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Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network

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Abstract

This study analyzes the contour distortions of polypropylene (PP) composite components applied to the interior of automobiles. Combining a trained radial basis network (RBN) [1] and a sequential quadratic programming (SQP) method [2], an optimal parameter setting of the injection molding process can be determined. The specimens are prepared under different injection molding conditions by varying melting temperatures, injection speeds and injection pressures of three computer-controlled progressive strokes. Minimizing the contour distortions is the objective of this study. Sixteen experimental runs based on a Taguchi orthogonal array table are utilized to train the RBN and the SQP method is applied to search for an optimal solution. In this study, the proposed algorithm yielded a better performance than the design of experiments (DOE) approach. In addition, the analysis of variance (ANOVA) is conducted to identify the significant factors for the contour distortions of the specimens.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ming Hsin University of Science and Technology, No. 1, Hsin Hsing Road, Hsin Feng, 30401, Hsinchu, Taiwan, Republic of China

    Jie-Ren Shie

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  1. Jie-Ren Shie
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Correspondence to Jie-Ren Shie.

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Shie, JR. Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. Int J Adv Manuf Technol 36, 1091–1103 (2008). https://doi.org/10.1007/s00170-007-0940-0

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  • DOI: https://doi.org/10.1007/s00170-007-0940-0

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