支持快速扩散制造的制造执行系统研究

结合武器装备快速变批量生产的需求,对武器装备快速扩散制造的概念和支撑理论进行了介绍;针对快速扩散制造过程中若干核心问题,提出了支持快速扩散制造的制造执行系统。介绍了制造执行系统实施的三个关键技术:支持快速扩散制造的生产计划与动态调度技术;基于资源平台的物料全生命周期管理及应用集成技术;基于工作流的制造过程自动导航与驱动技术。最后对制造执行系统的总体体系结构和功能模型展开探讨和研究。该系统在航天制造企业应用的效果显著,证明了该系统整体方案的可行性和有效性。     

快速扩散制造中的工作流管理技术

针对武器装备快速扩散制造的效率问题,以军工产品的扩散制造为实例背景,提出一种支持快速扩散制造的工作流管理技术,包括工作流建模技术、工作流运行控制技术和工作流系统实现方法,采用Petri网建模方法和基于Web的工作流引擎技术。阐明扩散制造中工作流管理系统的体系结构。结果证明其在提高军工产品制造效率方面效果良好。     

基于网络化制造工艺的设计技术以及在机床装备制造应用

随着我国科技的不断发展,信息技术也得到长足发展,且其发展促进了制造业发展的网络化、数字化、全球化,使得制造企业能充分利用外部先进制造资源,提高企业快速响应制造的能力,促进企业的发展壮大。其中网络化制造强调各企业间、我国社会资源乃至全球范围内的资源共享,企业加以整合利用以此提高制造工艺的设计技术及创新能力。另外,网络化能更好的构建企业间制造资源共享的平台,并将该平台转化成能为企业服务的重要工艺技术设计工具。合理的从网络化平台上获取可用的资源并加以整合利用,提高制造工艺的设计技术并将其合理的利用到机床装备制造中。本文基于国内外网络制造研究成果,结合相关知识浅显谈谈基于网络化制造工艺的设计技术以及在机床装备制造中的应用。     

面向云制造模式的制造资源协作支撑平台分析

随着产品复杂度的提高和更短的生产周期要求,单个制造企业资源和能力无法满足要求。应依托第三方制造资源协作平台,实现各企业分布式制造资源和服务的共享和协作。研究面向云制造模式的制造资源协作平台的架构、功能和运作机制,为构建制造云提供有效的基础设施解决方案。     

制造网格中基于SLA的资源管理模型研究

为确保制造网格平台提供的制造资源及服务能够达到使用者的功能与质量要求，建立了基于SLA的资源管理模型，并对模型中的资源调度算法进行了深入研究。该模型以制造资源的服务质量(QoS)属性为评价标准，以服务水平协议(SLA)为约束条件，通过资源调度方法和策略为消费者提供有质量保证的制造服务。最后，以快速成型制造为应用，验证了模型的实用性及资源调度算法的可行性。     

云制造环境下基于双层规划的资源优化配置模型

为了解决云制造环境下制造资源的优化配置问题,综合考虑需求与服务双方以及云平台运营方的利益,提出了一种基于双层规划的资源优化配置模型。该模型以前景理论结合多约束多属性评价体系求解出的供需双方满意度作为上层规划的优化目标;以云平台资源利用率最大化为下层规划的优化目标;通过双层规划并采用改进的i-NSGA-II-JG算法对多目标制造资源配置问题进行求解。最后,通过算例仿真实验证明了该模型的可行性和优越性。     

基于混沌遗传算法的集团分布式制造工序资源配置

集团分布式制造企业往往存在着地理位置不集中、制造资源和制造能力不均衡、资源闲置与资源短缺并存等问题,针对集团制造企业在制造资源配置过程中多主体、多任务、多资源、多工序以及协同性的特点,从集团公司总体利益及下属企业个体利益多角度出发,综合考虑生产成本、加工资源、加工效率等多个因素,建立集团分布式制造资源配置优化模型,并采用基于Logistic混沌改进的遗传算法求解该模型的Pareto最优解.最后对国内某建材装备集团的制造资源配置过程进行算例分析,以验证模型和算法的有效性.     

基于MAS的产品设计制造应用服务提供系统

提出了一个基于多Agent系统的产品设计制造应用服务提供系统,通过将分布的制造资源和应用系统封装为网络上的自主Agent为连入网络的用户提供应用服务．从而实现资源共享和生产能力的快速扩散。Agent之间的协作和协调为实现集成应用服务环境提供了有效机制。讨论了系统的主要组成、面临的问题和使能技术,并通过一个仿真应用系统进行验证。     

基于知识融合的扩散制造工艺调整方法

在快速扩散制造模式下,为了使发放到扩散企业的工艺数据能够有效地指导扩散件的生产,提出一种基于知识融合的定型工艺调整方法.首先分析扩散制造工艺设计知识内涵,然后基于本体和规则对扩散制造工艺设计知识进行表达,并在此基础上构建扩散制造工艺调整模型,最后给出基于工艺知识元推理的知识融合策略.在某航天企业的实施验证表明,该方法有...     

面向中小企业的云制造应用

云制造是采取包括云计算在内的当代信息技术前沿理念,支持制造业在广泛的网络资源环境下,为产品提供高附加值、低成本和全球化制造的服务。云制造为中小企业提供了面向中小企业公共服务平台,能够促进中小企业快速地发展。通过中小企业的发展现状,详细地分析了云制造对中小企业发展产生的作用。     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

网络化制造环境下多维度制造资源建模技术研究*

结合军工制造业资源数据描述规范,提出网络化制造环境下多维度制造资源建模方法。通过应用视图维制造资源刻画各应用系统的资源模型,并利用生命周期维制造资源实现企业制造资源信息统一表达框架的构建。为了支持企业间不同层次的合作需求,将企业内部离散的个体制造资源封装成能够反映企业制造能力的聚合粒度维制造资源。最后为了解决企业资源能力语义表达问题,将语义Web技术引入其中,研究基于本体论的多维度制造资源建模和语义化描述方法。     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

自动制造系统的稳健控制方法的综述

随着科学技术的快速发展,制造自动化在制造工厂已经成为一个主流方向.在过去的几十年中,研究人员已经对自动制造系统的死锁问题做了大量的研究.但是大多数解决方案总是假设分配的资源不会故障.然而,任何一个制造研究者都知道,资源故障来自各种各样的原因,包括工件破损、传感器故障、零件缺失和电器失灵等.显然,一旦资源发生故障,后续加工路径中需要使用这个故障资源的进程将停滞,不能完成其加工生产,直到故障资源被修复.那些不使用故障资源的支路也会被发生停滞的进程所阻塞.最坏的情况就是一个简单的资源故障可能会导致整个系统的崩溃.因此,制造系统中的资源故障问题急需解决.通过分析大量的文献资料,本文对解决死锁和阻塞问题的控制方法做了系统的总结研究.同时,对本文提出的稳健无死锁控制策略以及亟待开展的研究工作做了详细的介绍.     

基于区块链的云制造系统内可信资源调度方案

针对目前云制造系统中存在的各参与主体间信任问题以及资源调度效率问题,研究了将区块链技术应用于云制造系统中.首先,阐述了区块链技术应用于云制造系统的意义,提出了一种基于区块链技术的云制造系统;其次,设计了基于智能合约的制造资源调度方式,构建制造成本最小、时间最短、合格率最高的资源调度模型并用差分进化算法进行求解;最后,进行实验仿真.结果表明,基于区块链技术的智能合约内进行资源调度方法在保证了系统内各参与主体间相互信任的同时,有效地提高了云制造系统的资源调度效率和资源调度方案的优越性.     

Research on recommendation and interaction strategies based on resource similarity in the manufacturing ecosystem

Nowadays, the thriving of the manufacturing ecosystems (ME) driven by the increasing competition in industrial markets, the ubiquitous implementation of intelligent systems, and the more frequent collaboration among manufacturing enterprises. During the practice of the system upgrade, it is increasingly noted that the redundancy of manufacturing resources and the inefficiency in resource configuration are the major obstacles to achieving satisfying value-creation within ME, which also result in cumbersome decision making (DM) in the problems of requirement-service configuration (RSC) and collaborative production. To address these issues, the research on resource recommendation and interaction is carried out. Firstly, the resource similarity models for autonomous resource filtering brace the whole DM mechanism in RSC and push the most suitable resource to the host automatically. Then, the interaction model provides a self-organized production mode without human intervention. The blindness, lag, and unfairness in the manual communication is eliminated by the Machine to Machine (M2M) interaction and automatic coordination. Besides, an NLP-based machine learning algorithm is introduced for quantifying semantic distance and measuring the differences between orders. Composed by these models, a total solution named Industry-Chat (I-Chat) emerges. With the help of that, production resources can be scheduled and managed autonomously and the order-based production processes could be promoted seamlessly. Thus, an improved industrial ecosystem with automatic DM and self-organization for future intelligent manufacturing is realized. The practicability of the research is verified by a case study. The results show that the production cost is reduced by 12%, the resource utilization rate is improved and its economic value is demonstrated.     

A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system

The layout design problem is one of the most important issues for manufacturing system design and control. A revised electromagnetism-like mechanism (REM) is proposed in this paper for the layout design of reconfigurable manufacturing systems utilizing automated guided vehicle. First, the formal model considering both loaded and empty flows is given. Then the REM is developed to solve the proposed model. In the REM, particles are encoded discretely. The charge of a particle is calculated according the total material handling cost of the particle. In the local search procedure, variable neighbourhood search strategy based on Hamming distance is adopted. In the moving procedure, the particles are moved according to the ordering of each element. To verify the effect of the proposed method, several computation cases are carried out. The computation results show that the proposed method is able to get optimal solutions for small scale problems and near optimal solutions within limited computation time for large scale problems. This indicates that the proposed method is effective and efficient.     

团购模式下云制造服务资源组合优化模型与算法

在云制造服务环境中,为了进一步降低需求者的服务成本,提出了一种团购模式下云制造服务资源组合优化模型与算法。在云制造平台发展的初期阶段,以服务需求者的视角分析云制造服务资源组合优化管理问题,通过团购模式研究了资源组合优化模型与算法,模型中考虑团购定价、团购信任度等关键影响因素,对云制造资源组合优化进行综合决策;设计改进的遗传算法进行模型求解,进一步对团购模式下云制造服务资源组合模型进行仿真分析。通过不同规模问题的仿真实验验证了模型与算法的有效性和可行性,仿真结果表明,在团购规模逐渐增大的情况下,团购模式比个体模式更具有成本优势。