Design & Manufacturing

Korean Society of Die & Mold Engineering (한국금형공학회)
권호 표지

A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network

인공신경망을 활용한 사출성형품의 질량과 치수 예측에 관한 연구

  • Yang, Dong-Cheol (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology) ;
  • Lee, Jun-Han (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology) ;
  • Kim, Jong-Sun (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology)
  • 양동철 (한국생산기술연구원 형상제조연구부문) ;
  • 이준한 (한국생산기술연구원 형상제조연구부문) ;
  • 김종선 (한국생산기술연구원 형상제조연구부문)
  • Received : 2020.07.28
  • Accepted : 2020.09.30
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 기계산업핵심기술개발사업(Project No. KM200224, 10067766)의 지원으로 진행되었습니다.

References

  1. Shi, H., Gao, Y., Wang, X., "Optimization of Injection Molding Process Parameters Using Integrated Artificial Neural Network Model and Expected Improvement Function Method", Int. J. Adv. Manuf. Technol., Vol. 48, pp. 955-962, 2010. https://doi.org/10.1007/s00170-009-2346-7
  2. Joseph B, D., "Injection Molds and Molding: A Practical Manual", Springer Science & Business Media, Germany, 1987.
  3. Jong, W. R., Huang, Y. M., Lin, Y. Z., Chen, S. C., Chen, Y. W., "Integrating Taguchi Method and Artificial Neural Network to Explore Machine Learning of Computer Aided Engineering", J. Chin. Inst. Eng., Vol. 43, pp. 1-11, 2020. https://doi.org/10.1080/02533839.2019.1676655
  4. Shen, C., Wang, L., Li, Q., "Optimization of Injection Molding Process Parameters Using Combination of Artificial Neural Network and Genetic Algorithm Method", J. Mater. Process Technol., Vol .183, pp. 412-418, 2007. https://doi.org/10.1016/j.jmatprotec.2006.10.036
  5. Liao, S. J., Hsieh. W. H., Wang, J. T., Su, Y. C., "Shrinkage and Warpage Prediction of Injection Molded Thinwall Parts Using Artificial Neural Networks", Polym. Eng. Sci., Vol. 44, pp. 2029-2040, 2004. https://doi.org/10.1002/pen.20206
  6. Zhao, J., Cheng, G., Ruan, S., Li, Z., "Multi-objective Optimization Design of Injection Molding Process Parameters Based on the Improved Efficient Global Optimization Algorithm and Non-dominated Sorting-based Genetic Algorithm", Int. J. Adv. Manuf. Technol., Vol. 78, pp. 1813-1826, 2015. https://doi.org/10.1007/s00170-014-6770-y
  7. Solvason, C. C., Chemmangattuvalappil, N. G., Eljack, F. T., Eden, M. R., "Effient Visual Mixture Design of Experiments Using Property Clustering Techniques", Ind. Eng. Chem. Res., Vol. 48, pp. 2245-2256, 2009. https://doi.org/10.1021/ie800877d
  8. Leaman, R., Dogan, R. I., DNorm, Z. L., "Disease Name Normalization with Pairwise Learning to Rank", Bioinf., Vol. 29, pp. 2909-2917, 2013. https://doi.org/10.1093/bioinformatics/btt474
  9. Adnan, R., Ruslan, F. A., Samad, A. M., Zain, Z. M., "New Artificial Neural Network and Extened Kalman Filter Hybrid Model of Flood Prediction System", 2013 IEEE 9th Int. Colloq. Signal Process. Appl., pp. 252-257, 2013.
  10. Svozil, D., Kvasnieka, V., Pospichal J., "Introduction to Multi-layer Feed-Forward Neural Networks", Vol. 39, pp. 43-62, 1997. https://doi.org/10.1016/S0169-7439(97)00061-0
  11. Thornton, C., Hutter, F., Hoos, H. H., Brown, K. L., "Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms", Proc. Int. Conf. Knowl. Discovery Data Min., pp. 847-855, 2013.