制造单元控制系统的建模

为了使制造单元具有敏捷性,单元控制软件应具有模块性和可重用性,在设计阶段建立的单元控制模型是关键。本文讨论敏捷制造单元控制模型的建立,提出了基于制造资源模型的系统控制模型。用面向对象方法与 Ｐｅｔｒｉ 网相结合的方法建立资源的控制模型。     

Product modularity and assembly systems: An automotive case study

Designing modular products offers a number of advantages. However, modularity may be challenging in terms of production and assembly. In this paper, the influence of product design modularity on assembly systems is investigated. A modular design from the automotive industry is presented and compared with a traditional design. The influence of modularity on the assembly system is discussed including attributes such as production rate, investment cost and product's delivery delay.     

并联夹具机构灵巧性分析

针对汽车行业对制造系统以及包括夹具在内的所有部件的高模块化和灵活性的要求,提出一种具有高自配置性和灵活性的夹具系统。其中,安装在并联机构末端执行器上的夹具具有高刚度、高精度、可重构性,并且可以通过编程使其在工作空间中移动的特点。而并联机构作为自配置夹具系统的支持单元,在工作空间和动态特性方面具有一定的局限性。为了提高并联机构的综合工作性能,在工作空间中,分别以全局运动条件数和全局动态条件数作为运动灵巧性和动态灵巧性的评价标准。以工作空间体积、全局运动条件指标和全局动态条件指标为目标函数,对并联夹具机构进行优化,得到优化后的结构参数。     

网络环境下多学科协同产品建模系统研究

通过对多学科协同设计特点的分析,提出了一种融合结构数据、语义数据和协同管理数据的产品信息模型.采用一种扩展的面向对象方法来表达产品模型,并可根据需要进行多层次的继承及添加新的属性类,实现产品模型的横向和纵向扩展.基于这种产品信息模型及其表达方法,提出了一种多学科协同产品建模的系统结构,可有效地支持多学科协同地进行产品建模,并具有良好的模块性、柔性及可扩展性.     

Developing of an expert system for nonferrous alloy design

Expert systems have been used widely in the predictions and design of alloy systems. But the expert systems are based on the macroscopic models that have no physical meanings. Microscopic molecular dynamics is also a standard computational technique used in materials science. An approach is presented to the design system of nonferrous alloy that integrates the molecular dynamical simulation together with an expert system. The knowledge base in the expert system is able to predict nonferrous alloy properties by using machine learning technology. The architecture of the system is presented.     

Photovoltaic applications

The strong social implications of the energy systems, the complex links between the technical systems, and the connections to environmental aspects, have imposed strong efforts in the development of researches on the electricity production from renewable sources. Photovoltaic (PV) systems, converting sunlight energy into electrical energy, are characterised by modularity, functional autonomy and long duration of operation. This paper presents a detailed analysis of real-case applications of PV systems, already in operation for some years, with the focus on the technologies adopted, energy and data management and characterisation of the PV system performance.     

基于集成学习算法的可持续模块划分方法

随着客户对可持续产品需求的增加,针对产品功能和结构属性的传统模块化设计,正逐步转向可持续模块化设计。然而当前的模块划分方法更注重于对模块化指数的优化迭代,忽略了产品多组件间的信息传递和反馈。为此,提出一种集成学习算法的可持续模块划分方法。首先,面向组件间的回收性、材料和寿命因素,构建产品的综合DSM模型。其次,提出集成学习算法的弱分类器构建规则和强分类器结合策略。最后,通过颚式破碎机案例验证了所提模块划分方法的可行性,集成学习算法同直接聚类和遗传算法的模块划分结果对比,表明所提方法各模块组件更接近DSM的对角线。     

神经网络在线自学习跟踪控制及其在伺服系统中的应用

针对传统自适应和自校正控制中存在的问题，提出一种基于神经网络的在线自学习控制方法，既做到了对象模型的在线辨识和控制器的在线设计，又避免了神经网络控制方法通常存在的实时控制的困难，使复杂系统的在线学习控制成为可能。仿真表明该方法具有良好的鲁棒性和控制精度。     

Product variety management

A great challenge facing industry today is managing variety throughout the entire products life cycle. Drivers of products variety, its benefits, pre-requisites and associated complexity and cost are presented. Enhancing consumers’ value through variety and approaches for achieving it efficiently including modularity, commonality and differentiation are discussed. Variant-oriented manufacturing systems paradigms, as enablers of product variety, and the effective co-development of variants and their manufacturing systems to ensure economic sustainability are reviewed. Industrial applications and guidelines to achieve economy of scope with advantages of economy of scale are discussed. Perspectives and insights on future research in this field are offered.     

An attention-gating recurrent working memory architecture for emergent speech representation

This paper describes an attention-gating recurrent self-organising map approach for emergent speech representation. Inspired by evidence from human cognitive processing, the architecture combines two main neural components. The first component, the attention-gating mechanism, uses actor–critic learning to perform selective attention towards speech. Through this selective attention approach, the attention-gating mechanism controls access to working memory processing. The second component, the recurrent self-organising map memory, develops a temporal-distributed representation of speech using phone-like structures. Representing speech in terms of phonetic features in an emergent self-organised fashion, according to research on child cognitive development, recreates the approach found in infants. Using this representational approach, in a fashion similar to infants, should improve the performance of automatic recognition systems through aiding speech segmentation and fast word learning.     

A Connectionist Model of Choice and Reaction Time in Absolute Identification

A connectionist architecture is developed that can be used for modeling choice probabilities and reaction times in identification tasks. The architecture consists of a feedforward network and a decoding module, and learning is by mean-variance back-propagation, an extension of the standard back-propagation learning algorithm. We suggest that the new learning procedure leads to a better model of human learning in simple identification tasks than does standard back-propagation. Choice probabilities are modeled by the input-output relations of the network and reaction times are modeled by the time taken for the network, particularly the decoding module, to achieve a stable state. In this paper, the model is applied to the identification of unidimensional stimuli; applications to the identification of multidimensional stimuli—visual displays and words—is mentioned and presented in more detail in other papers. The strengths and weaknesses of this connectionist approach vis-à-vis other approaches are discussed     

A Topological Semantics for Rule Extraction with Neural Networks

Rule extraction with neural networks is a subject of increasing interest. Research in this area could benefit from the availability of a formal model of the semantics of the rules. A model of this kind would express the relationship between the application data, the neural network learning model and the extracted rules with mathematical rigor, allowing systematic analysis and modification of rule extraction approaches and the neural network architectures used. However, formal models of this kind are not in common use. This paper proposes a formal semantic model and includes an analysis of an example rule extraction architecture and some issues raised by it and other architectures. In the formal model, the semantics of a neural network is expressed through a form of model theory based upon concepts from topology, including limit points and continuous functions. A state of adaptation of the neural network in which it has learned a set of rules from training data corresponds to a continuous function between topological systems. Topological systems, the domains of inputs to the network, are a generalization of the concept of a topological space. The results of an example analysis with this model suggest a direction for improvements to the example architecture and the desirability of applying the model to other rule extraction approaches.     

新一代智能数控的研究现状与发展

综述了国内外新一代智能数控系统体系结构研究的现状，介绍了关于新一代智能数控系统硬件和软件的结构形式、基于PC机平台的实现技术及数控网络环境的研究情况，指出了新一代智能数控系统的研究发展趋势。     

Connectionist learning: A comparison of neural networks and an optical thin-film multilayer model

Current work on connectionist models has been focused largely on artificial neural networks that are inspired by the networks of biological neurons in the human brain. However, there are also other connectionistarchitectures that differ significantly from this biological exemplar. We proposed a novel connectionist learning architecture inspired by the physics associated with optical coatings of multiple layers of thin-films in a previous paper (Li and Purvis 1999, Annals of Mathematics and Artificial Intelligence, 26: 1-4). The proposed model differs significantly from the widely used neuron-inspired models. With thin-film layer thicknesses serving as adjustable parameters (as compared with connection weights in a neural network) for the learning system, the optical thin-film multilayer model (OTFM) is capable of approximating virtually any kind of highly nonlinear mappings. The OTFM is not a physical implementation using optical devices. Instead, it is proposed as a new connectionist learning architecture with its distinct optical properties as compared with neural networks. In this paper we focus on a detailed comparison of neural networks and the OTFM (Li 2001, Proceedings ofINNS-IEEE International Joint Conference on Neural Networks, Washington, DC, pp. 1727-1732). We describe the architecture of the OTFM and show how it can be viewed as a connectionist learning model. We then present experimental results on solving a classification problem and a time series prediction problem that are typical of conventional connectionist architectures to demonstrate the OTFM's learning capability.     

基于MAS的动态协同可重构单元制造系统研究

动态多变的生产环境对制造系统的自适应性提出了越来越高的要求,单元制造系统能够以单元的形式重构,形成以信息为主、与批量生产无关的制造联合体(虚拟制造单元)以快速响应外界的变化需求.文章论述了单元制造系统国内外研究现状,建立了单元制造系统的体系结构模型.在分析制造系统控制结构的基础上,提出了基于MAS的单元化制造系统协同控制结构,该结构具有分层、分散及协调控制的特点,保证了系统柔性且易于实现全局优化.建立了基于CORBA的MAS可重构单元管理软件体系结构,松散偶合的软构件技术易于实现制造企业现有车间层管理软件系统的扩展和升级.     

Semi-Double-loop machine learning based CPS approach for predictive maintenance in manufacturing system based on machine status indications

The paper presents two original and innovative contributions: 1) the model of machine learning (ML) based approach for predictive maintenance in manufacturing system based on machine status indications only, and 2) semi-Double-loop machine learning based intelligent Cyber-Physical System (I-CPS) architecture as a higher-level environment for ML based predictive maintenance execution. Considering only the machine status information provides rapid and very low investment-based implementation of an advanced predictive maintenance paradigm, especially important for SMEs. The model is validated in real-life situations, exploring different learning algorithms and strategies for learning maintenance predictive models. The findings show very high level of prediction accuracy.     

A multi-agent architecture for plug and produce on an industrial assembly platform

Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.     

先进制造系统信息集成技术综述

先进制造系统信息集成技术是实施敏捷制造的基础，信息集成技术能够将来自数据管理系统、内容管理系统、数据仓储和其他企业应用软件的核心元素绑定成一通用的平台。在对国内外相关研究进行分析的基础上，文章阐述了先进制造系统信息集成的内容、方法、企业制造网络平台及其研究现状，提出了当前制造信息系统信息集成存在的问题；信息集成的关键技术是将企业现有的IT技术集成到一个统一框架中。企业的面向服务架构是敏捷制造系统信息集成方法的发展趋势，其目标是根据市场需求，对企业应用软件提供的功能进行业务流程编排，在任意水平上实现集成。     

Modularity in small machine tools

Up to now, machine tools in micro production hardly feature modularity and hence offer only few opportunities to reconfigure or individualize a manufacturing process. This article gives a summary of concepts, how modularity in small micro machine tools can be designed and implemented. A size-adaptable machine frame is the basis for various possible process layouts. Adapters, a feed module with replaceable drive and kinematics, equipped with appropriate interfaces as well as a supply concept are introduced to allow further configuration. The combination of those concepts characterizes a small modular machine tool system. Technical solutions and fully functional prototypes will be shown and discussed in the following paper.     

Enhanced risk management by an emerging multi-agent architecture

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.