Prediction of Fracture Force of Weld Line Based on Artificial Neural Networks

Abstract:

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A feed-forward three-layer neural network was proposed to predict the fracture force of injection-molded parts’ weld line. Firstly, the most significant process parameters which affect the fracture force of weld line were analyzed. Secondly, melt temperature, injection pressure, holding pressure and holding time were chosen as import variables and the fracture force of weld line was chosen as output variable to construct artificial neural networks. Furthermore, the performance of ANN was evaluated and tested by its application to verification tests with process parameters randomly selected which all of them were not used in the network training. Results showed that the ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.86%,and maximum relative error (MRE) in the range of 1.84% for the test data set, and which can comparatively accurately reflect the influence relation of the injection process parameters on part’s quality index under the circumstance of data deficiencies.

Info:

Periodical:

Advanced Materials Research (Volumes 314-316)

Edited by:

Jian Gao

Pages:

547-553

DOI:

10.4028/www.scientific.net/AMR.314-316.547

Citation:

P. F. Zhu et al., "Prediction of Fracture Force of Weld Line Based on Artificial Neural Networks", Advanced Materials Research, Vols. 314-316, pp. 547-553, 2011

Online since:

August 2011

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Price:

$35.00

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