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。

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.

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.

挤出吹塑过程由型坯成型、型坯吹胀与制品冷却三个阶段构成。采用不同的方法对该三阶段的机理问题进行了研究。采用神经网络方法预测了受模口温度和挤出流率影响的型坯成型阶段的膨胀。利用建立起来的神经网络模型预示的膨胀与实验结果很吻合，且可在一定范围内，预示不同工艺条件下型坯的直径膨胀和壁厚膨胀，为型坯的直径和壁厚的在线控制提供了理论依据。基于薄膜近似和neo-Hookean 本构关系，建立了描述型坯自由吹胀的数学模型，并通过实验方法获得了型坯吹胀的瞬态图象。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。该模型还可预示型坯的自由吹胀对材料性能、型坯尺寸和工艺条件等的依赖性。基于ANSYS有限元软件，对吹塑制品的三维冷却进行了模拟，预示了制品厚度方向任一位置的瞬态温度分布，并可预示成型工艺参数、制品壁厚、塑料与模具材料的热性能以及吹塑模具冷却的强度与时间等对吹塑制品冷却的影响，这可为进一步分析吹塑制品的显微结构和性能提供温度数据。