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【期刊论文】Theoretical Modeling of Dispersive Melting Mechanism of Polymers in an Extruder
黄汉雄, HAN-XIONG HUANG* and YU-CHENG PENG
,-0001,():
-1年11月30日
Studies on the melting in a single-screw extruder have mainly concentrated on the Maddock melting mechanism. The dispersive melting mechanism, named by us, is of great practical utility. A six-block physical model is first proposed for the dispersive melting mechanism in the present study. Then nonisothermal non-Newtonian mathematical models for the dispersive and Maddock melting mechanisms are developed. It has been demonstrated that the dispersive melting model has a number of advantages over the Maddock melting model: the mechanical power consumption can be decreased; the polymer temperature profile is more uniform and its average temperature is lower. Furthermore, melting can be accelerated and hence the total melting length shortened.
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黄汉雄, HAN-XIONG HUANG
,-0001,():
-1年11月30日
Self-reinforced polypropylene (PP) sheets have been prepared from melt flow-induced crystallization through a conical slit die fed by a conventional extruder. Their structure and properties, influenced by the die pressure ranging from 20 to 50 MPa and die outlet temperature, are studied by scanning electron microscopy observation, differential scanning calorimetry analyses, tensile strength, and light transmittance measurements. At a die outlet temperature of 1627C and a pressure above 30 MPa, conspicuous increases in the melting peak, tensile strength, and light transmittance (they can be used to characterize the self-reinforcement degree of sheet) are observed. The self-reinforcement degree, however, increases only slightly with increasing pressure as it exceeds 40 MPa. Raising the die outlet temperature from 162 to 1727C results in a further increase in the self-reinforcement degree (for example, a highest tensile strength of 288 MPa) while keeping the pressure at 40 MPa, so bulk PP materials with high properties can be produced from continuous melt extrusion under pressures lower than 40 MPa. Furthermore, the melt temperature plays an important role in determining the properties of self-reinforced polymeric materials. Q 1998 John Wiley & Sons, Inc. J Appl Polym Sci 67: 2111–2118, 1998
polypropylene, self-reinforcement, extrusion, structure and properties
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【期刊论文】Prediction of parison swell in plastics extrusion blow molding using a neural network method
黄汉雄, H.-X. Huang*, C.-M. Liao
Polymer Testing 21(2002)745-749,-0001,():
-1年11月30日
A neural network-based model approach is presented in which the effects of the die temperature and flow rate on the diameter and thickness swells of the parison in the continuous extrusion blow molding of high-density polyethylene (HDPE) are investigated. Comparison of the neural network model predictions with experimental data yields very good agreement and demonstrates that the neural network model can predict the parison swells at different processing parameters with a high degree of precision (within 0.001). 2002 Elsevier Science Ltd. All rights reserved.
Plastics, Extrusion blow molding, Parison swell, Neural network method
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黄汉雄, 杨晓松
,-0001,():
-1年11月30日
基于薄膜近似和neo-Hookean本构关系,建立了描述塑料挤出吹塑中型坯自由吹胀的数学模型。在实验方面,采用视频图像捕获技术获得了吹塑模具型腔内型坯吹胀的瞬态图像。比较发现,理论预示的型坯轮廓分布与实验观察结果较吻合。型坯中部的胀大速率要比两端的大得多,且在很低的吹胀压力(本研究约为20kPa)下即与模具型腔接触。本文还预示了型坯中截面半径对吹胀压力、材料模量和型坯起始壁厚的依赖性。型坯的胀大速率随材料模量或型坯起始壁厚的减小而提高。本文建立的数学模型还可用于预示型坯自由吹胀过程中局部的拉伸比、轴向与周向的局部应力分布以及壁厚分布。
塑料, 吹塑, 型坯, 吹胀
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【期刊论文】HDPE/PA6 blends: parison formation behaviour in extrusion blow molding
黄汉雄, H.-X. Huang∗, C.-M. Liao
Polymer Testing 22(2003)509-513,-0001,():
-1年11月30日
The dependence of the diameter swell and sag of a parison extruded from blends of high-density polyethylene (HDPE)/polyamide-6 (PA6)/compatibilizer on the blend composition and flow rate is determined by analyzing video images of the parison. Among the blends tested, blends with a PA6 concentration of 35% or below exhibit an appreciable swell. A greater degree of sag begins to appear for the blend with a PA6 content of 45%. A neural network approach is applied to the experimental data, leading to a model for predicting the diameter swell profile from new levels of input variables, including the blend composition and flow rate.
Blends, High-density polyethylene, Polyamide-6, Extrusion blow molding, Parison formation, Neural network method
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