FMS故障诊断的模糊行为Petri网研究

根据ＦＭＳ故障诊断推理中知识的模糊性，给出了模糊行为Ｐｅｔｒｉ网（ＦＢＰＮ）的定义，研究了用模糊行为Ｐｅｔｒｉ网表示模糊产生式规则的方法，提出了一种模糊反向推理机制，给出了算法的实现。最后以ＢＦＥＧ－ＦＭＳ的刀库换刀故障为例，证明了该方法的可行性和有效性。     

面向柔性制造系统的非传统工件安装方法及其应用技术

回顾了国内外关于面向柔性制造系统的工件安装方法及其研究动态，介绍了无基准零件密封技术（Ｒｅｆｅｒｅｎｃｅ Ｆｒｅｅ Ｐａｒｔ Ｅｎｃａｐｓｕｌａｔｉｏｎ－ＲＦＰＥ）的基本原理、组成和工艺过程，指出了该项技术用于生产实际尚存在的不足，提出并研制了快速寻位与状态记忆（Ｒａｐｉｄ Ｌｏｃａｔｉｎｇ ａｎｄ Ｓｔａｔｅ Ｍｏｍｏｒｙ－ＲＬ＆Ｍ）通用夹具系统，以促进ＲＬ＆ＳＭ安装技术在敏捷制造中的应用。     

FMES运行监视与故障诊断系统的研究与设计

以长春柔性制造实验系统为研究对象，研究了柔性制造系统的运行监视与故障诊断技术，研制了基于实时动画和人工智能的ＦＭＥＳ运行监视和故障诊断系统。介绍了ＦＭＥＳ－ＭＤＳ的总体结构讨论了ＦＭＥＳ－ＭＤＳ中的信息检测方法和故障诊断技术。     

哈尔滨工业大学FMS研究中心

哈尔滨工业大学ＦＭＳ研究中心哈尔滨工业大学ＦＭＳ研究中心是为适应先进制造技术研究的需要于１９８８年成立的一个跨学科、跨系的科研实体，是国防科工委ＦＭＳ检测监控与质量保证网点的牵头单位，主要从事ＦＭＳ和ＣＭＳ领域高新技术的研究与应用，目前共有研究人员３...     

计算机辅助构造PRS工具的实现

通过对企业活动之间基本联系方式的分析以及过程规则集基本类型研究，借助功能－功能矩阵ＦＦＭ（Ｆｕｎｃｔｉｏｎ－ＦｕｎｃｔｉｏｎＭａｒｔｒｉｘ）及其操作算法，建立了计算机辅助构造过程规则集（ＰｒｏｃｅｄｕｒａｌＲｕｌｅＳｅｔ，ＰＲＳ）工具－ＣＡＢＰＲＳ，并在其基础上将ＰＲＳ自动转化成０－０表达和行为关系图（ＢｅｈａｖｉｏｒＲｅｌａｔｉｏｎｓｈｉｐＧｒａｐｈ，ＢＲＧ）以验证ＰＲＳ的一致性和完整性，以及实     

一种多品种多加工中心FMS中的批调度问题研究

针对一种比较典型的多品种,多加工中心柔性加工企业（ＦＭＳ）的生产调度问题建立了动态模型,并给出了一种多级递阶算法,利用这种算法,可以在较少的计算量基础上获得近优解。     

优化交货可靠性的装配作业车间分批调度

装配作业车间生产多规格小批量装配型产品,通常具有差异BOM结构,主要由客制零件组成,但同规格产品仍然包含一定比例的可互用零件,每类零件具有一定批量。此类车间分批调度需要最优化零件类的批次分割和子批加工序列。考虑客户对高交货可靠性的需求,构建了基于最优化交货可靠性为决策优化目标的分批调度数学模型,建立了基于遗传算法和优先级分派规则的混合求解算法。最后通过仿真实验分析和验证了所提方法的有效性。     

FMS的应用现状与改进措施

从ＦＭＳ的应用现状入手，结合ＦＭＳ的原理和其他先进制造方法和制造模式，分析了ＦＭＳ在应用中的误区，并就此提出了改进措施。     

FMS控制系统统一仿真模型的建立及实现

针对目前ＦＭＳ存在的问题，在现有ＦＭＳ仿真方法基础上提出了一种面向对象的集成化、开放统一框架模型，该框架模型系统集成了ＦＭＳ的资源描述、仿真、调度、评价、控制等子系统，利用层次建模、面向对象以及人工智能的思想方法，把各功能模型有机结合起来，应用于ＦＭＳ控制系统的仿真、设计及实际运行控制。     

Fe—Mn和Fe—Mn—Si粉末混合物的机械合金化

对Ｆｅ－２４Ｍｎ，Ｆｅ－２４Ｍｎ－６Ｓｉ成分的粉末混合物进行了机械球磨，并对不同时间的球磨样品进行了Ｘ射线衍射（ＸＲＤ）和Ｍｏ¨ｓｂａｕｅｒ谱测量。结果表明，球磨使得Ｆｅ、Ｍｎ、Ｓｉ在原子尺度上发生了混合，形成了顺磁性、面心立方结构的Ｆｅ－２４Ｍｎ或Ｆｅ－２４Ｍｎ－６Ｓｉ纳米晶合金，这是Ｆｅ、Ｍｎ或Ｓｉ原子由颗粒表面到体内扩散的结果。球磨６７ｈ以后结构未发生变化，表明形成的是一种热力学亚稳结构，这个结果与Ｆｅ－Ｍｎ和Ｆｅ－Ｍｎ－Ｓｉ合金在室温下的相图结构明显不同。     

Scheduling of machines and automated guided vehicles in FMS using differential evolution

This paper addresses the problem of simultaneous scheduling of machines and two identical automated guided vehicles (AGVs) in a flexible manufacturing system (FMS). For solving this problem, a new meta-heuristic differential evolution (DE) algorithm is proposed. The problem consists of two interrelated problems, scheduling of machines and scheduling of AGVs. A simultaneous scheduling of these, in order to minimise the makespan will result in a FMS being able to complete all the jobs assigned to it at the earliest time possible, thus saving resources. An increase in the performance of the FMS under consideration would be expected as a result of making the scheduling of AGVs as an integral part of the overall scheduling activity. The algorithm is tested by using problems generated by various researchers and the makespan obtained by the algorithm is compared with that obtained by other researchers and analysed.     

Managing flexible manufacturing technology: Must parts in an FMS always move in a job-shop manner?

The literature typically assumes that an FMS consists of a number of machines of different processing capability. Furthermore, in view of the inherent flexibility of an FMS, many researchers believe that 'flexible' implies that a wide variety of parts can be, and should be, processed simultaneously in an FMS. To do otherwise will inevitably mean some machines are idle and hence the FMS may become a tremendous waste. This paper is an attempt to study this issue. Unlike a job shop, which is typically an eclectic collection of machines of different process capabilities, most flexible manufacturing systems consist of only one or two types of versatile programmable machines. We contend that the FMS machines are analogous to the multi-skilled workers in lean production. In lean production, workers work in teams. It is common for each team to work on one job at a time, from start to finish. Jobs are not passed around from worker to worker. Likewise, machines in an FMS can be assigned into 'teams', where each 'team' is dedicated to process a single part type (or family) at a time. Parts do not necessarily have to move from machine to machine in a job shop manner.     

Control policy simulation based on machine age in a failure prone one-machine,one-product manufacturing system

This paper deals with the production planning problem of a flexible manufacturing system (FMS). This problem is within the class of problems which are usually faced during the life cycle of FMS. There are basically two approaches used to solve this problem: analytical and simulation. The objective of this paper is to develop a methodology for determine the control policy for a given FMS by using the combination of both analytical and simulation approaches. The production rate control of a one-machine one-product system is presented to illustrate our approach. The contribution of the analytical part of the proposed model consists of generating a near-optimal controller based on the concept of an age-dependent hedging point policy. This sub-optimal policy is used as input of the simulation part and the improvement of operating conditions (control policies) achieved with a constant demand rate. The proposed model includes also a stochastic demand and lot sizes in production which consists of similar products batches with bulk arrivals. The robustness of the controller against both demand rates and production types has been validated.     

A note on the problem of scheduling a flexible manufacturing system for Just-in-time customers

In supplying just-in-time customers, one common industry practice is for the vendor to set up a warehouse near the point of demand. We assume that the plant is a flexible manufacturing system (FMS) where the changeover times between parts of the same family axe. negligible, but not between parts of different families. Thus the FMS must be configured to produce one family of parts at a time. For each part type, we assume that the vendor makes deliveries to the warehouse at fixed intervals. We examined two related issues: the run length for each part family and the number of deliveries to the warehouse in each cycle. We first showed that it is not necessary for the vendor to produce for each delivery, nor is it desirable to complete the entire production lot before delivery. We then investigated the FMS loading problem for two or more part-families. We developed the expressions for the optimal run length and delivery frequency, (1) when loading the part types sequentially and (2) when loading the part types simultaneously. The resulting models can be shown to be consistent with the classical EOQ model.     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

A scheduling approach for a flexible manufacturing system

This paper discusses the scheduling problem of a particular flexible manufacturing system (FMS). The two main components of the FMS are a CNC turret lathe and a CNC machining centre. In the system a wide range of different jobs has to be processed. Each job consists of one or more processing operations on one or both machines. Important characteristics of the scheduling problem are sequence-dependent change-over times (on the turret lathe) and transfer times (on both machines and between the machines). The change-over times are caused by the need to exchange tools in the turret when a new part is going to be processed. The transfer times reflect the time needed to perform manual transportation and clamping activities between two subsequent processing (machining) operations of a part. In this paper a branch and bound algorithm is described based on an active schedule strategy. Solutions are compared to results obtained by a simple dispatching rule     

遗传算法的双目标柔性作业车间调度研究

研究了FMS环境下先进制造车间路径柔性的优化调度问题.同时考虑现代生产准时制的要求,建立了柔性作业车间调度问题的双目标数学优化模型,并给出了求解模型的遗传算法的具体实现过程;针对模型的特殊性,提出了染色体两层编码结构,将AOV网络图应用到解码和适应度函数的计算中,通过一个调度实例进行验证,给出了相应的选择、交叉、变异操作设计方案.     

A new generic timed Petri net model for design and performance analysis of a dual kanban FMS

A new generic black token timed Petri net (PN) model of FMS based on the process plan is presented. The model allows modelling the FMS configuration, determining the optimal work in process, the number of kanban cards and the required capacity for a given maximum production rate. The lead-time and utilization of each station can be determined. Each station can have multiple identical servers. The approach is based on modelling the FMS as an event graph with part circuits, kanban circuits and sequencing circuits. The part circuits contain tokens that represent parts with attached kanban cards. In the kanban circuits there will be places containing tokens that represent kanban Move Cards (MC) and Production Cards (PC). Circuits called sequencing circuits will model the sequencing of the parts on each station. The tokens in these sequencing circuits represent the number of identical servers. The optimal solution of the model is obtained in three steps. The first step is the initial marking of the PN. The second step involves determination of the invariants of the PN. The third is solving a linear programming model.     

FMS Scheduling Simulation Based on an Evolution Algorithm

A FMS（flexible manufacturing system) scheduling algorithm based on an evolution algorithm (EA) is developed by intensively analyzing and researching the scheduling method in this paper.Many factors related to FMS scheduling are considered sufficiently.New explanations for a common kind of the encoding model are given.The rationality of encoding model is ensured by designing a set of new encoding methods,while the simulation experiment is performed.The results show that a FMS scheduling optimum problem with multi-constraint conditions can be effectively solved by a FMS scheduling simulation model based on EA.Comparing this method with others,this algorithm has the advantage of good stability and quick convergence.     

A comparison of tool management strategies and part selection rules for a flexible manufacturing system

An important element in the successful operation of flexible manufacturing systems (FMS) is the management of the tooling component. This paper reports on one aspect of tool management for FMS operations. Four tool allocation and scheduling strategies are compared in the presence of three part selection rules through a simulation study of a five-machine FMS with an automated tool handling system. The tool allocation strategies are similar to those used in industry while the part selection rules are synthesized from the literature on FMS scheduling under tooling constraints. The use of different tooling strategies produces significantly different outcomes in FMS performance.