多型腔注射模充填不平衡试验

多型腔注射模在实际生产中有着广泛应用,充填平衡是保证多型腔模塑制品质量均匀一致的关键。自然平衡流道中也会发生充填不平衡现象,其原因可能是熔体流动产生的剪切热所致,但一直没有试验结果予以证明。基于此,利用可视化注射模具和红外温度传感器,通过直接观测熔体在流道和型腔中的动态流动行为并测量型腔入口处熔体的温度变化,对不同注射速率下不同材料在自然平衡多型腔注射模的充填不平衡进行研究。结果表明,由于剪切热的作用,主流道中不均匀但对称的熔体温度分布在分流道中失去对称性是产生充填不平衡的根本原因;充填不平衡程度不但取决于主流道中熔体的温度分布,还取决于分流道中凝固层的分布及熔体粘度对温度变化的敏感性。解决自然平衡多型腔注射模充填不平衡问题的根本,在于改善或消除分流道中熔体温度分布在流动平面的不对称性。     

多型腔PET瓶坯模具热流道创新设计

对96型腔PET瓶坯模具热流道进行创新设计,突破传统三流道设计,采用五级流道设计,使用整体注塑成型方法,保证了在模具流道里面的塑料均匀地流到各个型腔中,产品冷却时间一致,一次能生产出多个产品,提高了模具的生产效率。且圆弧连接减小了材料冲击力,凝料流畅、缺陷少,解决了多型腔热流道模具设计的关键问题,并对注塑过程进行模拟分析,数据吻合,设计合理。     

模具型腔气体压力对微发泡注塑件表面质量的影响

微发泡注塑成型制品表面的螺旋纹及银纹缺陷阻碍了此新工艺的推广应用,此缺陷主要是由填充过程中熔体流动前沿的泡孔发生破裂造成的。为提高微发泡注塑件的表面质量,提出一种利用高压氮气进行加压并使用高压容器进行稳压的模具型腔气体压力控制方法,构建相应的模具型腔气体压力控制装置和控制系统,进行某种医疗器械外观产品的气体反压(Gas counter pressure,GCP)微发泡注塑试验,研究填充过程中模具型腔气体压力对微发泡注塑件表面质量的影响规律。结果表明,随着模具型腔气体压力的提高,微发泡注塑件表面螺旋纹及银纹逐渐减少直至消除,塑件表面光泽度显著提高,塑件表面粗糙度明显降低。对选用的ABS材料,模具型腔气体压力为3 MPa时可获得表面质量优良的微发泡注塑产品。     

注射成型过程熔体前沿充填不平衡现象的试验研究

注射成型的充填平衡性问题是影响塑料制品成型精度的主要因素之一.注射成型过程中,塑料熔体充填型腔时流动前沿会产生左右偏移的现象,从而导致充填的不平衡.为研究这种充填不平衡现象的机理,设计出一套装有红外线纤维传感器的移动式温度测量系统,采用该系统及可视化手段对一模两腔注塑成型过程熔体流动前沿在型腔内的偏移现象进行观察和分析.试验结果表明,不同注射速率下流动前沿的偏移现象及偏移程度也不同,流动前沿的偏移与熔体在流道内的流动行为以及温度场的变化有关.根据试验结果及剪切热理论所建立的流道内熔体流动行为模型可以较好地描述熔体前沿充填不平衡现象的生成机理.流道内熔体流动的示踪试验表明该模型与实际情况相吻合.     

笔记本电脑转轴注塑模具设计

通过分析笔记本电脑转轴的使用要求,结合注塑成型工艺分析,对笔记本电脑转轴的部分结构进行改进,以更好地满足塑件注塑成型要求,并保证塑件成型质量和使用性能。借助UG软件对笔记本电脑转轴进行了一模两穴侧进浇的两板模结构设计,模具中采用了下沉式的浇口套结构,通过将浇口套安装在型腔中,大大减少了浇口套凝料的浪费,并提高了塑件注塑成型质量。     

基于MoldFlow组合型腔浇注系统的平衡设计

为了保证模具的数量与客户产品组装使用量相匹配,打印机中的2种齿轮采取2＋1组合模穴成型。利用MoldFlow软件提供的流道平衡分析模块,优化流程中各段流道的直径,使塑料熔体从主流道进入各型腔的压力保持相等,从而保证了熔体能够同一时间充满3个型腔,实现了3个齿轮的平衡浇注成型。     

基于Pro/E的筐壳类塑件注塑模具设计与制造

针对筐壳类塑件壁薄、难以成型的特点,探讨了Pro/E软件在注塑模具设计中的优点,介绍了注塑模具开发的一般流程.设计了前、后、左、右四个侧向的斜导柱侧抽芯机构,来成型侧面孔和实现侧向抽芯.把整个型腔设计成侧滑块和型腔底板的形式,降低了模具型腔加工的难度.采用模具专家系统EMX,提高了设计效率.在传统的设计基础上,将模流分...     

空调温控器上盖热流道注塑模具设计

针对目前空调温控器上盖生产中出现的填充不均、翘曲变形和熔接痕等塑件质量、外观较差,且流道废料多的问题采用热流道技术辅以模流分析解决了这个问题。通过对空调温控器上盖塑件的结构和工艺性分析,确定一模四腔的热流道注塑模具设计。基于Pro/E对热流道浇注系统、成型零部件结构、侧抽机构以及推出机构等进行三维实体设计,利用模流分析软件进行模流分析。生产结果表明,所设计的模具结构合理,生产的塑件达到了图纸的技术要求。     

多孔复杂型腔塑件注塑模具CAD/CAE数字化研究

在UG中建立了塑件的三维数据图,借助模流分析(Moldflow)软件对塑件注射压力、时间、温度、浇注系统进行计算机辅助工程分析(CAE),确定最优的注射压力、注射时间、模具温度、熔体温度、浇口位置及流道等。优化了注塑工艺参数、注塑模具的设计,确定了最佳的注塑工艺方案,快速成型出优质的塑料制品,提高了模具的质量,缩短了模具的开发周期。     

微注塑充模流动过程熔体黏度影响规律的数值模拟

微型塑件注射成型充模流动过程中,与宏观塑件相比,塑料熔体黏度随剪切速率的变化对流动的影响有很大的不同.对比微观和宏观黏度模型,运用流体分析软件Fluent,对微注塑充模流动过程熔体黏度的影响规律进行了研究.分析了熔体黏度的分布规律,结果表明微尺度降低了熔体黏度.研究了微尺度熔体黏度模型对熔体速度场、温度场和压力场的影响规律,结果表明:微尺度黏度模型的速度相对较大,近壁面区熔体温度相对较高,对微通道中的压力分布几乎没有影响.总体而言,由于通道微尺度而造成的熔体黏度变化有利于微注塑成型.     

Digitized die forming system for sheet metal and springback minimizing technique

The design of the runner and gating systems is of great importance to achieving a successful injection moulding process. The subjects of this study are the finite element and abductive neural network methods applied to the analysis of a multi-cavity injection mould. In order to select the optimal runner system parameters to minimize the warp of an injection mould, FEM, Taguchi’s method and an abductive network are used. These methods are applied to train the abductive neural network. Once the runner and gate system parameters are developed, this network can be used to accurately predict the warp of the multi-injection mould. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neural network in order to search the gate and runner system parameters. This method obtains a satisfactory result as compared with the corresponding finite element verification.     

Injection moulding optimisation of multi-class design variables using a PSO algorithm

Injection moulding optimisation seeks to achieve the highest possible moulding quality under the specified constraints. To this end, the factors (design variables) affecting the moulding quality should be adjusted, including those of process parameters, mould design, part geometry, etc. Past work in this aspect is primarily focused on tuning the process parameters and mould design (e.g., gate location, runner and cooling channel layout), with less attention on the part geometry, and none on them all. To address this problem, this paper presents a PSO (particle swarm optimisation) algorithm for the optimisation of multi-class design variables, such as the part thickness, process parameters (melt temperature, mould temperature, injection time) and gate location. The optimisation is targeted at different aspects of moulding quality, including part warpage, weld lines, air traps, and so on. In applying the PSO algorithm, the paper proposes a modified elite archiving method, which can expedite the convergence speed, hence improving the efficiency of the algorithm. A computer program was developed that automates the steps such as adjusting the part thickness, the injection moulding process parameters and the gate location, activating the CAE software to simulate the injection moulding process, retrieving the simulation results, and evaluating the objective functions. The whole procedure iterates a number of generations by following the search process of the algorithm. A case study was also presented to illustrate as well as to test the proposed methodology, which was demonstrated as both effective and efficient.     

Study of process parameters effect on the filling phase of micro-injection moulding using weld lines as flow markers

Micro-injection moulding (micro-moulding) is a process which enables the mass production of polymer micro-products. In order to produce high-quality injection moulded micro-parts, a crucial aspect to be fully understood and optimised is the filling of the cavity by the molten polymer. As a result, the relationships between the filling pattern and the different process parameter settings have to be established. In this paper, a novel approach based on the use of weld lines as flow markers to trace the development of the flow front during the filling is proposed. The effects on the filling stage of process parameters such as temperature of the melt, temperature of the mould, injection speed and packing pressure have been investigated. An optical coordinate measuring machine has been employed for the investigation. The micro-cavity, which presents micro-features ranging from 600 μm down to 150 μm, has been manufactured by micro-electrodischarge machining. A commercially available polystyrene grade polymer has been moulded using a high-speed injection moulding machine. The design of experiment technique was employed to determine the effect of the process parameters on the filling phase of the micro-cavity. In addition, extensive measuring uncertainty analysis was performed to validate the experimental plan. Results show that the temperature of the mould and the injection speed are the most influencing process parameters during the injection moulding of a micro-component.     

Micro genetic algorithm based optimal gate positioning in injection molding design

The paper deals with the optimization of runner system in injection molding design. The design objective is to locate gate positions by minimizing both maximum injection pressure at the injection port and maximum pressure difference among all the gates on a product with constraints on shear stress and/or weld-line. The analysis of filling process is conducted by a finite element based program for polymer flow. Micro genetic algorithm (mGA) is used as a global optimization tool due to the nature of inherent nonlinearlity in flow analysis. Four different design applications in injection molds are explored to examine the proposed design strategies. The paper shows the effectiveness of mGA in the context of optimization of runner system in injection molding design.     

CAE技术在注塑成型浇注系统优化设计中的应用

注塑模浇注系统是塑料注塑成型模具设计中的关键技术之一,利用CAE软件Moldflow可对不同浇口位置进行流动模拟分析,预测可能存在的气泡和熔接痕的位置,为模具设计人员优化模具设计提供依据,提高一次试模的成功率。并经过实践证明,注塑成型中通过浇注系统位置的优化可以显著提高产品的质量。     

A computational system for process design of injection moulding: Combining blackboard-based expert system and case-based reasoning approach

The process design of injection moulding involves the selection of the injection moulding machine, mould design, production scheduling, cost estimation, and determination of injection moulding parameters. Expert system approaches have been attempted to derive the process solution for injection moulding in the past few years. However, this approach has been found to be incapable of determining the injection moulding parameters owing to the difficulty in setting the moulding parameters. In addition, the existing expert systems for process design lack the proper architecture for organising a heterogeneous knowledge source. In this paper, the combination of a blackboard-based expert system and case-based reasoning approach is introduced to make up the deficiencies of the existing expert-system approach to the process design, from which a computational system for process design of injection moulding, named CSPD, was developed and described. CSPD first derives the process solution including the selection of injection moulding machine and mould base, tooling cost, and processing cost estimation, and production scheduling based on the blackboard-based expert-system approach. It is then followed by the determination of the injection moulding parameters based on the case-based reasoning approach and the previously derived partial solution.     

A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach

Process design of injection moulding involves the selection of the injection moulding machine, mould design, production scheduling, cost estimation, and determination of injection moulding parameters. An expert system approach has been used to derive the process solution for injection moulding over the past few years. However, this approach is found to be incapable of determining the injection moulding parameters owing to the fragile nature of the knowledge for setting the moulding parameters. In addition, the existing expert systems for process design lack proper architecture for organising heterogeneous knowledge sources. In this paper, the combination of a blackboard-based expert system and a case-based reasoning approach is introduced to eliminate the deficiency of the existing expert-system approach to process design, from which a computational system for the process design of injection moulding, named CSPD, has been developed. CSPD first derives the process solution including the selection of the injection moulding machine and the mould base, tooling cost, processing cost estimation, and production scheduling based on the blackboard-based expert-system approach. It is then followed by the determination of the injection moulding parameters based on the case-based reasoning approach and the previously derived partial solution.     

<Emphasis Type="Bold">Application of Neural Networks in Injection Moulding Process Control</Emphasis>

In order to produce precise injection moulding products, a closed-loop controller is employed instead of the open-loop control of a traditional injection moulding machine for monitoring the filling and post-filling phases of the injection processes. Since the injection moulding system has complicated and variable dynamics, the classical control theory is difficult to implement for the precise injection moulding processes. Here, two intelligent neural network control strategies are employed to adjust the injection speed of the filling phase and control the nozzle pressure of the post-filling phase. Since the neural controller has learning ability to track the variation of the injection processes, this control strategy has the advantages of adaptivity and robustness for general purpose application to an injection moulding machine. The experimental results show that this controller has good performance in the actual injection moulding processes.