敏捷制造中伙伴选择的建模方法

随着信息技术的发展和全球化市场竞争环境的形成,制造业的经营观念发生了深刻的改变。敏捷制造(Agile Manufacturing,AM)作为提高企业群体竞争能力的全新制造业生产组织模式,被认为是21世纪制造业采用的主导模式。对AM的出现和发展以及相关技术进行综述后主要讨论了在敏捷虚拟企业(Agile Virtual Enterprises,AVE)组建过程中,支持敏捷虚拟企业(AVE)盟主进行合作伙伴选择与实现的模糊层次分析法。     

敏捷虚拟企业中基于CORBA的多级代理建模

敏捷制造虚拟企业（Agile Virtual Enterprise,AVE）作为一种新型的制造模式正在逐渐兴起。将CORBA技术应用到制造业中，根据制造业自身的特点，提出了分布式制造系统的多级代理模型。在该模型中，代理表示虚拟组织中某一功能部件或任务执行点；工作流表示代理之间相互依赖的关系；事件触发机制实现代理之间的互操作。由此，虚拟组织的运作和产品的协同开发就有了可靠的实现依据 。     

敏捷制造下车间重构中的制造资源选择

敏捷制造被认为是21世纪的制造模式．实现敏 捷制造的组织形式是敏捷虚拟企业（AVE）,它根据产品开发的需要动态重构．动态重构敏 捷虚拟企业中的制造资源选择问题是对实现敏捷制造的挑战．本文给出了一个资源选择问题 的整数规划模型,其目标是使费用最小．利用任务之间的先后顺序的特点,我们将整数规划 模型转换为图论模型．基于图论模型,我们给出了一个求最优解的有效算法．其计算复杂性 是多项式有界的．     

基于FP算法的AVE局部模型划分

局部模型的划分是简化敏捷虚拟企业（Agile Virtual Enterprise，AVE）决策问题的重要方法，划分结果可能直接影响到敏捷虚拟企业的建立方式、AVE模型的优化以及合作伙伴的选择等。为了很好地完成AVE局部模型的划分问题，提出一种基于多层反馈神经网络的局部模型划分方法。该方法采用三层网络结构，各层完成指定的任务，第一层完成各输入向量的相似度是否大于给定值的判别；第二层则将相似度大于给定值的输入向量映射到同一类值，从而完成预分类；再利用反馈网络的反馈作用完成最后的分类。其结构清晰、分类灵活、学习的复杂性最低且网络的性能良好。     

基于FP算法的AVE局部模型划分

局部模型的划分是简化敏捷虚拟企业(Agile Virtual Enterprise,AVE)决策问题的重要方法,划分结果可能直接影响到敏捷虚拟企业的建立方式、AVE模型的优化以及合作伙伴的选择等。为了很好地完成AVE局部模型的划分问题,提出一种基于多层反馈神经网络的局部模型划分方法。该方法采用三层网络结构,各层完成指定的任务,第一层完成各输入向量的相似度是否大于给定值的判别;第二层则将相似度大于给定值的输入向量映射到同一类值,从而完成预分类;再利用反馈网络的反馈作用完成最后的分类。其结构清晰、分类灵活、学习的复杂性最低且网络的性能良好。     

基于Web Services和MA的虚拟企业架构

虚拟企业是企业间的一种动态联盟,它面向全球范围的企业资源,通过构建VE联盟之间的快速重组,实现联盟企业之间的敏捷化和柔性化.因此它是未来企业生产经营和市场竞争的发展模式.随着市场竞争的日趋激烈和信息技术的高速发展,对虚拟企业的发展提出了更高的要求.为了解决虚拟企业耦合度过高、缺乏柔性等问题,在分析了Web服务和多代理技术在虚拟企业中应用的必要性和可行性基础上,提出了基于Web服务和多代理的虚拟企业架构,并给出了一个改进模型.     

基于VESA的虚拟企业构建方案的研究

虚拟企业是二十一世纪制造企业的主要模式,它面向全球范围的企业资源,通过构建虚拟企业的联盟之间重组,实现了联盟企业间的柔性化。如何迅速构建虚拟企业是敏捷制造企业要解决的首要问题。Internet的飞速发展,为企业利用互联网实现虚拟企业的构建提供了基础。文章提出了采用ISP形式的虚拟企业构建方案——虚拟企业服务代理商（Virtual Enterprise Service Agent,VESA）,并探讨了VESA的应用构建框架和处理业务。     

异地资源配置系统关键技术研究

自从 １９９１年美国里海大学和 １３ 家大公司组成的联合研究小组向美国国会提交的《２１世纪制造业发展战略》的著名研究报告中首次提到敏捷制造（ＡＭ）的概念以来,敏捷制造及其系统实现方法引起了各国的广泛关注．并迅速成为包括机械制造、计算机、自动控制和管理等学科的研究热门。 敏捷制造强调通过虚拟企业的形式,利用异地资源来制造市场所需产品。因此,合作企业的优化选择和异地资源的合理配置是虚拟企业构造和运作过程中的重要活动,而异地加工任务的描述、企业信息资源的提取和建模、特征映射的实现、加工任务与企业的匹配以及…     

基于随机层次分析法的虚拟企业风险评价

分析虚拟企业风险因素的层次结构以及量化评价中的不确定性,设计了随机层次分析法(SAHP)来对其风险进行评价.在随机层次分析法中,将专家咨询法过程中的不确定性描述为随机变量,得到随机判断矩阵.进而应用随机模拟方法确定随机判断矩阵中元素的估计值.运用随机层次分析法对某虚拟企业三个备选组建方案的风险评价进行了实证分析,阐明该方法对于多指标、不确定性的最优方案选择问题是一种科学、可行的方法.     

敏捷供需链的运作机理研究

基于因果关系图、层次分析法（AHP）、网络分析法（ANP）等理论和方法提出了敏捷供需链运作机理的研究框架，分别从辨析关键因素、分析因果关系、评价相互影响以及结果分析等方面进行了探索，并通过实例对具体的方法进行了详细说明．     

Additive manufacturing in the spare parts supply chain

As additive manufacturing (AM) evolves to become a common method of producing final parts, further study of this computer integrated technology is necessary. The purpose of this research is to evaluate the potential impact of additive manufacturing improvements on the configuration of spare parts supply chains. This goal has been accomplished through scenario modeling of a real-life spare parts supply chain in the aeronautics industry. The spare parts supply chain of the F-18 Super Hornet fighter jet was selected as the case study because the air-cooling ducts of the environmental control system are produced using AM technology. In total, four scenarios are investigated that vary the supply chain configurations and additive manufacturing machine specifications. The reference scenario is based on the spare parts supplier's current practice and the possible future decentralization of production and likely improvements in AM technology. Total operating cost, including downtime cost, is used to compare the scenarios. We found that using current AM technology, centralized production is clearly the preferable supply chain configuration in the case example. However, distributed spare parts production becomes practical as AM machines become less capital intensive, more autonomous and offer shorter production cycles. This investigation provides guidance for the development of additive manufacturing machines and their possible deployment in spare parts supply chains. This study contributes to the emerging literature on AM deployment in supply chains with a real-world case setting and scenario model illustrating the cost trade-offs and critical requirements for technology development.     

The status,challenges, and future of additive manufacturing in engineering

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a “positive feedback loop” effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the “print-it-all” paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration.     

Eco-friendly additive manufacturing of metals: Energy efficiency and life cycle analysis

Additive manufacturing (AM) of metal materials has attracted widespread attention and is shifting the conventional manufacturing landscape toward free-form processes. With increasing concerns about global sustainability, eco-consideration is highly encouraged to be integrated into AM processes. This review provides a comprehensive and timely discussion on the life cycle of metal parts fabricated through AM. The energy consumption required for raw metal material extraction and subsequent AM processes is analyzed. The eco-design and energy efficiency of metal AM are evaluated to reveal the role of manufacturing methods, machine subsystems, and post-processing modes in the eco-integration. AM-induced supply chain management, utilization, and recycling of the printed metal structure are also analyzed. Finally, a comprehensive life cycle assessment regarding the environmental, social, and economic impacts of metal AM is also addressed. Future directions of AM are also briefly discussed to provide insight and vision on the emerging field of additive eco-manufacturing.     

Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach

The rapid development of Additive Manufacturing (AM) has been conspicuous and appealing towards manufacturing end-use products and components over the past decade. The continual advancement of AM has brought many advantages such as personalization and customization, reduction of material waste, cutting off the existence of special tooling during fabrication, etc. However, the AM approach has its limitations, such as a lack of knowledge of AM process activities and the progressive industrialization of AM, which makes the design process activities unstable, unpredictable, and have a limited effect. The concept of “design for AM (DFAM)” is increasing, which means we have the opportunity to concentrate almost totally on product functioning. Therefore, the entire design paradigm must be revised to accommodate new production capabilities, geometries, and parameters to avoid molding or machine tooling technology constraints. Few studies have attempted to provide systematic and quantitative knowledge of the relationship between these elements and the feasibility of the design process, making it difficult for designers to assess and control AM industrialization. For this reason, DFAM is needed to reform AM from rapid manufacturing to a mainstream manufacturing method. This paper put forward a framework based on the Fuzzy Bayesian Network (FBN) for DFAM decision-making. Twenty impact factors were encapsulated from experts’ experience and existing literature to investigate the potential adaptability of DFAM. The proposed approach uses expert knowledge and Fuzzy Set Theory (FST) presented with Triangular Fuzzy Numbers (FFN) to perceive the uncertainties. The Bayesian Network (BN) captures the causal relationships and dependencies among the impact components and analyzes the DFAM adaptability for robust probabilistic reasoning. A robot arm claw was used to show the effectiveness of our approach. The results showed that FBN could be used to guide DFAM adaptability in the manufacturing industry.     

A survey on the methods and tools of concurrent new product development and agile manufacturing

Companies in either manufacturing or servicing have to be restructured or re-organized in order to overcome with challenges of the 21st century in which customers are not only satisfied but also delighted. In this competitive environment, organizations should use a flexible, adaptive and responsive paradigm that can be entitled by a unique term: agile manufacturing (AM). An AM system is able to develop a variety of product at low cost and in a short time period. For this, it has some of useful enabling technologies and physical tools. Among these, concurrent engineering (CE) is a systematic approach to the integrated, concurrent design of product and their related processes, including manufacture and support. It is then a useful and beneficial approach to reduce the development time and manufacturing cost, while simultaneously improving the quality of a product in order to better respond to the customer expectations. The aim of this study is to underline the synergistic impact of new product development (NPD) and CE, (which can be called CNPD), and to survey their methods and tools in association with the AM.     

A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

In-situ point cloud fusion for layer-wise monitoring of additive manufacturing

Additive manufacturing (AM) has received an increasing attention in the manufacturing sector, owing to its high-level design freedom and enhanced capability to produce parts with complex geometries. With advances in AM technologies, the role of AM has been shifting from rapid prototyping to viable production-worthy manufacturing of functional parts. However, AM processes are highly inconsistent, and the lack of quality assurance significantly hampers the broader adoption of AM. Most existing techniques for AM online monitoring focus on the detection of conspicuous defects, such as under-fills and cracks. They are limited in their ability to detect layer surface variations induced by miniature process shifts. The objective of this study is to develop a new layer-wise monitoring framework for AM quality assurance based on in-situ point cloud fusion. Specifically, online 3D structured-light scanning is used to capture the surface morphology from each printed layer. The collected point cloud is partitioned, and the morphological patterns in local regions are delineated with a new affinity measure to evaluate the conformity to the reference. A deep cascade model is further introduced to leverage the local affinities for the identification of abnormal patterns on the printed layers. Finally, a statistical control chart is constructed for process monitoring and the identification of miniature shifts. Simulation and real-world case studies using the fused filament fabrication (FFF) process are conducted, and experimental results have demonstrated the effectiveness of the developed framework. It has a great potential to be implemented in diverse AM processes with a wide variety of materials for mission-critical applications.     

A new complicated-knowledge representation approach based on knowledge meshes

This paper presents a new complicated-knowledge representation method for the self-reconfiguration of complex systems such as complex software systems, complex manufacturing systems, and knowledgeable manufacturing systems. Herein, new concepts of a knowledge mesh (KM) and an agent mesh (AM) are proposed along with a new KM-based approach to complicated-knowledge representation. KM is the representation of such complicated macroknowledge as an advanced manufacturing mode, focusing on knowledge about the structure, functions, and information flows of an advanced manufacturing system. The multiple set, KM, and the mapping relationships between both, are then formally defined. The union, intersection, and minus operations on the multiple sets are proposed, and their properties proved. Then, the perfectness of a KM, the redundancy set between the two KMs, and the multiple redundancy set on the redundancy set are defined. Three examples are provided to illustrate the concepts of the KM, multiple set, multiple redundancy set, and logical operations. On the basis of the above, the KM-based inference engine is presented. In logical operations on KMs, each KM is taken as an operand. A new KM obtained by operations on KM multiple sets can be mapped into an AM for automatic reconfiguration of complex software systems. Finally, the combination of two real management modes is exemplified for the effective application of the new KM-based method to the self-reconfiguration of complex systems. It is worth mentioning that KM multiple sets can also be taken as a new formal representation of software systems if their corresponding AMs are the real software systems.