面向齿轮传动系统协同设计的任务规划方法研究

在齿轮传动系统的协同设计中,存在任务间耦合关系复杂、返工现象严重、设计流程混乱的问题,针对这一问题,提出了一种基于模糊设计结构矩阵(FDSM)的全局-局部两级任务规划方法。首先,根据任务分解层次结构对设计任务进行了分解,依据设计任务间的输入输出关系构建了FDSM,通过对FDSM的行列变换完成了对耦合设计任务的识别与非耦合设计任务的规划,实现了全局规划;其次,引入了模糊耦合度量化设计任务间的耦合程度,计算了耦合集内各任务的输入输出量,完成了耦合任务集的解耦,实现了局部规划;最后,以齿轮传动系统协同设计为例,阐述了整个规划过程,并对所提出的方法进行了验证。研究结果表明:规划后FDSM的复杂程度降低了35.32%,减少了设计迭代次数;实现了并行设计任务集的自动识别,优化了设计任务执行次序,缩短了设计时间,可以为协同设计任务分配提供参考。     

并行设计中任务调度问题的研究

为了对产品并行开发过程进行合理规划,针对并行设计中任务调度的特点,综合考虑并行设计所涉及的企业运作的具体因素和约束,以模糊集理论为基础,给出了一种建立并行设计过程模糊设计结构矩阵的方法,提出了基于“任务影响度”的耦合任务集内任务执行调度算法。综合考虑并行设计中子任务和团队内成员的各种动态、模糊和非量化因素,建立了子任务和团队内成员之间的平衡矩阵,并利用匈牙利算法和遗传算法,按不同情况给出了子任务到团队内成员的分配方法。     

FMA结构化分解驱动的加工中心设计过程规划

为了更好的实现加工中心的设计,减少设计过程的迭代次数,缩短开发周期,以加工中心的功能运动为核心,利用FMA结构化分解将加工中心按照"功能-运动-动作"的顺序逐层分解至相应的元动作,定义了元动作设计结构单元,在考虑各设计单元耦合性的基础上提出了FMA结构化分解驱动的加工中心设计过程规划流程;然后用可变度与敏感度双指标表示设计单元间的耦合程度,以设计结构矩阵为工具对设计单元间的耦合强度进行量化,利用基于次序指标的割裂算法对耦合强度矩阵进行割裂规划,优化设计单元的初始设计顺序,从而实现了加工中心的设计过程规划,加速了加工中心的设计与开发;最后以托盘交换架功能组元动作设计单元间的设计规划过程为例,证明了本文的可行性。     

考虑RMTSS特性的产品综合设计过程规划方法

在现有过程规划技术的基础上,提出了产品性能与可靠性、维修性、测试性、保障性、安全性（RMTSS）综合设计过程规划与重组的一般方法与流程。分析了RMTSS设计工作对产品设计过程的影响。考虑到综合设计任务间信息交互的特点,给出了一种多指标的信息依赖关系确定方法。针对现有撕裂算法处理RMTSS设计所带来的大规模耦合任务集的排序异常问题,提出了一种基于模糊聚类的耦合集分割算法。通过耦合集分割、撕裂与聚合的综合应用,完成耦合任务的合理排序。最后,结合电子产品板级的性能与可靠性、测试性的综合设计过程进行了分析和验证。     

并行设计耦合任务集的识别方法

并行设计中强调多种知识的有效集成，耦合任务是一组具有相互依赖关系的任务的集合，它正反映了这种知识集成的要求，它对设计时间和质量均有着关键的影响。通过对耦合任务集的识别可以为合理分配设计资源、在效规划设计过程、科学组建集成开发队伍提供参考。本文在建立设计过程图的基础上提出了识别耦合任务集的有效方法。     

协同产品设计过程中任务量的迭代收敛特征分析

为帮助设计者合理规划与调整设计任务,基于耦合任务间的实时沟通这一实际情况,引入任务量完工比例参数,以表征产品设计中的协同特征,并以迭代过程中的任务调整量来度量耦合任务之间的联系与作用强度,将设计结构矩阵转化为任务转移矩阵,建立任务总量模型,揭示了因耦合引起的设计过程反复和任务总量迭代收敛的规律。根据特征向量矩阵,确定影响任务总量迭代收敛的主要因素。通过实例,证明该任务总量模型和迭代收敛规律是合理可行的。     

雷达结构并行设计过程分解方法初探

雷达结构设计实施并行工程必然会增加设计过程的复杂程度和管理难度。对设计过程合理地分解可以减轻或消除各种设计影响因素之间的相互耦合。分解的关键是耦合任务集的识别。本文基于图论的有关理论和代数矩阵方法 ,提出一种雷达结构并行设计过程的分解方法。最后给出一个此法在雷达结构设计过程分解中的应用例。     

一种机电产品并行设计中协同方法的研究

机电产品的设计需要多学科设计人员共同参与、协同设计。按学科分解设计任务宜采用并行的模式完成。学科间的协同本质是设计变量取值的协调 ,以空间集方式表示设计解可以使各学科间的协同变得方便。基于这一思想 ,本文分析了一个简单机电产品设计过程中的耦合关系 ,描述了如何把基于解空间的协同方法应用于设计过程     

面向自顶向下协同装配设计任务时间窗算法

针对自顶向下协同装配设计过程具有高并行性和任务之间的依赖关联等特点,提出了自顶向下协同装配设计的任务时间窗算法.针对任务分组后,组内、组外任务之间存在的依赖关联约束,通过串行活动并行化和耦合活动串行化来确定任务的时间窗约束,消除了时间窗约束求解过程中产生的冲突,减少了设计过程等待时间.在此基础上,构建了基于设计结构矩阵的设计过程随机规划算法,确定了任务的时间窗.     

基于内部迭代的耦合任务集求解方法

针对并行迭代模型中的工作转移矩阵模型存在的缺陷,从任务迭代时间、信息传递的不完备特性以及迭代次数的有限性等方面进行了修正,建立了改进的并行迭代模型,进而结合遗传算法,求解多阶段模式下耦合集的最优执行工期和成本。用该模型求解两个产品开发过程的耦合任务集,验证了其有效性,并进一步分析了影响耦合集工期和成本的因素。     

Concurrent design process analysis and optimization for aluminum profile extrusion product development

Owing to intricate interdependency and information feedback among tasks, the concurrent design process probably cannot converge to the correct solution when there are wrong integration and improper interaction between activities. Therefore research on concurrent design process optimization is necessary. In the paper, a novel methodology is proposed to analyze and optimize the concurrent engineering process scientifically. Based on design structure matrix and graph theory, coupled task recognition and design task level plotting are performed for the concurrent design process of aluminum profile extrusion product development. Three factors are used to describe the coupling property of activity, namely sensitivity, complexity and affection factors, which provide the basis to analyze development process quantitatively. And an optimism algorithm is presented to define the initial iteration order of coupled task set. Finally a rational and efficient concurrent design process model is constructed, which can make aluminum profile product development faster, with lower costs and higher quality. The methodology proposed is also applicable to other concurrent engineering fields.     

Multi-loop decentralized PID control based on covariance control criteria: an LMI approach

PID control is well known and widely applied in industry and many design algorithms are readily available in the literature. However, systematic design of multi-loop or decentralized PID control for multivariable processes to meet certain objectives simultaneously is still a challenging task. Designing multi-loop PID controllers such that the process variables satisfy the generalized covariance constraints is studied in this paper. A convergent computational algorithm is proposed to calculate the multi-loop PID controller for a process with stable disturbances. This algorithm is then extended to a process with random-walk disturbances. The feasibility of the proposed algorithm is verified by applying it to several simulation examples.     

A novel feasible task sequence-oriented discrete particle swarm algorithm for simple assembly line balancing problem of type 1

To solve the simple assembly line balancing problems of type 1 (SALBP-1), almost all of particle swarm algorithms (PSAs) for SALBP-1 adopt task sequence-oriented solution representation and are limited to the priority-based indirect encoding of feasible task sequence (FTS) so far. In this paper, firstly a novel FTS-oriented particle swarm algorithm (FTSOPSA) that directly records a FTS by a particle, named direct discrete PSA (DDPSA), is proposed to solve SALBP-1. In the DDPSA, a new multi-fragment crossover-based updating mechanism is developed, and the fragment mutation is incorporated into the DDPSA to improve exploration ability. Secondly, a systematic comparison of DDPSA and two existing FTSOPSAs as well as two existing genetic algorithms (GAs) has been presented against a set of instances selected from the literature and 15 randomly generated instances of SALBP-1. Comparisons between the FTSOPSAs and existing GAs show promising higher performance of the proposed DDPSA for SALBP-1, and also show that the direct encoding of FTS seems superior to the priority-based indirect encoding of FTS for solving SALBP-1.     

产品协同设计中的任务调度方法研究

分析并行工程下协同设计的特点,提出一种事件触发的任务调度策略。为了合理地进行任务调度,在提出调度获益函数的基础上建立了协同设计的任务调度模型。运用遗传算法求解该调度模型。结果显示了该调度算法的有效性,从而为产品的协同设计任务调度提供了一种有效的方法。     

Disturbance-rejection-based tuning of proportional–integral–derivative controllers by exploiting closed-loop plant data

A systematic data-based design method for tuning proportional–integral–derivative (PID) controllers for disturbance attenuation is proposed. In this method, a set of closed-loop plant data are directly exploited without using a process model. PID controller parameters for a control system that behaves as closely as possible to the reference model for disturbance rejection are derived. Two algorithms are developed to calculate the PID parameters. One algorithm determines the optimal time delay in the reference model by solving an optimization problem, whereas the other algorithm avoids the nonlinear optimization by using a simple approximation for the time delay term, enabling derivation of analytical PID tuning formulas. Because plant data integrals are used in the regression equations for calculating PID parameters, the two proposed algorithms are robust against measurement noises. Moreover, the controller tuning involves an adjustable design parameter that enables the user to achieve a trade-off between performance and robustness. Because of its closed-loop tuning capability, the proposed method can be applied online to improve (retune) existing underperforming controllers for stable, integrating, and unstable plants. Simulation examples covering a wide variety of process dynamics, including two examples related to reactor systems, are presented to demonstrate the effectiveness of the proposed tuning method.     

Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms

In this paper, we describe a new form of neuro-fuzzy-genetic controller design for nonlinear system derived from a manipulator robot. The proposed method combines fuzzy logic and neuronal networks which are of growing interest in robotics, the neuro-fuzzy controller does not require the knowledge of the robot parameters values. Furtheremore, the genetic algorithms (GAs) for complex motion planning of robots require an evaluation function which takes into account multiple factors. An optimizing algorithm based on the genetic algorithms is applied in order to provide the most adequate shape of the fuzzy subsets that are considered as an interpolation functions. The proposed approach provides a well learning of the manipulator robot dynamics whatever the assigned task. Simulation and practical results illustrate the effectiveness of the proposed strategy. The advantages of the proposed method and the possibilities of further improvements are discussed.     

面向协同设计的任务规划与解耦策略

为了合理规划协同设计任务,缩短产品开发周期,首先提出了符号设计结构矩阵的概念,并根据设计结构矩阵元素的类型,对设计结构矩阵进行了分类,在有向图建模方法的基础上,扩展了有向图的建模方法。其次,提出了将后序任务对前序任务的强依赖关系用一个弱依赖关系和一个反馈特征来代替的弱化策略,并结合扩展有向图的建模方法和符号设计结构矩阵,给出耦合任务集自动解耦算法。最后以冲压模具协同开发为例进行了验证。实践表明,该方法能有效地减少耦合任务集设计迭代,具有实际的推广价值。     

图像处理技术在胶带撕裂检测中的应用

针对带式输送机胶带纵向撕裂故障,介绍了带式输送机胶带撕裂的原因以及撕裂信号的检测方法,提出一种运用图像处理技术进行胶带撕裂检测的新方法,是作者在总结大量胶带撕裂检测方法后提出的一种新的尝试,具有一定的工程应用价值。     

基于聚类分析的大容量耦合设计任务规划的研究

针对大容量耦合设计任务采用串行执行方式会导致设计项目开发周期冗长、规划方案数量庞大这一问题,通过对聚类分析对象的特点与耦合设计任务的特点进行分析比较,阐述了聚类分析用于缩短耦合任务执行周期和减少其规划方案数量的可行性,进而提出了一种基于聚类分析的大容量耦合设计任务规划的新方法,以获得耦合设计任务的最佳执行顺序及最短执行时间。以某机械手的研发过程为例,验证了该方法的有效性。     

冷轧过程断带故障的诊断研究

在冷轧过程中,断带故障是冷轧工序的主要生产故障之一。针对冷轧过程断带故障的特点,提出一种基于核主元分析(KPCA)非线性特征提取和最小二乘支持向量机(LSSVM)分类的故障诊断方法。此方法采用KPCA理论将冷轧过程原始空间数据映射到高维空间,并在高维空间进行主元分析,从而降维、去相关性,得到冷轧过程非线性特征向量。将降维后的特征主元作为LSSVM输入进行训练和识别,根据LSSVM的输出结果判断冷轧过程工作状态与故障类型。仿真结果表明:基于KPCA非线性特征提取和LSSVM分类的故障诊断方法计算速度快,能有效地提取冷轧过程断带故障特征,识别断带故障类型。