Modelling of yield length in the mould of commercial plastics using artificial neural networks

Karatas C., SÖZEN A., ARCAKLIOĞLU E., Erguney S.

MATERIALS & DESIGN, vol.28, no.1, pp.278-286, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 1
  • Publication Date: 2007
  • Doi Number: 10.1016/j.matdes.2005.06.016
  • Journal Name: MATERIALS & DESIGN
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.278-286
  • Keywords: yield length, plastic mould, artificial neural network, EJECTOR-ABSORPTION CYCLE, ENERGY-SYSTEMS
  • Gazi University Affiliated: Yes


According to the yield length of a plastic, whether a mould is filled fully or not can be estimated. In this study, a new formula based on various injection parameters was developed to determine the yield length in plastic moulding of the commercial plastics, the most widely used ones are low-density polyethylene, high-density polyethylene, polystyrene and polypropylene, by artificial neural network (ANN). This study covers two main objectives: (i) the yield properties of plastics have been investigated at various injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould including spiral chutes as experimental; (ii) the yield length of the commercial plastics based on various measured injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould was described by ANN using experimental data. Some experimental data were used as test data, and these values were not used for training. Scaled conjugate gradient and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. The results show that the maximum mean absolute percentage error (MAPE) was found to be 1.574969% and R-2 values 0.99964. The best approach was found in the LM algorithm with six neurons. The MAPE and R-2 for testing data were 1.849 and 0.9995, respectively, similar to algorithm and neurons. This study is considered to be helpful in predicting the yield length in the mould whose injection parameters are known. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs. (c) 2005 Elsevier Ltd. All rights reserved.