复杂产品设计与多学科设计优化综述

开展复杂产品(系统)设计理论和方法研究有助于提高复杂产品的设计水平,也是当前产品设计研究领域中的热点和难点.多学科设计优化(MDO)作为解决复杂产品(系统)设计难题的一种最新、最有效的理论和方法,受到越来越广泛的关注.在分析产品设计活动的基础上,论述了复杂产品(系统)的概念,分析了复杂产品及其设计的复杂性.系统研究了多学科设计优化的提出、定义、主要思想、国内外研究与应用现状、主要研究内容和关键技术等问题,讨论了国内广泛并深入开展多学科优化设计研究和应用的必要性和紧迫性.     

数控加工仿真系统的研究

数控加工作为现代机械加工的主要方式,是目前应用最广泛的加工方法之一.数控加工仿真是一个复杂的研究课题.涉及多方面的基础理论和试验的研究,是数控加工技术进一步发展和应用必须突破的难点.对数控加工过程进行仿真具有重要的理论研究与实际应用价值.因此本文着重研究仿真技术在数控系统中发展情况.本文介绍了仿真系统的分类及其对加工过程的作用以及对几何仿真和物理仿真的研究现状和研究内容,并对数控系统仿真技术水平及现状进行了分析,展望了数控仿真技术的发展趋势.     

基于生产线优化配置的混杂系统建模与仿真

基于生产线连续和离散并存的混杂特征，提出了混杂系统的建模及其优化控制方法。研究了加工单元的混杂自动机模型（混杂基本结构），面向生产线的混杂系统可看作若干混杂基本结构的合成，有效地避免了系统规模增长给模型造成的组合爆炸问题。在此基础上，对满足一定产量情况下最大化机器利用率的混杂优化控制方法进行了研究，提出了基于蒙特卡洛法的性能优化仿真计算方法。最后，对某有限缓冲容量串行生产线进行了数值仿真计算，仿真结果验证了该研究方法的可行性和有效性。     

基于混杂系统特性的机械式自动变速器故障诊断策略

基于机械式自动变速器(Automated manual transmission, AMT)自动变速系统混杂特性,对AMT自动变速系统故障诊断技术进行研究。运用混杂自动机建立AMT自动变速系统模型,结合系统运行特点研究系统行为轨迹,分析混杂系统模型与行为轨迹之间一致性关系。分析AMT自动变速系统故障定义,结合混杂系统模型提出AMT混杂系统故障行为的定义。通过混杂系统故障行为轨迹,分析故障可诊断性条件,从系统故障模型和故障阈值条件的角度描述故障、可诊断性、诊断精度、系统可观测状态变量之间的关系,并运用故障熵的概念来描述故障可诊断的程度。从系统功能、系统轨迹、系统控制周期多角度对AMT混杂系统行为轨迹进行分析和划分类型,结合国际故障诊断参考流程提出AMT自动变速系统故障诊断策略。将故障诊断策略和诊断算法移植于实车平台,大量实车里程验证了该策略的正确性和实时性。     

风力发电系统研究与应用前景综述

随着科技的迅猛发展,风力发电系统的设计呈现出数字化、复杂化的发展趋势.文中从TRIZ理论、数字孪生和复杂系统的视角,以风力发电系统的特征为切入点,对于风力发电系统的相关研究进行综述,研究了TRIZ理论、数字孪生在风力发电领域复杂系统创新设计中的应用.最后讨论了TRIZ理论和数字孪生在风力发电复杂系统创新设计中的发展趋势和应用前景.     

机械多体系统计算机辅助分析的理论方法及其应用研究

系统地介绍了作者近年在机械多体系统计算机辅助分析的理论方法及其在机构与机器人等机构多体系统中的应用做的工作和成果。列举了进一步研究的课题及今后发展的趋势。     

云计算在军事训练系统中的应用探析

云计算作为一种网络技术,以其巨大的优势对军队信息化建设产生了深远的影响。将云计算引入军事训练领域,对于转变传统军事训练观念、加快军事训练系统融合集成和资源开发利用、促进战斗力的快速生成具有重大意义。本文介绍了云计算的概念和作用,分析了云计算对于军事训练系统研究的重要价值和意义,研究了云计算应用于军事训练系统中的主要方法和重点内容。     

功能危险分析在飞机刹车系统中的应用研究

功能危险分析(Functional Hazard Analysis,即FHA)是一个分析和评估过程,主要是以分析评估系统内或设备中潜在的故障及所发生故障的影响。它通过分析来确定系统操作中可能发生的故障,并判断故障对人员的危害。FHA是系统安全评估中的重要内容,是其他安全性分析项目的基础,更是民用飞机满足适航审定要求必须完成的分析项目。通过对功能危险分析方法在飞机刹车系统应用过程的分析,总结了该方法应用时遇到的难点以及应对办法,并简要说明了分析结果。     

机械多体系统计算机辅助分析的理论方法及其应用研究

系统地介绍了作者近年来在机械多体系统计算机辅助分析的理论方法及其在机构与机器人等机械多体系统中的应用方面所做的工作和成果，列举了进一步研究的课题及今后发展的趋势。     

10千瓦瞬变电磁发送系统技术特点技术及GTR应用技术研究

１０千瓦瞬变电磁发送系统的主要技术难点，特色分析及功率器件ＧＴＲ在该系统中的应用。     

混合式土壤源热泵系统的研究现状及展望

由于土壤源热泵系统单独长期运行存在土壤热失衡的问题,为此本文详细分析了土壤源热泵系统产生土壤热失衡的原因。并以此阐述了针对土壤热失衡问题而广泛采用的混合式土壤源热泵系统,并就其研究与发展现状进行了综述。介绍了混合式土壤源热泵系统的工作原理、运行模式、功能及系统优势。指出了目前该系统研究与应用中亟待解决的关键技术问题。最后对混合式土壤源热泵系统进行了建议和展望。     

Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review

Reliable recognition of fault type and assessment of fault severity is essential for decision making in condition-based maintenance of gear transmission systems. In engineering practice, the gear systems are often subject to hybrid faults on the same component or different components. The concurrence of multiple faults makes the fault detection, in particular, the examination of both the fault types and severities, more challenging. Recently, this research area has been recognized as an important direction. A logic solution is to decouple the hybrid faults. This paper reviews various aspects of recent research in decoupling diagnosis of hybrid faults in gear transmission systems, and discusses the techniques used for gearbox hybrid faults decoupling. The general fault detection technologies for gearboxes are also briefly summarized. A potential methodology based on the bounded component analysis (BCA) for hybrid faults decoupling is discussed. Possible future research trends of gearbox hybrid faults decoupling diagnosis are suggested.     

Evolutionary optimisation of hedging points for unreliable manufacturing systems

The production rates of manufacturing systems are notoriously difficult to control, since such systems are dynamic, uncertain and non-linear. However, the introduction of hedging-point policies for such systems has led to much progress in optimal production control. But the theoretical results so far obtained for such hedging-point policies are still far from complete, since the optimal hedging points (i.e., the optimal inventory levels) are analytically available only for simple systems and under restrictive assumptions. In this paper, an evolutionary stochastic optimisation procedure is proposed to estimate the short-run optimal hedging points for failure-prone manufacturing systems under crisp-logic control. This methodology is illustrated by examples and is validated by comparing the evolutionary results with the available analytical long-run solutions. The proposed evolutionary methodology is also shown to be capable of generating optimal hedging points for unreliable systems producing multiple products with different priorities. In addition, the relative merits of genetic algorithms, evolution strategies and adaptive evolution strategies in hedging-point optimisation are compared.     

Modeling and design optimization of asynchronous flexible assembly systems with statistical process control and repair

This article presents the implementation of hybrid procedures involving the use of analytical performance evaluation techniques, discrete event simulation, and Monte Carlo optimization methods for the stochastic design optimization of asynchronous flexible assembly systems (AFASs) with statistical process control (SPC) and repair loops. AFASs are extremely complex and difficult to analyze in that such systems are subject to starvation and blocking effects, random jam occurrences at workstations, and splitting and merging of the assembly flow due to repair loops. Hence, an integrated approach simultaneously analyzing the interactions between product quality and optimal/near optimal system design is pursued. In the analytical analysis stage, a model based on GI/G/1 queueing network theory is used. In the Monte Carlo optimization stage, two alternative stochastic optimization approaches, namely, heuristic versions of stochastic quasigradient and simulated annealing algorithms, are implemented and compared in terms of their capabilities of solving complex AFAS design problems. The hybrid procedures presented appear to perform reasonably well in designing AFASs to reach a target production rate.     

On-line and off-line wheel/rail contact algorithm in the analysis of multibody railroad vehicle systems

An on-line and off-line hybrid contact algorithm for modeling wheel/rail contact problems is developed based on the elastic contact formulation. In the hybrid algorithm developed in this investigation, the off-line tabular search is used for predicting the location of tread contact points, while the on-line iterative search is used for predicting flange contact points. By so doing, a computationally efficient procedure is achieved while keeping accurate predictions of contact points for severe contact scenarios such as sharp curve and turnout negotiations. The use of the proposed hybrid algorithm can eliminate the time-consuming on-line iterative search for the second points of contact. Since the location of the second point of contact is pre-computed by the contact geometry analysis, the occurrence of two-point contact can be predicted by using the look-up table at the one-point contact configuration. A flange climb simulation demonstrates that the proposed hybrid contact search algorithm can be effectively used for modeling wheel/rail contacts in the analysis of general multibody railroad vehicle systems. This paper was presented at the 4th Asian Conference on Multibody Dynamics(ACMD2008), Jeju, Korea, August 20–23, 2008. Hiroyuki Sugiyama received his Ph.D. from the University of Illinois at Chicago in 2005. Dr. Sugiyama is currently an Assistant Professor at Tokyo University of Science, Tokyo, Japan. His research interests include the development of computer formulations for contact problems in vehicle systems and the large deformation problems of constrained multibody systems. Kohei Araki received his BS degree in Mechanical Engineering from Osaka City University in 2006. Mr. Araki is currently a Master’s student at Osaka City University, Osaka, Japan. His research interest is in the modeling of wheel/contact problems in railroad vehicle dynamics. Yoshihiro Suda received his Doctoral degree from the University of Tokyo in 1987. Dr. Suda is currently a Professor at the University of Tokyo, Tokyo, Japan. His research interests are in the dynamics of railroad vehicles and automobiles, in-telligent transportation systems (ITS) and personal mobility vehicles. He is currently serving as an Associate Editor of the IMechE Journal of Multi-Body Dynamics.     

Finite time stability and controller design for nonlinear impulsive sampled-data systems with applications

This paper investigates the finite time stability (FTS) for nonlinear impulsive sampled-data systems. By constructing an appropriated Lyapunov function and employing average impulsive interval (AII) method, some FTS criteria for the nonlinear impulsive sampled-data systems are derived in terms of linear matrix inequalities (LMIs), which can be easily verified via the LMI toolbox. The hybrid controller including sampled-data controller and impulsive controller is designed via the established LMIs. Moreover, the impulse effect considered in this paper including stabilizing impulse and destabilizing impulse. Our developed results are less conservative than the recent work in the literature. Finally, two numerical examples are provided to show the applications of the proposed criteria.     

Smart manufacturing systems for Industry 4.0: Conceptual framework,scenarios, and future perspectives

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.     

A model management systems approach to manufacturing systems design

Manufacturing systems design involves the solution of a complex series of interrelated problems. This complexity will increase in the future as manufacturing practices change to meet increased global competition. Research within manufacturing systems design has mainly been focused on finding improved models for solving particular problems, or extending existing modeling techniques. This has resulted in numerous modeling tools being available to support manufacturing systems design. However, little research work has been carried out into consolidating the existing theories and models. As a result, a large body of this work has not been applied in industry.Model management has evolved as a research area which investigates methods for storing, modifying, and manipulating models. This article describes a prototype model management system for manufacturing systems design. The objective here is not to develop another decision support system for manufacturing design, but to illustrate, through the development of a prototype system, a number of key ideas of how concepts from the area of model management systems can be used to support manufacturing systems design. The prototype model management system utilizes the structured modeling framework and uses an extended version of the structured modeling language. An important aspect of the prototype model management system is the incorporation of the model development task, thus allowing the system to be easily updated and adapted. The prototype system was evaluated using a range of queueing network models for manufacturing systems design.     

Model predictive control of discrete-time hybrid systems with discrete inputs

This paper proposes and discusses a model predictive control approach to hybrid systems with discrete inputs only. The algorithm, which takes into account a model of a hybrid system, described as a mixed logical dynamical system, is based on a performance-driven reachability analysis. The algorithm abstracts the behavior of the hybrid system by building a "tree of evolution." The nodes of the tree represent the reachable states of a process, and the branches connect two nodes if a transition exists between the corresponding states. A cost-function value is associated with each node, and based on this value the exploration of the tree is driven. As soon as the exploration of the tree is finished, the corresponding input is applied to the system and the procedure is repeated.     

Expert systems for fault diagnosis of machinery

This paper is devoted to illustrating the impact of expert system technology on fault diagnosis of machinery. It first introduces the basic concept of a machinery environment and then discusses the main features of expert systems. A review of the state of the art of current applications in the field of machinery diagnosis is presented. The impact of this newly emerging technique on the design of diagnosis systems is also analysed.