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Rumelhart, D.E., Hinton, G.E. and Williarns, R.J. (1986) Learning Internal Representations by Error Propagation. The MIT Press, Cambridge, MA.

has been cited by the following article:

• TITLE: Parameter Prediction of Stretch-Blow Molding Process of PET Using Neural Networks

  AUTHORS: Talal H. Alzanki, Mutaz M. Jafar

  KEYWORDS: Stretch-Blow Molding (SBM) Process, Polyethylene Terephthalate (PET) Bottles, Temperature Distribution Model, Uniform Wall Thickness Distribution

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.12 No.7, July 31, 2019

  ABSTRACT: This study presents a parameter selection strategy developed for the Stretch-Blow Molding (SBM) process to minimize the weight of preforms used. The method is based on a predictive model developed using Neural Networks. The temperature distribution model of the preform was predicted using a 3-layer NN model with supervised backpropagation learning. In addition, the model was used to predict the uniform air pressure applied inside the preform, taking into account the relationship between the internal air pressure and the volume of the preform. Parameters were validated using in situ tests and measurements performed on several weights and lengths of a 0.330 Liter Polyethylene Terephthalate (PET) bottles. Tests showed that the model adequately predicts both the blowing kinematics, mainly zone temperatures and blowing and stretching pressures along the walls of the bottle while maintaining the bottle strength and top load requirements. In the second step, the model was combined to automatically compute the lowest preform weight that can be used for a particular 330 ml bottle design providing a uniform wall thickness distribution.