Parameter Prediction of Stretch-Blow Molding Process of PET Using Neural Networks
Open Access
- 1 January 2019
- journal article
- Published by Scientific Research Publishing, Inc. in Journal of Software Engineering and Applications
- Vol. 12 (07), 278-292
- https://doi.org/10.4236/jsea.2019.127017
Abstract
No abstract availableThis publication has 16 references indexed in Scilit:
- Parameter study of stretch—blow moulding process of polyethylene terephthalate bottles using finite element simulationProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2010
- Optimization of preform temperature distribution for the stretch‐blow molding of PET bottles: Infrared heating and blowing modelingPolymer Engineering & Science, 2008
- Preform shape and operating condition optimization for the stretch blow molding processPolymer Engineering & Science, 2007
- Modeling and simulation of stretch blow molding of polyethylene terephthalatePolymer Engineering & Science, 2004
- Intelligent Control Using Artificial Neural Networks and Fuzzy Logic: Recent Trends and Industrial ApplicationsPublished by Springer Science and Business Media LLC ,1997
- Training feedforward networks with the Marquardt algorithmIEEE Transactions on Neural Networks, 1994
- On the Convergence of the LMS Algorithm with Adaptive Learning Rate for Linear Feedforward NetworksNeural Computation, 1991
- 30 years of adaptive neural networks: perceptron, Madaline, and backpropagationProceedings of the IEEE, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- Biaxially oriented poly(ethylene terephthalate) bottles: Effects of resin molecular weight on parison stretching behaviorPolymer Engineering & Science, 1981