The Experts below are selected from a list of 531 Experts worldwide ranked by ideXlab platform
Wendy Rodriguezcastellanos - One of the best experts on this subject based on the ideXlab platform.
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Extrusion Blow Molding of a starch gelatin polymer matrix reinforced with cellulose
European Polymer Journal, 2015Co-Authors: Wendy Rodriguezcastellanos, Denis Rodrigue, Fernando Martinezbustos, Magdalena TrujillobarraganAbstract:Abstract This work investigated the possibility of using hydrolyzed corn starch–gelatin as a base matrix and cellulose as reinforcement, to produce containers by Extrusion Blow Molding. First, the compounds were characterized by dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) to determine their viscoelastic behavior and thermal stability. The results showed that the most suitable processing temperature should be less than 120 °C to avoid degradation. Furthermore, the addition of cellulose decreased the viscosity of the starch–gelatin polymer matrix allowing the compounds to be processed at temperatures as low as 100 °C. Then, parisons were obtained by Extrusion Blow Molding and presented suitable processing characteristics. Overall, the best containers were found to have 44% higher energy at break and better dimensional stability when cellulose was added.
Magdalena Trujillobarragan - One of the best experts on this subject based on the ideXlab platform.
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Extrusion Blow Molding of a starch gelatin polymer matrix reinforced with cellulose
European Polymer Journal, 2015Co-Authors: Wendy Rodriguezcastellanos, Denis Rodrigue, Fernando Martinezbustos, Magdalena TrujillobarraganAbstract:Abstract This work investigated the possibility of using hydrolyzed corn starch–gelatin as a base matrix and cellulose as reinforcement, to produce containers by Extrusion Blow Molding. First, the compounds were characterized by dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) to determine their viscoelastic behavior and thermal stability. The results showed that the most suitable processing temperature should be less than 120 °C to avoid degradation. Furthermore, the addition of cellulose decreased the viscosity of the starch–gelatin polymer matrix allowing the compounds to be processed at temperatures as low as 100 °C. Then, parisons were obtained by Extrusion Blow Molding and presented suitable processing characteristics. Overall, the best containers were found to have 44% higher energy at break and better dimensional stability when cellulose was added.
Huang Han-xiong - One of the best experts on this subject based on the ideXlab platform.
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Application of Modern Design Methods in Parison Formation Stage of Extrusion Blow Molding
China Plastics Industry, 2020Co-Authors: Huang Han-xiongAbstract:Parison formation is a critical stage in Extrusion Blow Molding. The present situation of the applications of three methods (Finite Element methods?CAD and Artificial Neural Network) in the parison formation stage of Extrusion Blow Molding is systematically reviewed.
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Prediction of Parison Dimension Based on RBF Neural Network in Plastics Extrusion Blow Molding
China Plastics Industry, 2020Co-Authors: Huang Han-xiongAbstract:Based on the prediction of the parison dimension in plastics Extrusion Blow Molding by BR neural network,RBF neural network was applied to predict the parison dimension in plastics Extrusion Blow Molding,and the results of RBF was compared with that by BP.The results showed both RBF and BP could predict the parison dimensions well,but the training time of RBF was much shoter,being only 0.7% of that of BP.
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Support Vector Machine for Parison Swell Prediction in Extrusion Blow Molding
The Plastics, 2020Co-Authors: Huang Han-xiongAbstract:Support vector machine was used to predict the distribution of the parison swell in processing of Extrusion Blow Molding,and the comparison of the results with the predicted ones using the artificial neural network showed the stronger generalization capability of support vector machine.
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Constitutive Equations for Simulation of Parison Formationduring Extrusion Blow Molding
China Plastics, 2020Co-Authors: Huang Han-xiongAbstract:Parison formation is a critical stage in plastics Extrusion Blow Molding. In the numerical simulation of parison formation, there are two main methods: one is to treat the polymer melt as a Newtonian fluid, the other is as a viscoelastic fluid. As for the viscoelastic fluid, differential and integral constitutive equations are used. These constitutive equations are reviewed systematically.
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Constitutive Equations in Studying on Parison Formation Stage of Extrusion Blow Molding
Plastics Science and Technology, 2020Co-Authors: Huang Han-xiongAbstract:Parison formation is a critical stage in extr usion Blow Molding. In the numerical simulation of parison formation , there are two main methods: one is to treat the polymer melt as Newtonian fluid, the ot her is to treat the melt as viscoelastic fluid.Differential and integral consti tutive equations were used to analyse viscoelastic fluid.
Han-xiong Huang - One of the best experts on this subject based on the ideXlab platform.
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Part cooling in Blow Molding: Finite element simulation
2020Co-Authors: Han-xiong Huang, Yu-zhou Li, You-fa HuangAbstract:The part cooling stage of the Extrusion Blow Molding process was simulated based on the ANSYS finite element software. The transient temperature profiles across the part thickness were predicted. The experiment results validated that the predictions are reasonable. Then the temperature dependent quiescent crystallinity development across the thickness was calculated using a Nakamura equation. The influence of the processing parameters, part thickness, and the thermal properties of plastics and mold materials can be analyzed.
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new strategies for predicting parison dimensions in Extrusion Blow Molding
Polymer-plastics Technology and Engineering, 2011Co-Authors: Han-xiong Huang, Jiongcheng Li, Dong Li, Gengqun HuangAbstract:In this work, two new strategies were proposed for predicting the parison thickness and diameter distributions in Extrusion Blow Molding. The first one was a finite-element-based numerical simulation for the parison extruded from a varying die gap. The comparison of simulated and experimental parison thickness distributions indicates that the new method has certain accuracy in predicting the parison thickness from a varying die gap. The second one was an artificial neural network (ANN) approach, the characteristics of which are in sufficient patterns that can be obtained without doing too many experiments. The diameter and thickness swells of the parisons extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the ANN model. The dimension of one location on the parison can provide one pattern to train the ANN model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given ran...
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optimizing parison thickness for Extrusion Blow Molding by hybrid method
Journal of Materials Processing Technology, 2007Co-Authors: Gengqun Huang, Han-xiong HuangAbstract:Abstract A hybrid method consisting of finite element method (FEM), artificial neural network (ANN), and genetic algorithm (GA) was used to find the optimal parison thickness distribution for a Blow molded part with required thickness distribution. Firstly, numerical simulations on the parison inflation were performed using FEM and the K-BKZ integral type constitutive equation. Based on the simulation results, a back propagation (BP) ANN model was then developed to build the relationship between parison thickness distribution and the objective function, which was used to evaluate the wall thickness distribution of part. The predictive ability of the ANN model was verified through FEM simulation results different from those utilized in the training stage. Finally, a GA was developed and used to search for the optimal parison thickness distribution. The results showed that the hybrid method proposed in this work can effectively obtain the optimal parison thickness distribution for a Blow molded part with required wall thickness distribution. Compared with the trial and error method, the hybrid method can shorten the part development time and save a lot of material.
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Parison Dimension Prediction in Extrusion Blow Molding Using Neural Network Approach: A New Strategy
Volume 8: Heat Transfer Fluid Flows and Thermal Systems Parts A and B, 2007Co-Authors: Han-xiong Huang, Dong LiAbstract:As the plastics Extrusion Blow molded parts are getting more and more complex, it is necessary to optimize the parison dimension distribution. Predicting the parison dimension distribution is useful to optimize the thickness distribution and property of the final part. The dependency between parison dimensions and materials characteristics, processing conditions, and die geometry is a highly nonlinear and fully coupled one. In this work, diameter and thickness swells of the high-density polyethylene parison extruded under different flow rates were obtained by a well-designed experiment. The obtained data were then used to train and test the artificial neural network (ANN) model. Trained and tested ANN model can be used to predict the dimensions at any location on the parison within a given range.Copyright © 2007 by ASME
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Optimization of Parison Thickness in Extrusion Blow Molding Using Fuzzy Iterative Learning Control Algorithm
Applied Mechanics, 2006Co-Authors: Gengqun Huang, Han-xiong HuangAbstract:Blow molded parts require a strict control of the part thickness distributions so as to achieve the required mechanical performance and to minimize the usage of material. In this paper, an optimization algorithm combining iterative learning control (ILC) with fuzzy logic was used to get the optimal parison thickness distributions for a part with uniform wall thickness distribution. Instead of using the pure numerical method, engineer knowledge and experience were combined in the optimization algorithm using fuzzy rules. It was shown that the fuzzy ILC algorithm is effective to obtain the optimal parison thickness distributions.Copyright © 2006 by ASME
Denis Rodrigue - One of the best experts on this subject based on the ideXlab platform.
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Extrusion Blow Molding of a starch gelatin polymer matrix reinforced with cellulose
European Polymer Journal, 2015Co-Authors: Wendy Rodriguezcastellanos, Denis Rodrigue, Fernando Martinezbustos, Magdalena TrujillobarraganAbstract:Abstract This work investigated the possibility of using hydrolyzed corn starch–gelatin as a base matrix and cellulose as reinforcement, to produce containers by Extrusion Blow Molding. First, the compounds were characterized by dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) to determine their viscoelastic behavior and thermal stability. The results showed that the most suitable processing temperature should be less than 120 °C to avoid degradation. Furthermore, the addition of cellulose decreased the viscosity of the starch–gelatin polymer matrix allowing the compounds to be processed at temperatures as low as 100 °C. Then, parisons were obtained by Extrusion Blow Molding and presented suitable processing characteristics. Overall, the best containers were found to have 44% higher energy at break and better dimensional stability when cellulose was added.
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Extrusion Blow Molding of a starch–gelatin polymer matrix reinforced with cellulose
European Polymer Journal, 2015Co-Authors: Wendy Rodríguez-castellanos, Denis Rodrigue, Fernando Martínez-bustos, Magdalena Trujillo-barragánAbstract:Abstract This work investigated the possibility of using hydrolyzed corn starch–gelatin as a base matrix and cellulose as reinforcement, to produce containers by Extrusion Blow Molding. First, the compounds were characterized by dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) to determine their viscoelastic behavior and thermal stability. The results showed that the most suitable processing temperature should be less than 120 °C to avoid degradation. Furthermore, the addition of cellulose decreased the viscosity of the starch–gelatin polymer matrix allowing the compounds to be processed at temperatures as low as 100 °C. Then, parisons were obtained by Extrusion Blow Molding and presented suitable processing characteristics. Overall, the best containers were found to have 44% higher energy at break and better dimensional stability when cellulose was added.
