先进先出式注射成型机的研制和数值模拟及质量控制
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摘要
注射成型是现代塑料工业中极为重要的一种加工技术，塑料制品以其质轻、价廉且具有优良的综合机械性能而在机械、电子、航空、汽车、通信等领域的应用不断扩展。这些应用场合不仅对塑料制品的尺寸精度，而且对制品形状的复杂程度提出了很高的要求，这使得对注射制品质量的控制变得至关重要。注射成型工艺复杂，制品质量受到成型机械、材料性能及工艺参数等多种因素的影响。本文针对热塑性塑料的成型过程，以注射成型机的设计——成型过程模拟——成型工艺优化为主线，设计了先进先出式注射成型机，构造成型过程的数学模型，实现成型过程的动态分析，研究成型过程的变化历史与制品质量的关系，将现代优化算法与数值模拟相结合，定量分析成型工艺参数与制品性能之间的关系，从而实现制品质量控制的终极目标。
1．注射机的技术参数、性能与物料性质、注射成型工艺有着密切的关系。本文对注射成型机的两大主要部件(注射装置和合模装置)进行了结构分析。对其塑化装置进行了创新设计，采用国际上最先进的“先进先出”方式，把液压驱动螺杆置于注射活塞中，将注射和塑化部件合而为一，简化了结构，从而优化了工艺；并对其主要零件螺杆、螺杆头、机筒和喷嘴进行了设计。针对合模装置，结合增压式和充液式合模装置的优点，设计了内装增压缸的自吸阀式合模装置，结构紧凑，合模精度高，锁模力增大，稳定性提高，并解决了液压锁模装置不易解决的漏油问题。
2．本文从流变学基本理论出发，通过对充模和保压过程的量纲分析，建立了注射成型充填和保压过程的数学模型。采用FEM╱FDM／CVM程序对数学模型进行了数值求解，获得成型过程中的压力、温度、剪切速率等物理场的分布，用来预测成型过程中出现的制品质量缺陷；研究了保压过程对制品质量的影响，考察了三种保压模式(恒压保压、线性保压、阶跃式保压)与制品重量、沉降斑、体积收缩之间的影响关系，进而确定最优保压曲线。
3．本文提出了一种新的试验设计方法——析因试验设计法研究了注射成型过程中多变量因子之间的交互作用的影响，即从众多的试验因子中抽取与试验目标密切相关的单因子和交互因子。并将交互作用作为一种重要的试验因子列入正交试验表与其它单因子共同起作用，从而能够全面而有效地配置试验参数，优化注射成型的工艺参数。
4．本文在析因试验设计的基础上，利用Taguchi方法设计了L_(27)试验矩阵，选择重量、体积收缩和沉降斑作为注塑件的质量指标，分析模具温度、熔体温度、保压压力和保压时间四个因子及交叉因子对质量指标的影响，优化了注射成型工艺，且为智能设定参数提供了规则。
5．本文将人工神经网络技术与数值模拟相结合用于注射成型制品质量的优化，对注射成型过程进行模型辨识，建立了注塑件质量与工艺参数之间的量化模型，从根本上克服了以往回归分析法由于事先给定数学形式而导致信息丢失的可能性。
6．通过实验，对试验设计方法和神经网络方法所构建的数学模型进行了验证，实验得出的结果与计算值基本吻合。这表明本文提出的数学物理模型、试验设计方法可以用于优化注射成型工艺生产过程中，对塑料制品的设计和质量控制有重要的参考价值。
Injection molding is an important technology in modern plastic technology. On account of light weight, low price and good comprehensive mechanical properties, plastics get widely used in such industries as mechanics, electronics, aviation, automobile, communication and so on. Industrial parts are usually requested to have high precision and complicated shapes, so it is important to control the plastic parts quality. Product quality subjects to injection molding machine, material performance and process parameters. The present dissertation pays attention to the injection molding machine designing, polymer processing simulation, and the optimization of processing conditions. The FIFO injection molding machine is designed; the mathematical models and numerical algorithm are developed for processing simulation; the modern optimization algorithms and numerical simulation are integrated to optimize processing conditions; the qualities relations between processing parameters and mechanical behaviors are determined; and quality control is finally implied.
1. Injection molding machine is very important for processing technology, whose technical parameter and performance is related to plastic character and injection molding processing. Two important units' structure (injection and clamping unit) is anglicized. For injection unit, this design places screw into injection ram in order to realize FIFO injection; for clamping unit, combing the pressure-charged and liquid-filled clamping unit's merits, this design adopt self-feeding valve to complete clamping, whose clamping precision is high and closing pressure is large. Besides the reliability, precision and cost, this design emphasizes injection molding machine's floor space.
2. Following the principle theory of viscous flow, the dimensions of physical and geometrical variants in filling and packing phase are analyzed; the filling and post-filling stage's governing equations are obtained. A hybrid finite-element/finite-difference and the control volume method is employed to solve the equations, not only to obtain the pressure, temperature and shear rate field, but also to predict forming detects.
3. A new design of experiment- fractional factorial design is put forward to study the interaction effect between process parameters. So the important interaction and single factors are screened to put into orthogonal table and to optimize the processing parameters.
4. After the fractional factorial design, An L_(27) experimental matrix design based on Taguchi method is performed to optimize injection molded parts. The part weight, volume shrinkage and sink marks are selected as quality index, and the relationship between mold temperature, melt temperature, holding pressure, holding time and quality index are determined.
5. An artificial neural network with numerical simulation is used to optimize injection molded parts' quality, and the qualities relations between processing parameters and mechanical behaviors are determined. This method overcomes the drop-out of regression method.
6. Experimental verification is conducted to certificate the numerical model created by design of experiment and artificial neural network. The results are coincident. This showed analyzing and optimizing of injection molding processing by simulation method had important reference to production of molded parts and design of molds.
1．注射机的技术参数、性能与物料性质、注射成型工艺有着密切的关系。本文对注射成型机的两大主要部件(注射装置和合模装置)进行了结构分析。对其塑化装置进行了创新设计，采用国际上最先进的“先进先出”方式，把液压驱动螺杆置于注射活塞中，将注射和塑化部件合而为一，简化了结构，从而优化了工艺；并对其主要零件螺杆、螺杆头、机筒和喷嘴进行了设计。针对合模装置，结合增压式和充液式合模装置的优点，设计了内装增压缸的自吸阀式合模装置，结构紧凑，合模精度高，锁模力增大，稳定性提高，并解决了液压锁模装置不易解决的漏油问题。
2．本文从流变学基本理论出发，通过对充模和保压过程的量纲分析，建立了注射成型充填和保压过程的数学模型。采用FEM╱FDM／CVM程序对数学模型进行了数值求解，获得成型过程中的压力、温度、剪切速率等物理场的分布，用来预测成型过程中出现的制品质量缺陷；研究了保压过程对制品质量的影响，考察了三种保压模式(恒压保压、线性保压、阶跃式保压)与制品重量、沉降斑、体积收缩之间的影响关系，进而确定最优保压曲线。
3．本文提出了一种新的试验设计方法——析因试验设计法研究了注射成型过程中多变量因子之间的交互作用的影响，即从众多的试验因子中抽取与试验目标密切相关的单因子和交互因子。并将交互作用作为一种重要的试验因子列入正交试验表与其它单因子共同起作用，从而能够全面而有效地配置试验参数，优化注射成型的工艺参数。
4．本文在析因试验设计的基础上，利用Taguchi方法设计了L_(27)试验矩阵，选择重量、体积收缩和沉降斑作为注塑件的质量指标，分析模具温度、熔体温度、保压压力和保压时间四个因子及交叉因子对质量指标的影响，优化了注射成型工艺，且为智能设定参数提供了规则。
5．本文将人工神经网络技术与数值模拟相结合用于注射成型制品质量的优化，对注射成型过程进行模型辨识，建立了注塑件质量与工艺参数之间的量化模型，从根本上克服了以往回归分析法由于事先给定数学形式而导致信息丢失的可能性。
6．通过实验，对试验设计方法和神经网络方法所构建的数学模型进行了验证，实验得出的结果与计算值基本吻合。这表明本文提出的数学物理模型、试验设计方法可以用于优化注射成型工艺生产过程中，对塑料制品的设计和质量控制有重要的参考价值。
Injection molding is an important technology in modern plastic technology. On account of light weight, low price and good comprehensive mechanical properties, plastics get widely used in such industries as mechanics, electronics, aviation, automobile, communication and so on. Industrial parts are usually requested to have high precision and complicated shapes, so it is important to control the plastic parts quality. Product quality subjects to injection molding machine, material performance and process parameters. The present dissertation pays attention to the injection molding machine designing, polymer processing simulation, and the optimization of processing conditions. The FIFO injection molding machine is designed; the mathematical models and numerical algorithm are developed for processing simulation; the modern optimization algorithms and numerical simulation are integrated to optimize processing conditions; the qualities relations between processing parameters and mechanical behaviors are determined; and quality control is finally implied.
1. Injection molding machine is very important for processing technology, whose technical parameter and performance is related to plastic character and injection molding processing. Two important units' structure (injection and clamping unit) is anglicized. For injection unit, this design places screw into injection ram in order to realize FIFO injection; for clamping unit, combing the pressure-charged and liquid-filled clamping unit's merits, this design adopt self-feeding valve to complete clamping, whose clamping precision is high and closing pressure is large. Besides the reliability, precision and cost, this design emphasizes injection molding machine's floor space.
2. Following the principle theory of viscous flow, the dimensions of physical and geometrical variants in filling and packing phase are analyzed; the filling and post-filling stage's governing equations are obtained. A hybrid finite-element/finite-difference and the control volume method is employed to solve the equations, not only to obtain the pressure, temperature and shear rate field, but also to predict forming detects.
3. A new design of experiment- fractional factorial design is put forward to study the interaction effect between process parameters. So the important interaction and single factors are screened to put into orthogonal table and to optimize the processing parameters.
4. After the fractional factorial design, An L_(27) experimental matrix design based on Taguchi method is performed to optimize injection molded parts. The part weight, volume shrinkage and sink marks are selected as quality index, and the relationship between mold temperature, melt temperature, holding pressure, holding time and quality index are determined.
5. An artificial neural network with numerical simulation is used to optimize injection molded parts' quality, and the qualities relations between processing parameters and mechanical behaviors are determined. This method overcomes the drop-out of regression method.
6. Experimental verification is conducted to certificate the numerical model created by design of experiment and artificial neural network. The results are coincident. This showed analyzing and optimizing of injection molding processing by simulation method had important reference to production of molded parts and design of molds.
引文
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