic algorithm (GA) soft computing techniques. A three-dimensional FE model of the studied process has been developed and validated, using experimental literature data, to predict two melt flow behaviour (MFB) indexes named ingate velocity and jet high. Two feed-forward back-propagation ANN-based process models were developed and optimised to establish the relationship between the FSD input parameters and each studied MFB index. Both ANN models were trained, tested and tuned by using database generated from FE computations. It was found that both ANN models could independently predict, with a high accuracy, the values of the ingate velocity and the jet high for training and test data. The developed ANN models were coupled with an evolutionary GA to select the optimal FSD for each one. The validity of the found solutions was tested by comparing ANN-GA prediction with FE computation for both studied MFB indexes. It was found that error between predicted and simulated values does not exceed 5.61% and 6.31% respectively for the ingate velocity and the jet high, which proves that the proposed approach is reliable and robust for FSD optimisation.

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Abbreviations

ANN:

Artificial neural networks

CAD:

Computer-aided design

FEM:

Finite element method

FFBP:

Feed-forward back-propagation

FSD:

Filling system design

GA:

Genetic algorithm

logsig:

Log-sigmoid transfer function

MFB:

Melt flow behaviour

MSE:

Mean square error

PSO:

Particle swarm optimisation

purelin:

Linear transfer function

trainlm:

Levenberg-Marquardt training algorithm

traingdx:

Gradient descent with momentum and adaptive learning rate training algorithm

tansig:

Tan-sigmoid transfer function

SQP:

Sequential quadratic programming

VOF:

Volume of fluid

3DSP:

Three-dimensional sand printing

LPC:

Low-pressure casting

EPSC-VL:

Expendable pattern shell casting process with vacuum and low pressure

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Authors and Affiliations

  1. MSMP-EA7350, Arts et Métiers ParisTech, 2 cours des Arts et Métiers, 13617, Aix-en-Provence, France

    Ahmed Ktari & Mohamed El Mansori

  2. Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77840, USA

    Mohamed El Mansori

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  1. Ahmed Ktari
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  2. Mohamed El Mansori
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AK planned and carried out the simulations. AK and ME contributed to the analysis of the results and to the writing of the manuscript.

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Correspondence to Ahmed Ktari.

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Ktari, A., El Mansori, M. Intelligent approach based on FEM simulations and soft computing techniques for filling system design optimisation in sand casting processes. Int J Adv Manuf Technol 114, 981–995 (2021). https://doi.org/10.1007/s00170-021-06876-z

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