Situation dependent decision selector for production control in testing and rework cell

This paper presents a production control approach for the testing and rework cell in a dynamic and stochastic computer integrated manufacturing (CIM) system. The proposed situation dependent decision selector (SDDS) observes the status of the system and jobs at every decision point, and makes its three different decisions on dispatching, pre-emption, and dispatching within pre-emption in real time. This paper also develops a new feature selection algorithm for the SDDS. Experimental results show that the SDDS is better than other static control rules and another dynamic control approach with respect to the number of tardy jobs.     

Analysis of timeout mechanism in a testing-repair model

With a dispatching rule collaborated, timeout is an effort to maximise system performance, by disabling the access of a job to a resource when the job keeps the resource idle too long and then by giving a waiting job the access to the resource. This paper first defines a generic testing-repair model with a single tester and proves that there exists at least one optimal solution in two extreme timeout thresholds with respect to the makespan. Experimental results are presented to support the proof. This problem is then formulated as a discrete multi-objective optimisation problem with five common performance measures of production systems. The two extreme timeout thresholds are efficient solutions in all cases, and they are even dominant in several cases. This paper explores the properties of the timeout collaboration protocol and provides useful insights into applying the timeout mechanism to production systems.     

Corrective maintenance through dynamic work allocation and pre-emption: case study and application

This case study develops an innovative management and scheduling system for corrective maintenance of machines in a manufacturing facility. The study also involves a comparative evaluation of the proposed and the existing systems under a spectrum of operating conditions. A comprehensive simulation is used to evaluate system performances under a variety of settings which include reliability, service level, and cost consequences. The analysis is based on a full factorial experimental design. In summary, the developed self-regulating management system which involves dynamic work allocation and pre-emption is shown to yield higher machine availability and higher mechanic utilisation even with fewer mechanics. The study also finds that the new system is more streamlined, agile, and robust although it is subject to more-constrained machine reliability and mechanic service time environments. Further, a major reduction of current manpower can still achieve at least 95% machine availability, illustrating the cost effectiveness and efficacy of the developed system. This rule-based corrective maintenance system can be operated in uncertain environments on a real-time basis without additional reformatting costs and provides a competitive measure to deal with managerial issues such as low retention rate for skilled mechanics, highly uneven training levels and pay scales. The financial consequences and gains in strategic advantage with respect to the facility's operational structure are promising after implementation. Moreover, the system developed in this case study represents a meaningful starting point for a more vigorous theoretical research on the bucket brigade system to different functions in industrial and operations management.     

Computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem

This paper presents a computationally efficient neuro-dynamic programming approximation method for the capacitated re-entrant line scheduling problem by reducing the number of feature functions. The method is based on a statistical assessment of the significance of the various feature functions. This assessment can be made by combining the weighted principal components with a thresholding algorithm. The efficacy of the new feature functions selected is tested by numerical experiments. The results indicate that the feature selection method presented here can extract a small number of significant features with the potential capability of providing a compact representation of the target value function in a neuro-dynamic programming framework. Moreover, the linear parametric architecture considered holds considerable promise as a way to provide effective and computationally efficient approximations for an optimal scheduling policy that consistently outperforms the heuristics typically employed.     

不均衡训练集特征选择的印刷套准状态识别

目的 针对不均训练集导致印刷套准识别模型无法较好识别印刷套不准图像的问题,提出基于最大相关、最小冗余的印刷标志图像数据特征选择方法.方法 提取印刷标志图像的多维特征数据,计算特征与印刷套准和印刷套不准2类之间的相关性和特征之间的冗余度.确定特征选择的目标函数,通过增量搜索方法寻找最优特征,加入特征子集,实现不均衡印刷标志图像的特征选择.结果 文中的特征选择方法获得了3项不均衡数据分类性能评价指标,A为0.9900,R为0.9400,Gmean为0.9466.结论 在不均衡印刷标志图像套准识别中,文中提出的方法性能优于实验中的未处理方法、PCA方法、Relief方法和NCA方法.     

Product failure prediction with missing data

In production data, missing values commonly appear for several reasons including changes in measurement and inspection items, sampling inspections, and unexpected process events. When applied to product failure prediction, the incompleteness of data should be properly addressed to avoid performance degradation in prediction models. Well-known approaches for missing data treatment, such as elimination and imputation, would not perform well under usual scenarios in production data, including high missing rate, systematic missing and class imbalance. To address these limitations, here we present a method for predictive modelling with missing data by considering the characteristics of production data. It builds multiple prediction models on different complete data subsets derived from the original data-set, each of which has different coverage of instances and input variables. These models are selectively used to make predictions for new instances with missing values. We demonstrate the effectiveness of the proposed method through a case study using actual data-sets from a home appliance manufacturer.     

玻纤增强复合材料连续缠绕管材技术发展现状

复合材料连续缠绕管材凭借耐腐蚀、强度高等优势在市政饮水工程、输油管道、水处理、排污等领域具有广泛的应用。本文主要介绍了目前复合材料缠绕管材基体树脂的选型、缠绕工艺、生产设备和检测标准,对复合材料连续缠绕管材生产过程中可能出现的缺陷进行分析并归纳了现有复合材料管材的连接方式,最后对复合材料连续缠绕管材行业进行了总结与展望。     

基于复杂类型数据的发现特征子空间模型(DFSSM)的研究

探讨围绕知识发现领域中较为宏观、较为重大的问题。首先,根据复杂类型数据（包括Web数据、多媒体数据、空间数据、时间序列数据等）所具有的非线性动力学性质和特征,采用模式（定义为Hilbert空间中的矢量）来定量地表征复杂类型数据的多变性及具有的不确定状态和行为,并用模式的变化来刻画其整体知识发现过程的发展和演变规律;其次,以知识发现系统内在机理的研究为基础,构造了复杂类型数据知识发现系统的总体结构模型——发现特征子空间模型DFSSM;最后,用基于Web的文本挖掘系统和基于图像信息（气象云图）的知识发现系统作为实例进行了验证,结果表明DFSSM方法对于非结构化的文本数据及图像数据类型的知识发现过程具有指导性作用。因此,该结构模型具有较好的实用性与普适性,有望拓展到其他复杂类型数据的知识发现过程中。     

卡片导航平衡控制系统性能仿真研究

为了减少装配作业车间内因物料齐套产生的等待浪费,使具有装配约束的关联零件加工进度得到有效协同,设计一类卡片导航平衡控制系统 (control of balance by card-based navigation,COBACABANA)。其基于两类卡片循环回路实现任务投放与作业分派的可视化进度协同控制逻辑。本文详细介绍系统的运行机制和系统控制参量,通过构建一般化的装配作业车间仿真模型,探讨在不同装配关联度下各控制参量的性能变化。实验结果表明,COBACABANA系统性能良好,并且选择合适的控制参量就能够有效提升关联零件的进度协同性。     

An application of graphical analysis of repair data

This expository article presents a simple and informative non-parametric plot of repair data on a sample of systems. The plot is illustrated with transmission repair data from cars on a preproduction road test. © 1998 John Wiley & Sons, Ltd.     

LMR—1型掩模缺陷检查激光修整仪

本文介绍了科学院光电技术研究所研制的我国第一代掩模缺陷(检查)激光修整仪。介绍了掩模缺陷的产生及其修补方法,国外主要激光修补仪产品,掩模缺陷激光修整原理和实验研究,着重介绍了LMR-1型掩模缺陷(检查)激光修整仪的结构原理和主要单元,系统功能和主要技术指标及仪器操作流程。该仪器可用于检查和修整2～6in(50～152mm)掩模版上的尺寸2～60μm的缺陷。     

A novel grey‐based feature ranking method for feature subset selection

Abstract

In this paper, a novel grey‐based feature ranking method for feature subset selection is proposed. The classification effectiveness of each attribute of a specific classification problem is proposed and then each attribute can be ranked. Features with higher classification effectiveness are more important and relevant and thus considered as the final feature subset for pattern classification. Experiments performed on various application domains are reported to demonstrate the performance of the proposed approach. The proposed approach yields better performance than other existing feature subset selection methods and is helpful for improving the classification accuracy in pattern classification.     

Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.