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.     

Self-optimizing MPC of melt temperature in injection moulding

The parameters in plastic injection moulding are highly nonlinear and interacting. Good control of plastic melt temperature for injection moulding is very important in reducing operator setup time, assuring consistent product quality, and preventing thermal degradation of the melt. Step response testing was performed on the barrel heating zones on an industrial injection moulding machine (IMM). The open loop responses indicated a high degree of process coupling between the heating zones. From these experimental step responses, a multiple-input-multiple-output model predictive control strategy was developed and practically implemented. The requirement of negligible overshoot is important to the plastics industry for preventing material overheating and wastage, and reducing machine operator setup time. A generic learning and self-optimizing MPC methodology was developed and implemented on the IMM to control melt temperature for any polymer to be moulded on any machine having different electrical heater capacities. The control performance was tested for varying setpoint trajectories typical of normal machine operations. The results showed that the predictive controller provided good control of melt temperature for all zones with negligible oscillations, and, therefore, eliminated material degradation and extended machine setup time.     

<Emphasis Type="Bold">Optimisation of Plastic Injection Moulding Process with Soft Computing</Emphasis>

The paper presents a hybrid strategy in a soft computing paradigm for the optimisation of the plastic injection moulding process. Various plastic injection molding process parameters, such as mold temperature, melt temperature, injection time and injection pressure are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimise the process parameters. An approximate analysis model is developed using a Back-propagation neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimisation problem, a nonbinary genetic algorithm is applied to solve the optimisation model. The effectiveness of the improved strategy is shown by an example.     

Residual stress analysis of the post-filling stage in injection moulding

This residual stress accumulated during the post-filling and cooling stages will lead to warpage of parts after demolding. Based on the principle of viscoelastic mechanics, the linear isothermo-viscoelastic constitutive equation is established. Then this constitutive equation is deduced according to the analysis of the thermorheologically simple material for given boundary conditions of the temperature field. Considering the restraint conditions of mould and the characteristics of injection moulding at the post-filling stage, some reasonable hypotheses have been assumed and hence the stress model is constructed. This mathematical model is solved using the finite difference method. The results can help predict the warpage of plastic products .     

应用进出油口独立控制原理改善泵控差动缸系统效率

针对新型双调速泵控制的差动缸电液伺服系统,特别是应用于注塑机锁模机构的情况进行了研究。在总压力和腔压力控制的基础上,进一步提出应用缸进口和出口独立控制的思想,使缸在运动过程中,背压保持较低的值,系统的压力始终与负载相适应,在满足系统动静态特性的同时,可充分发挥每台电动机的功率能力,同时降低了系统本身的能量消耗和液压泵的发热,减小电动机功率损失近20％,研究工作得到了仿真和试验验证。     

Adaptive neural network controller for the molten steel level control of strip casting processes

The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semiexperimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional PID controller.     

低压注塑机注射装置智能化温度控制研究

低压注塑机注射装置用于将塑化态的热熔胶注射至模腔内,其温度控制的稳定性和快速性关系到注塑制品质量。为研究该装置温度特性,提出了一种注射装置温控系统的近似模型,并采用自组织、自学习和自适应的径向基函数神经网络与PID控制相结合的控制策略。在该控制策略中,利用RBF神经网络提供的Jacobian矩阵信息实时调整PID控制参数。经仿真和测试证明该方法具有响应速度快、稳定性好的特点。     

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.     

In-situ neural network process controller for copper chemical mechanical polishing

Process control is one of the key methods to improve manufacturing quality. This research proposes a neural network based run-to-run process control scheme that is adaptive to the time-varying environment. Two multilayer feedforward neural networks are implemented to conduct the process control and system identification duties. The controller neural network equips the control system with more capability in handling complicated nonlinear processes. With the system information provided by this neural network, batch polishing time (T) an additional control variable, can be implemented along with the commonly used down force (p) and relative speed between the plashing pad and the plashed wafer (v). Computer simulations and experiments on copper chemical mechanical polishing processes illustrate that in drafting suppression and environmental changing adaptation that the proposed neural network based run-to-run controller (NNRTRC) performs better than the double exponentially weighted moving average (d-EWMA) approach. It is also suggested that the proposed approach can be further implemented as both an end-point detector and a pad-conditioning sensor.     

现代注塑机控制技术的现状与展望

注塑机是现代产品制造业中主要加工设备之一，是成型塑料制品的专用设备。近年来注塑机控制技术的飞跃发展为实现塑料制品的先进生产奠定了重要基础。这里较详细地概述了现代注塑机的控制理论、控制系统组成以及控制技术水平的现状，并对注塑机控制技术的发展作了展望。     

注塑机的PCC控制系统

本文介绍了新型可编程计算机控制器(PCC)的特点、多任务处理的编程方式以及在注塑机控制上的应用，并对注塑机PCC控制系统的硬件构成和软件设计进行了重点介绍。     

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.     

基于成形模拟的注塑件熔接缝确定与评价

在塑料注射成形流动保压模拟结果的基础上，提出了基于节点特征模型的熔接缝位置确定新算法，通过对熔接缝性能影响因素的深入分析，创建了基于人工神经网络方法的熔接缝性能评价模型，首次实现了满足工程需要的熔接缝性能定量预测。     

注塑模流道尺寸与型腔充填关系的实验研究

为充分发挥模具效能,提高生产力,降低单件成本,生产中经常使用一模多腔模具.优化分流道设计对模具来说是非常重要的.流道的主要作用是使熔体从喷嘴快速而顺畅地注入模具型腔内,微小塑件的注塑成型好坏很大程度上取决于流道的优化设计.笔者对微小塑件注塑成型过程中熔体流动充填过程进行了研究,一模四个微型型腔,分流道系统对称平衡布局,...     

A Hybrid Neural Network and Genetic Algorithm Approach to the Determination of Initial Process Parameters for Injection Moulding

Determination of the initial process parameters for injection moulding is highly skilled task and is based on a skilled operator's "know-how" and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed appraoch was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly from which good quality moulded parts can be produced without relying on experienced moulding personnel.     

圆柱-球体三自由度超声电机神经网络控制策略仿真

针对圆柱-球体三自由度超声电机难以建立精确数学模型的特点,设计了神经网络控制系统。该控制系统采用包含输入层、中间层和输出层3层网络的BP神经网络控制器。应用该控制器,实现了电机精确定位控制。结果表明,神经网络控制器的性能稳定,克服了常规控制存在的不足,得到了满意的控制效果。     

一种伺服电机变速泵液压系统的节能注塑机

通过注塑机节能液压系统的回顾与对比,得出伺服电机变速泵节能注塑机兼具精密与节能两大优点,是当今国产注塑机的发展方向.     

Process Transfer Function Development for Optical Media Manufacturing

The injection moulding of digital video disk substrates requires the selection of over forty process parameters to ensure the satisfaction of over twenty quality attributes. The work described characterised the behaviour of this complex moulding process through a formal set of design of experiments (DOE) consisting of: 1. Main effects DOE. 2. Multiple decoupled DOE. 3. Optimisation DOE. The described characterisation methodology identified critical process parameters and modelled linear, interaction, and non-linear effects of multiple quality attributes with regression coefficients varying from 65% to 98%. The developed empirical models are being used to facilitate advanced material development and production optimisation of digital video disks, but the described characterisation methodology can also be applied to other complex manufacturing processes.     

Shift robust control during inertia phase for random disturbance load

The automatic transmission of earthmoving machinery generally has problems of shifting shock and poor control robustness. Low-frequency high-energy random loads and large-space model uncertainties are the main causes of these problems. Therefore, we considered a type of wheel loader automatic transmission as the research object and propose a robust control method for the shifting inertia phase of the earthmoving machinery automatic transmission. The control method includes a robust controller K_b, a disturbance compensation controller K_d and a feedforward compensator K_f. The robust controller K_b is designed by μ synthesis method to resist the uncertainties of parameters and structure and guarantee the output speed to track the reference input. A load dynamic estimation method for random disturbance load of automatic transmission is proposed, which improves the compensation effect of disturbance compensation controller K_d. The feedforward compensator K_f is used to eliminate the influence of the reference input on the system. The above control methods were applied to the whole machine simulation platform of a certain type of wheel loader, and the random disturbance load of the measured under real working conditions was taken as input load. Under the conditions of no-load, half-load and full load, the control method has good smoothness and strong robustness. This paper has reference value for solving the inertial phase shift robust control of transmission under random disturbance load.

     

基于PLC控制的160型注塑机液压系统设计

对传统160型注塑机的液压系统进行适当改进,采用PLC替代继电器控制电路,实现自动控制,以提高液压系统的自动控制程度、可靠性和稳定性,大大提高注塑机的生产效率和自动化程度,减少了系统故障.具有一定的实用性.