Automated sampling decision scheme for the AVM system

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic virtual metrology based intelligent sampling decision (ISD) scheme had been previously developed for reducing the sampling rate and sustaining the virtual metrology (VM) accuracy. However, the desired sampling rate of the ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot adaptively increase the default sampling rate in the ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the default sampling rate in ISD, which may result in unnecessary waste. Accordingly, this paper proposes an automated sampling decision (ASD) scheme to adaptively and automatically modify the sampling rate online and in real time for continuous improvement. The ASD scheme can monitor the VM accuracy online as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the ASD scheme can automatically reduce the sampling rate while the VM accuracy performs well.     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.     

基于本体案例匹配的扰动作业车间智能调度辅助决策

为了快速响应扰动事件,恢复生产稳定,创新性地提出了多类型扰动事件本体与车间生产调度案例本体智能匹配技术。通过引入本体技术（ontology technology）,将以往生产动态调度的成功案例进行规范化和标准化处理,构建动态调度本体化的成功案例库和案例库自我完善的学习机制。结合案例推理（case-based reasoning,CBR）技术及相似度理论,将新扰动的生产车间调度问题与案例库的成功案例进行匹配,依据相似度所在阈值区间,实现扰动车间智能调度辅助决策,从而达到提高决策过程时效性,缩短决策时间的目的。通过仿真结果表明：与传统CBR及混合驱动调度策略相比,在本体-CBR的方法下比传统CBR的案例检索精度大约提高了6%,比混合驱动调度策略相比,在决策时间上要快25 min,故有效地提高了案例检索的精度与响应时间。     

基于VMD和改进多分类马田系统的滚动轴承故障智能诊断

为了有效提取滚动轴承的故障信号,选择合适的智能分类器识别故障状态,提出基于变分模态分解及多重马氏距离法的多分类马田系统的故障智能诊断系统。通过变分模态分解将振动信号分解为多个本征模函数并提取相关特征;并采用了多重马氏距离法的马田系统,以特征子集代替特征参与分类器的构建,以解决特征参数众多的问题;通过正交表和信噪比,筛选出各状态的敏感模态分量,并提出多分类马田系统,用于多类故障智能识别;将其应用于滚动轴承故障数据中,验证算法的有效性,并与其他算法对比分析。结果表明,基于变分模态分解及改进的多分类马田系统算法能简化诊断系统、训练耗时少,识别准确率高,是一种更为有效的故障智能诊断方法。     

Design of exponential control charts based on average time to signal using a sequential sampling scheme

Exponential charts based on time-between-events (TBE) data are widely investigated and applied in various fields. The average time to signal (ATS) is used instead of the average run length to evaluate the performance of TBE charts, since the ATS involves both the number and the time of samples inspected until a signal occurs. An ATS-unbiased exponential control chart is proposed when the in-control parameter is known. Considering the need in practice to start monitoring a production process as soon as possible, a sequential sampling scheme is adopted and the in-control parameter is estimated by an unbiased and consistent estimator. Some specific guidelines to stop updating control limits are obtained from the relationship between the phase I sample size and the actual false alarm rate. Finally, two real examples are given to illustrate the implementation and efficiency of the proposed method.     

国有企业丰田生产方式本土化标准研究

以“准时生产JIT”和“自动化”作为两大支柱的丰田生产方式(TPS)凭借其完整、超常规、最具革命性的全新生产方式被称为“改变世界的机器”.精益生产、持续改善等专门用语出现在全球制造业并成为争相学习、效仿的对象.国有企业在TPS本土化的道路上由于缺乏对其本质的理解和研究,照搬全抄、形式主义的学习方式只能步履维艰.文章以史为鉴,在吸取丰田经验教训的基础上,提出国有企业学习TPS的标准与方法,为TPS本土化提供依据和方向.     

A bill of materials-based approach for end-of-life decision making in design for the environment

Re-use, recycling or remanufacturing of products and components are good alternatives for reducing the environmental problems resulting from the huge amounts of waste currently arriving at landfills. A new approach is proposed in this paper for enhancing these alternatives from the earliest stages of product design. Given the product structure (obtained from its bill of materials (BOM)) and the joining and geometrical relationships among the components (obtained from the three-dimensional, computer-aided design representation), a model is proposed that will determine the EOL (EOL) strategy, i.e. the depth of disassembly inside the structure and the final end (re-use, recycle, remanufacture or disposal) for each disassembled part leading to the highest profit. A scatter search (SS) metaheuristic is used to determine the disassembly cost at each level of the BOM. The model presents a number of major improvements with respect to previous research. It addresses the problems of simultaneously determining both the best EOL strategy and the disassembly sequence, as well as allowing removal of components not only over the two or three Cartesian axes and affording the possibility of modifying the encountered strategy in a further step so as to fulfil other business criteria (such as disassembly time, resources availability or maximum waste generation rate).     

基于Director的交互式虚拟仿真系统

针对虚拟仿真系统的开发,设计了一套新方案。该方案利用软件Director MX的多种插件及其集成语言Lingo实现虚拟仿真系统的交互式操作,在满足一般性交互操作要求的同时避免使用OpenGL和C语言编程,大大降低了系统开发的复杂度。结合插件Havok研究了碰撞检测原理,根据实际虚拟仿真系统精度要求演变了BSP树分割法。     

A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.     

Intelligent and integrated RFID (Ⅱ-RFID) system for improving traceability in manufacturing

In the wake of globalization,many modern manufacturing companies in Norway have come under intense pressure caused by increased competition,stricter government regulation,and customer demand for higher value at low cost in a short time.Manufacturing companies need traceability,which means a real-time view into thenproduction processes and operations.Radio frequency identification(RFID) technology enables manufacturing companies to gain instant traceability and visibility because it handles manufactured goods,materials and processes transparently.RFID has become an important driver in manufacturing and supply chain activities.However,there is still a challenge in effectively deploying RFID in manufacturing.This paper describes the importance for Norwegian manufacturing companies to implement RFID technology,and shows how the intelligent and integrated RFID(n-RFID) system,which has been developed in the Knowledge Discovery Laboratory of Norwegian University of Science and Technology,provides instant traceability and visibility into manufacturing processes.It supports the Norwegian manufacturing industries survive and thrive in global competition.The future research work will focus on the field of RFID data mining to support decision-making process in manufacturing.     

Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0

Abstract

The main purpose of predictive maintenance (PdM) is to reduce unscheduled downtime and consequently improve productivity and reduce production cost. PdM has been featured as a key theme of Industry 4.0. However, the traditional PdM system was only designed for a single tool; as such, the resources allocation will become extremely complicated when hundreds of tools are working together in a factory. A manageable hierarchy and various health indexes are required for factory-wide equipment maintenance. To solve the problem mentioned above, this paper proposes a factory-wide intelligent predictive maintenance system by applying the so-called cyber-physical agent and advanced manufacturing cloud of Things to fulfill the requirements of Industry 4.0, the baseline predictive maintenance scheme to accomplish the PdM functions, and the newly proposed health index hierarchy to supervise factory-wide equipment maintenance.     

An ordering heuristic to develop the binary decision diagram based on structural importance

Fault tree analysis is often used to assess risks within industrial systems. The technique is commonly used although there are associated limitations in terms of accuracy and efficiency when dealing with large fault tree structures. The most recent approach to aid the analysis of the fault tree diagram is the Binary Decision Diagram (BDD) methodology. To utilise the technique the fault tree structure needs to be converted into the BDD format. Converting the fault tree requires the basic events of the tree to be placed in an ordering. The ordering of the basic events is critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. A number of heuristic approaches have been developed to produce an optimal ordering permutation for a specific tree. These heuristic approaches do not always yield a minimal BDD structure for all trees. This paper looks at a heuristic that is based on the structural importance measure of each basic event. Comparing the resulting size of the BDD with the smallest generated from a set of six alternative ordering heuristics, this new structural heuristic produced a BDD of smaller or equal dimension on 77% of trials.     

The elementary flux modes of a manufacturing system: a novel approach to explore the relationship of network structure and function

Elementary flux modes (EFMs) are a concept from Systems Biology, where they serve as an indicator of component relevance in metabolic networks. An elementary flux mode is a functionally relevant, non-decomposable path through a given network. In this paper, we apply elementary flux mode analysis to manufacturing systems, with the aim of using the number of EFMs as a predictor for resource significance in the manufacturing system. For this, we formulate a network representation of a manufacturing process, which allows us to define the manufacturing equivalent of a stoichiometric matrix to draw an analogy between metabolic and manufacturing systems. This, in turn, allows the computation of EFMs, which we conduct in a case-study for a real manufacturing system. We further show that the change of EFMs under resource breakdown is a good indicator of the average order lateness in the manufacturing system. In this way, EFMs provide insight into the relationship of network structure and function in manufacturing.     

基于DSP的智能小车路径跟随系统设计

在车辆主动避撞技术中,路径跟随是一项必不可少的技术．以TMS320F28335 DSP为核心控制模块,以多传感器系统为路径识别模块,搭建了10∶1的主动避撞智能小车模型．通过获取实时路径信息和小车的状态信息得到前方道路的曲率．根据分段式控制方法,使小车能够自动根据路径的曲率调整预瞄距离和行驶速度．通过建立合适的预瞄跟随驾驶员模型,使小车能够跟随道路行驶．通过PWM波驱动舵机和电机,以及PID控制,实验结果表明,小车能够按照规划的路径稳定行驶,且跟随误差小．这证明所建立的预瞄跟随驾驶员模型是正确的,且能实现路径跟随的功能．     

RZZY6000型装药机的常见故障分析与对策

文章介绍了RZZY6000型装药机的结构及工作原理,并针对灌装机过载、热合膜的安装与输送、药量的稳定性、药卷的封口质量、热合膜切刀故障、药卷破损等几个方面进行了分析与改进,有效地降低了设备的故障率,并使产能稳定在2t／h以上,装药精度控制在±40g以内,提高了设备的使用效率。实践表明该装药机是一种高效的塑膜装药机。     

Alternative production strategies based on the comparison of additive and traditional manufacturing technologies

Additive manufacturing technology has been evolving for several years. New material options, better processing speeds and greater autonomy are some of the characteristics of this technology that are still under research. However, in its current state, many commercially available 3D printers are competing with traditional manufacturing techniques in the fabrication of end-use products. In this paper, different additive manufacturing technologies are compared with injection moulding in a real-world case study. The comparison is conducted in terms of lead time and total production cost. From the case under study, it becomes obvious that none of the additive manufacturing technologies examined is yet able to practically replace injection moulding for medium- and high production volumes. However, when considering low-volume production, both rapid tooling and additive manufacturing may offer an alternative that could result into shorter lead times and decreased total production costs. In addition, the introduction of Additive Manufacturing in a producer’s production portfolio can increase flexibility, reduce warehousing costs and assist the company towards the adoption of a mass customisation business strategy.     

An efficient artificial compressibility (AC) scheme based on the characteristic based split (CBS) method for incompressible flows

In this paper, an artificial compressibility scheme using the finite element method is introduced. 2002 Zienkiewicz Silver Medal and Prize winning paper. The multi‐purpose CBS scheme is implemented in its fully explicit form to solve incompressible fluid dynamics problems. It is important to note that the scheme developed here includes split and velocity correction. The proposed method takes advantage of good features from both velocity correction and standard artificial compressibility schemes. Unlike many other artificial compressibility schemes, the proposed one works on a variety of grids and gives results for a wide range of Reynold's numbers. The paper presents some bench mark two‐ and three‐dimensional steady and unsteady incompressible flow solutions obtained from the proposed scheme. Copyright © 2003 John Wiley & Sons, Ltd.     

基于粒子系统的空气污染扩散模拟研究

通过基于粒子系统构建虚拟地理环境的方法进行了空气污染扩散的动态模拟研究.粒子系统将动态、模糊的污染物定义为微观粒子的集合,根据随机过程理论对污染气流扩散进行约束,从而实现在客观上对空气污染扩散的动态性和随机性的有效描述.该研究方法从粒子发射域、粒子运动、粒子绘制等关键环节,在满足视觉效果的前提下进行效率优化,并以珠江三角洲空气污染为案例,建立了虚拟地理环境原型系统,开展了空气污染动态扩散模拟,进行了实时交互查询与分析.实验结果证明,上述的方案能够达到模拟效果的逼真性和交互操作的实时性.     

A new model to optimize the human reliability based on CREAM and group decision making

The Cognitive Reliability and Error Analysis Method (CREAM) represents one of the second-generation approaches to human reliability assessment, taking into account the influence of environmental conditions on human error probability (HEP). In the context of CREAM, the Common Performance Conditions (CPCs) influence error probabilities. Since not all CPCs have equal impacts, this study employs the Bayesian Best Worst Method (BWM), a novel approach in group decision-making, to assign weights to these factors. Subsequently, two techniques based on basic CREAM are proposed. The current control mode is determined in the first technique according to the experts' opinions. Then the probability of human error is calculated based on the amount of control. It is possible to provide solutions for improving control mode, based on obtained results. Therefore, in this study, the second method has been used to make suggestions to enhance human reliability. For this purpose, in the second technique, an optimization problem is formulated to select the best applicable programs for managers to enhance human reliability. The proposed bi-objective model tries to increase the reliability of human resources by reducing human error and costs. The proposed bi-objective model seeks to bolster the reliability of human resources by concurrently minimizing HEP and associated costs. The efficiency of the presented methods is verified through a case study in the control room of the cement factory. The results of the first technique reveal an opportunistic control mode with a corresponding HEP of 0.0198. On the other hand, the outcomes of our proposed model underscore the greater impact of improving CPC levels in reducing the probability of human error. Ultimately, the practical programs derived from our mathematical model provide decision-makers with valuable insights to reduce the probability of human error to a mere 0.000172 through the transition from opportunistic to strategic control.