注塑件CAD建模与CAE智能处理的集成化研究

CAD／CAE技术的集成化研究是当前的一个研究热点，对于注塑件／模而言，CAD信息向CAE传输中存在信息丢失和少智能问题，而CAE的结果向CAD信息的反馈过程中也存在信息缺乏衔接和少智能化的问题，利用CBR（基于事件的推理）对工艺参数进行智能推理，然后结合推理出的参数值以及CAD中过滤出的信息，输入到CAE进行分析，对CAD中的特征信息与CAE后处理的信息进行合理的匹配，建立对后处理结果的缺陷解释专家系统。重点对工艺参数优选的CBR及后处理的专家解释系统和数据流的流动过程进行了深入的研究，提出CAD／CAE并行环思想，从而方便了注塑件特征造型与CAE智能处理的集成。     

Iterative learning fault-tolerant control for injection molding processes against actuator faults

The injection molding process is a typical multi-phase batch process. As the filling and packing-holding phases share the same actuator, faults occurring in the actuators may cause serious impact on the performance and running time. Because these two phases are of crucial importance in relation to the final quality of the product, to solve this problem is essentially meaningful. This paper proposes iterative learning fault-tolerant control (ILTFC) in terms of common multi-phase batch processes and then applies it to the injection molding processes. To develop the ILFTC design, the multi-phase batch process is treated as a switched system composed of different dimensional subsystems and then converted to an equivalent two-dimensional (2D) switched fault-tolerant Rosser model. A hybrid fault-tolerant law is then designed based on an average dwell time method. Sufficient conditions and minimum running time guaranteeing the exponential stability under both normal and fault conditions are obtained. Under the proposed control law, the control performance and running time will restore to the previous level before actuator faults occur. The efficiency and merits of the proposed scheme is illustrated by an injection molding process, and results show that it can guarantee the stability and minimum running time whether the process is in normal operation or in case of actuator faults.     

基于B／S结构的注塑机远程故障诊断系统的设计

为了提高注塑机故障诊断效率,提高企业经济效益,解决诊断人员不足的问题,提出将远程故障诊断系统应用到注塑机故障诊断中。给出了远程诊断的系统框架,基于三层B／S结构的系统模式,采用将子时段PCA、小波包分析和案例推理相结合的技术作为系统的故障诊断技术,有效实现了注塑机的远程故障诊断。     

执行器故障多率采样间歇过程的鲁棒 耗散迭代学习容错控制

针对一类具有干扰和执行器故障的多率采样间歇过程,提出一种具有鲁棒耗散性能的迭代学习容错控制算法.通过提升技术将多采样率过程用慢速率采样的状态空间模型来描述,并基于二维系统理论,把迭代学习控制过程转化为等价2D Roesser故障系统,再沿时间和迭代方向设计具有耗散性能的反馈容错控制器,并以线性矩阵不等式形式给出容错控制器存在的充分条件,同时确保多率采样间歇过程在正常和故障条件下的耗散性能.注塑过程的注射速度控制仿真验证了方法的有效性和可行性.     

注塑生产中残留物监测系统的研究

将设计一种将摄像头应用于注塑机残留物的监测中.该系统能够通过摄像头去采集注塑现场的信息,利用单片机微处理系统对采集的图像数据进行实时地处理,最后达到自动地去控制注塑机的运转功能,提高注塑机的自动化程度.主要结合了图像处理算法和单片机的优点,并进行不断地优化系统.试验结果表明该系统能够实时高效地进行自动监测.     

Induction machine fault detection using clone selection programming

A clonal selection programming (CSP)-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an CSP-based classifier for fault identification and classification. In this paper, the proposed CSP-based machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults correctly, but the rules generated is also very simple and compact and is easy for people to understand, This will be proved to be extremely useful for practical industrial applications.     

低成本单伺服注塑机械手控制系统设计

在注塑行业,注塑机械手已经逐渐替代人工操作并发挥着重要作用,而注塑机械手的精度又直接影响到注塑机的工艺和注塑件的质量.针对目前注塑机械手存在部分问题的现状,设计了一种基于STM32单片机的高精度低成本的注塑机械手控制系统,并对其硬件系统和软件系统做了详细的介绍.经过实验测试,注塑机械手达到了预期的功能和控制精度,但是其价格相比工业上的注塑机机械手降低了30％.     

Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization简

In view of the high energy consumption and low response speed of the traditional hydraulic system for an injection molding machine, a servo motor driven constant pump hydraulic system is designed for a precision injection molding process, which uses a servo motor, a constant pump, and a pressure sensor, instead of a common motor, a constant pump, a pressure pro-portion valve, and a flow proportion valve. A model predictive control strategy based on neurodynamic optimization is proposed to control this new hydraulic system in the injection molding process. Simulation results showed that this control method has good control precision and quick response.     

A Light-Weight Fault Injection Approach to Test Automated Production System PLC Software in Industrial Practice

A light-weight software-implemented fault injection (SWIFI) testing approach is introduced, focusing on technical process faults and system faults. The reaction of automated production systems (aPSs) and their programmable logic controller (PLC) software to these faults is tested. In order to tailor the testing approach to the aPS domain in industrial practice, our test generation is based on a classification of possible deviations, i.e. a classification of possible technical process and system faults as the PLC perceives them. As a result, both specification and test execution become more efficient for practitioners. Furthermore, the test specification is tailored for execution on IEC 61131-3 programming environments. In this, the execution of test cases both against simulation or the real aPS, is enabled.     

Ceramic micro parts produced by micro injection molding: latest developments

Powder injection molding is a preferred technology for the production of micro parts or microstructured parts. Derived from the well known thermoplastic injection molding technique it is suitable for a large-scale production of ceramic and metallic parts without final machining. To achieve good surface quality and control the part size and distortions is an important goal to allow mass production. This means that all process steps like part design adjusted for MIM/CIM-technology, appropriate choice of powder and binder components and injection molding simulation to design the sprue are required. Concerning the injection molding itself high quality mold inserts, high-precision injection molding with suitable molding machines like Battenfeld Microsystem50 or standard machine with special equipment like variotherm or evacuation of the molding tool and an adjusted debinding and sintering process have to be available. Results of producing micro parts by powder injection molding of ceramic feedstock will be presented.     

Tennessee-Eastman过程的学习型案例推理故障诊断方法

为了提高Tennessee-Eastman(TE)过程的故障诊断准确率,本文研究一种学习型伪度量(learning pseudo metric,LPM)代替距离度量的案例检索方法,并建立了TE过程的案例推理(case-based reasoning,CBR)故障诊断模型.首先建立LPM度量准则并对LPM模型进行训练,其次度量目标案例与每一个源案例的相似度,从中检索与目标案例相似的同类案例,再采用多数重用原则从同类案例中决策出目标案例的解,最后通过TE过程的运行数据对该方法的性能进行测试,并与典型的CBR和BP(back-propagation)神经网络和支持向量机等方法进行对比,表明本文方法能有效提高故障诊断准确率,在实际化工过程中具有一定的推广应用价值.     

Emulation of Software Faults: A Field Data Study and a Practical Approach

The injection of faults has been widely used to evaluate fault tolerance mechanisms and to assess the impact of faults in computer systems. However, the injection of software faults is not as well understood as other classes of faults (e.g., hardware faults). In this paper, we analyze how software faults can be injected (emulated) in a source-code independent manner. We specifically address important emulation requirements such as fault representativeness and emulation accuracy. We start with the analysis of an extensive collection of real software faults. We observed that a large percentage of faults falls into well-defined classes and can be characterized in a very precise way, allowing accurate emulation of software faults through a small set of emulation operators. A new software fault injection technique (G-SWFIT) based on emulation operators derived from the field study is proposed. This technique consists of finding key programming structures at the machine code-level where high-level software faults can be emulated. The fault-emulation accuracy of this technique is shown. This work also includes a study on the key aspects that may impact the technique accuracy. The portability of the technique is also discussed and it is shown that a high degree of portability can be achieved     

基于案例与规则推理的故障诊断专家系统

设计并实现基于案例的推理(CBR)与基于规则的推理(RBR)的故障旋转机械诊断专家系统。采用CBR与RBR串行方式进行推理,优先通过案例匹配方式寻求诊断结果,在不适用情况下转入通用性规则推理,并将诊断结果反馈给知识库进行优化。应用结果表明,该系统诊断结果与实际相符合,且诊断速度快、针对性强。     

盾构机机械类故障诊断研究进展综述

盾构机在运行过程中不可避免地会发生故障,这些故障中以机械类故障较为常见,对盾构机进行状态监测与故障诊断是保证其掘进作业安全、高效的重要手段。详细论述了盾构机刀具磨损、轴承、液压等常见机械类故障及相应的诊断方法,分析了各类故障诊断存在的问题并提出了相应的解决方案。最后对盾构机机械类故障诊断技术进行了建议与展望,并指出在故障诊断系统生态技术框架下,实现盾构机机械类故障诊断的信息化和智能化将会是盾构机未来的发展目标。     

基于MIL-STD-1553B的运载火箭故障诊断与故障注入方法

针对运载火箭系统众多,故障形式多样,出现故障难以准确定位的特点,研究了一种基于MIL-STD-1553B总线(下称1553B总线)的故障诊断和故障注入方法,采用故障树分析法确定失效故障链,实时诊断和预测系统故障;由于实际的系统故障数据较少,通过失效机理分析获得运载火箭的故障库,采用1553B总线对系统注入特定故障;在运载火箭地面故障诊断与注入系统中,原有的拓扑结构维持不变,故障诊断系统作为1553B总线监视器,故障注入系统作为1553B总线的远程终端,并与运载火箭的众多子系统互联,通过总线耦合器连接火箭外部的发射控制平台;最后通过基于实际工程型号开发的故障诊断平台和故障注入平台进行了测试,测试结果表明具有满意的故障覆盖率。     

基于控制参数化的注塑工业过程最优反馈控制方法

本文针对一类典型的注塑工业过程系统, 研究了注塑填充过程中产生的熔体流动速度最优跟踪控制问题, 提出了一种基于控制参数化的计算最优反馈控制器设计方法以实现注塑过程中熔融聚合物流动前沿位移的最优跟 踪控制, 进而达到改善注塑零件性能的高效生产目标. 首先, 面向注塑工艺复杂生产过程建立了动态过程系统数学 模型, 提出了注塑机内部熔融聚合物流动前沿位置的动态最优跟踪控制问题; 其次, 设计了一种多级反馈控制律, 通 过控制参数化方法将控制反馈核进行了参数化表示, 将控制器设计问题转化为一序列最优参数决策问题; 然后, 通 过状态灵敏度方程分析方法, 求解出了目标函数及约束条件关于决策变量参数梯度信息的显式表达式, 并基于所提 供的梯度信息结合序列二次规划算法进行了高效优化迭代求解; 最后, 通过实验仿真验证了本文所提出的最优反 馈控制器设计方法的可行性和有效性.     

Case Based Diagnosis and Repair of Software Faults

Software debugging (the diagnosis and repair of faults in software) can be a difficult and time consuming problem. In many cases a problem which can take an inexperienced developer a long time to correct can be quickly identified and corrected by a more experienced one. This is often because they have seen that problem (or a similar one) before. In this paper we present a system (called WATSON) which automatically constructs an explanation of the fault and a repair strategy from a library of partial repair models. This system exploits Case Based Reasoning (CBR) technology to perform this construction. The paper introduces the concepts behind CBR before going on to describe the components of the WATSON system. It then illustrates, by example, how WATSON constructs a diagnosis and repair model from the case databases.     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

In the processing industry, frequent faults call for assistance in diagnosis, and case‐based reasoning (CBR) can provide solutions applied by other operators in the past. This study investigated whether salient case ratings promote an uncritical acceptance of solutions. In 2 experiments, subjects diagnosed faults with a simulated CBR system, and ratings were presented in graphical or verbal format. In most trials, the case with the highest rating provided the correct solution, while in catch‐trials, it did not. Graphical ratings were hypothesized to speed up solutions but discourage cross‐checking and lead to errors in catch‐trials. These hypotheses were not confirmed, even though Experiment 2 maximized the incentive of relying on case ratings. While graphical ratings led subjects to start with the most highly rated case, they did not impair situation analysis and accuracy. The results suggest that during fault diagnosis people are not easily misled into overtrusting a CBR system.     

Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis

This paper deals with the application computer-aided engineering integrating with statistical technique to reduce warpage variation depended on injection molding process parameters during production of thin-shell plastic components. For this purpose, a number of Mold-Flow analyses are carried out by utilizing the combination of process parameters based on three level of L₁₈ orthogonal array table. In the meantime, apply the design of experiments (DOE) approach to determine an optimal parameter setting. In addition, a side-by-side comparison of two different approaches of simulation and experimental is provided. In this study, regression models that link the controlled parameters and the targeted outputs are developed, and the identified models can be utilized to predict the warpage at various injection molding conditions. The melt temperature and the packing pressure are found to be the most significant factors in both the simulation and the experimental for an injection molding process of thin-shell plastic parts.