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Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network

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Abstract

Warpage and volume shrinkage are important indicators of the quality of thin-walled parts during injection molding. In this study, the optimization goals are warpage and volume shrinkage. Design parameters include mold temperature, melt temperature, injection time, holding time, cooling time, and holding pressure. Based on the orthogonal experimental design and response surface experimental design, the MoldFlow software is applied to the simulation of the thin-walled part injection molding process. The importance of various parameters on warpage and volume shrinkage was analyzed by using analysis of variance. Based on the simulation results, a two-layer hidden-layer back propagation (BP) neural network model is established and the genetic algorithm (GA) is used to optimize the weights and thresholds of the back propagation neural network (BPNN) model to reduce warpage and volume shrinkage by optimizing the design parameters significantly. A support vector machine (SVM) combined with GA-BP was used to build a prediction model for predicting warpage and volume shrinkage. Taking the automobile wire harness protection frame as an example, and verified by numerical simulation, the GA-optimized two-layer hidden-layer BP neural network combination method is an effective method for injection molding to reduce warpage and volume shrinkage of thin-walled parts. SVM-BP-GA can accurately provide predictions for optimization goals; the amount of warpage and the volume shrinkage were 0.93% and 1.9%, respectively.

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Abbreviations

BPNN:

Back propagation neural network

GA:

Genetic algorithm

BP:

Back propagation

SOM:

Self-organizing competitive neural network

SOM-BPNN:

Self-organizing competitive-back propagation neural network

PP:

Polypropylene

PS:

Polystyrene

VCM:

Variable complexity method

PIM:

Plastic injection molding

SVM:

Support vector machine

SVM-GA-BP:

Support vector machine-genetic algorithm-back propagation

IEGO:

Efficient global optimization

RBF:

Radial basis function

SAO:

Sequential approximation optimization

SAO-RBF:

Sequential approximation optimization-radial basis function

ANOVA:

Analysis of variance

CNS-GA:

Variable complexity method-genetic algorithm

NSGA-II:

Non-dominant use of genetic algorithm

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Authors and Affiliations

  1. School of Materials Engineering, Shanghai University of Engineering and Technology, Room 1212, 1606 Administration Building, 333 Longteng Road, Songjiang District, Shanghai, China

    Zhiyuan Song, Shumei Liu & Zuoxiu Hu

  2. Aerospace Aerospace Manufacturing Engineering Department of electromechanical Institute, Nanjing University of Aeronautics and Astronautics, No. 29, Qinhuai District Yudao street, Nanjing, China

    Xingxing Wang

Authors
  1. Zhiyuan Song
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  2. Shumei Liu
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  3. Xingxing Wang
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  4. Zuoxiu Hu
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Correspondence to Shumei Liu.

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Song, Z., Liu, S., Wang, X. et al. Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network. Int J Adv Manuf Technol 109, 755–769 (2020). https://doi.org/10.1007/s00170-020-05558-6

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  • DOI: https://doi.org/10.1007/s00170-020-05558-6

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