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CSME 2020/08
Volume 41 No.4
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409-416
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Coupled ANN-Based Prediction of a Foam Expansion Ratio Using an FEM Computational Scheme to Predict the Material EVA Expansion Shape
Yi-Ren Jenga, De-Shin Liua and Hong-Tzong Yaua
aDepartment of Mechanical Engineering, National Chung Cheng University, 160, San-Hsing, Ming-Hsiung, Chia-Yi, 621, Taiwan, R.O.C.
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Abstract:
Accurately predicting the shrink mold shape of direct injection-expanded foam molding is a difficult and important task. This molding method is widely used by sports shoe sole manufacturers to create shock-resistant material. The relationship between the normal thickness of a shoe sole and the expansion ratio of ethylene vinyl acetate (EVA) foam material is crucial to obtaining the correct shrink mold size. This study conducts experiments involving a series of small rectangular specimens with varying z thickness, modified by Artificial Neural Network method (ANN), to qualify the micro expansion ratio along the x, y, and z directions through the normal thickness. Using this relationship as the heat expansion criterion, the expansion of foam could be simulated by the finite element method. Finally, two shoe sole types were used to verify the algorithm. The discrepancy between simulation results with the original designed CAD model in the y-direction was less than 4 mm, meeting the requirements of the shoe sole factory. Thus, the proposed method can be used to predict the shrink mold size rapidly and improve the traditional trial-and-error method.
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Keywords: Finite element, thickness, expansion ratio, Neural Network.
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©
2020
CSME , ISSN 0257-9731
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