Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts Using Forward and Reverse Mapping of Artificial Neural Networks

Patel G.C. Manjunath; Prasad Krishna

doi:10.4028/www.scientific.net/AMR.463-464.674

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 463-464Prediction and Optimization of Dimensional...

Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts Using Forward and Reverse Mapping of Artificial Neural Networks

Abstract:

The most significant process parameters affecting dimensional shrinkage in transverse and longitudinal directions of molded parts in Plastic Injection Molding (PIM) process are injection velocity, mold temperature, melt temperature and packing pressure. In the present work, ANN model was developed for forward and reverse mapping prediction. In forward mapping PIM process parameters are expressed as the input parameters to predict dimensional shrinkage, whereas in reverse mapping, attempts were made to predict an appropriate set of process parameters required for arriving at the required dimensional shrinkage. The trained network with one thousand input-output data randomly generated from regression equations reported by earlier researchers resulted in minimum mean squared error. The performance of developed model was compared with experimental values for ten different test cases. The results show that ANN model with both forward and reverse mapping is capable of prediction with an error level of less than ten percent.

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Periodical:

Advanced Materials Research (Volumes 463-464)

Pages:

674-678

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.463-464.674

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Cite this paper

Online since:

February 2012

Authors:

Patel G.C. Manjunath, Prasad Krishna

Keywords:

Artificial Neural Network (ANN), BP Neural Network (BPNN), Forward Mapping, Optimization, Plastic Injection Molding (PIM), Reverse Mapping

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References

[1] Chih-Cherng Chen, Pao-Lin Su & Yan-Cherng Lin, Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology, International Journal of Advanced Manufacturing Technology, 2009, Springer-Verlag London Limited. P. 1087-1095.

DOI: 10.1007/s00170-009-2045-4

Google Scholar

[2] Prasad K.D.V. Yaralagadda and Cobby Ang Teck Khong, Development of a hybrid neural network system for prediction of process parameters in injection moulding, Journal of Materials Processing Technology, 2001, pp.110-116.

DOI: 10.1016/s0924-0136(01)00901-3

Google Scholar

[3] Wen-Chin Chen, Gong-Loung Fu, Pei-Hao Tai and Wei-Jaw Deng, Process parameter optimization for MIMO plastic injection molding via soft computing, Expert Systems with Applications (2007).

DOI: 10.1016/j.eswa.2007.10.020

Google Scholar

[4] D. Mathivanan and N.S. Parthasarathy, Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm, International Journal of Advanced Manufacturing Technology, 2009, Springer-Verlag London Ltd. P. 1087-1095.

DOI: 10.1007/s00170-009-2021-z

Google Scholar

[5] B.H.M. Sadeghi, A BP-neural network predictor model for plastic injection molding process, Journal of Materials Processing Technology, 2000, pp.411-416.

DOI: 10.1016/s0924-0136(00)00498-2

Google Scholar

[6] Fie Yin, Huajie Mao, Lin Hua, Wei Guo and Maosheng Shu, Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding, Materials and Design, 2011, pp.1844-1850.

DOI: 10.1016/j.matdes.2010.12.022

Google Scholar

[7] Mahesh B. Parappagoudar, D.K. Pratihar and G.L. Datta, Forward and Reverse mappings in green sand mold system using neural networks, 2008, Applied soft computing, pp.239-260.

DOI: 10.1016/j.asoc.2007.01.005

Google Scholar

[8] Qi Wang, Haihu Ma, and Xiaodan Wang, Prediction and Assessment of Agricultural Modernization Level Based on Topsis and Artificial Neural Network, Proceedings of the 2010 ICCASM 2010, V2- pp.229-232, China.

DOI: 10.1109/iccasm.2010.5619382

Google Scholar