基于扩展干涉矩阵和遗传算法的拆卸序列规划

为高效求解非标准正交轴向上的装配体拆卸序列,将常用的基于全局坐标系的干涉矩阵扩展为包含基于装配体坐标系的全局干涉矩阵与基于零件坐标系的本地干涉矩阵干涉信息的扩展干涉矩阵,丰富了拆卸方向表达的多样性,并通过其检验拆卸序列的几何可拆性.在得到可行拆卸序列的基础上建立了目标函数,利用遗传算法对拆卸序列进行优化.通过一个实例验证了该方法在拆卸方向多样化方面的可行性和有效性.     

基于改进自适应和声遗传算法的装配序列优化研究

在考虑装配工具及零件装配方向改变对装配成本影响的同时, 增加装配体约束稳定性影响作为装配序列优化评价要素, 构建装配成本模型。基于装配体几何约束关系, 建立装配方向约束矩阵和连接关系矩阵, 并依据专家知识得到零件所对应工具的映射表, 通过矩阵扫描得到装配成本惩罚次数, 并与惩罚系数加权得到装配成本函数值。在和声算法中引入自适应的参数调节方式及遗传算子, 使和声算法能够解决离散型数学问题, 并较好地改善了和声算法方向性差的缺点, 通过求解惩罚函数最小值, 得到其所对应的最优装配序列解, 最后结合实例对比, 验证了该方法有的效性。     

车身装配序列生成及分总成的识别

应用图论知识，引入虚联接的概念来研究车身零件装配的优先关系，并用相邻矩阵表示．基于装配结构信息的矩阵表达，提出车身分总成的提取应满足的数学条件，并通过矩阵的排序运算，生成所有的装配序列．该方法可以有效地产生分总成，从而克服组合爆炸及简化装配序列的规划．     

基于集成干涉矩阵的蚁群装配序列规划

针对装配序列规划问题,构建了面向装配的蚁群算法。利用有向图进行产品的装配建模,有效地表达了装配零件之间的接触以及优先关系。探讨了装配干涉矩阵的形式和性质,提出了集成干涉矩阵的概念和变换方法,采用集成干涉矩阵以及工具列表矩阵来表达模型的基本装配信息。分析了基于干涉矩阵的可行方向推导过程、装配零件的状态转移概率以及蚁群算法的信息素更新规则。从装配可行方向的改变次数和装配工具的改变次数建立目标优化函数,从而建立面向装配的蚁群算法用于序列的搜索及构造,并给出蚁群算法的伪代码。最后通过实例对算法进行了验证说明。     

基于改进人工萤火虫算法的装配序列规划研究

装配是装备保养维护的重要环节,高效和无损地装配好拆卸维护的零件在战场上尤为重要.为了解决装配序列规划最优解问题,根据装配序列规划的特点,提出了基于人工萤火虫算法的离散SA-GSO算法.首先利用干涉矩阵对装配序列进行了可行性分析,并根据操作实际设定了适应度函数;然后针对人工萤火虫算法存在的易早熟等缺陷,利用模拟退火原理进行优化并对算法进行离散化,以适用于装配序列最优解问题;最后进行了实例验证,实验结果证明了该算法的可行性及有效性.     

基于离散时间最优控制的航空发动机装配序列规划

为实现航空发动机维修差错的控制,采用基于优先约束关系的装配子网对发动机部件装配序列建模.在给定的装配评价准则下,将装配序列规划问题转化为最优变迁激发序列问题.引入离散时间的Pontryagin最小值原理(DTPMP),将极小化哈密顿函数这一全局优化的必要条件作为求解零部件装配序列的启发信息.为避免潜在死锁,给出了最优变迁激发序列算法.最后对最优装配序列规划算法的分析显示,该算法有多项式时间的复杂度.     

基于果蝇优化算法的多工位装配序列规划

为同时解决产品装配序列规划和多工位分配问题,提出一种面向复杂产品的基于果蝇优化算法的多工位装配序列规划方法。首先,基于果蝇优化算法设计了针对求解序列的编码体系;其次,采用多子种群并行搜索模式,重新设计了果蝇优化算法的搜索过程;然后,为了综合考虑多工位上相关装配操作成本的影响,提出了新的适应度函数表达式,并将适应度函数与优先序列矩阵结合起来对进化过程进行引导,实现了对产品装配序列和工位分配顺序的优化;最后,以飞机起落架为例,验证了所提方法在解决多目标优化问题方面的有效性。     

基于符号零压缩二叉决策图的装配可行性判定方法

为了扩大装配序列规划的求解规模并提高求解效率,提升装配自动化水平,给出了装配体联接矩阵和干涉矩阵的零压缩二叉决策图描述,建立了基于符号零压缩二叉决策图的装配操作可行性判定方法。基于该方法,可以高效地求解出一个装配体的可行装配操作。结合实例验证了基于符号零压缩二叉决策图的装配体模型和可行装配操作判定方法的正确性和可行性。     

基于深度邻域搜索PSO 算法的装配序列优化问题

针对产品结构特征建立几何约束矩阵, 以最大化满足几何约束条件装配次数和最小化装配方向改变次数为目标, 研究产品装配序列优化问题. 利用值变换的粒子位置和速度更新规则, 基于具有随机性启发式算法产生初始种群, 提出一种带有深度邻域搜索改进策略的粒子群算法解决装配序列问题. 通过装配实例验证了所提出算法的性能并对装配序列质量进行了评价, 所得结果表明了该算法在解决装配序列优化问题上的有效性与稳定性.

     

一个基于知识的装配序列规划系统

本文提出一种基于知识的装配序列规划系统。该系统针对装配序列规划所涉及信息的纯几何描述和计算的复杂度等问题，采用领域知识推理，并结合适量几何计算的方法，求解装配序列规划问题。系统包括：任务理解，装配关系模型生成和序列规划三个组成部分。     

Geometric computation based assembly sequencing and evaluating in terms of assembly angle, direction, reorientation, and stability

Assembly sequence matters much to the performance in assembly production. Focusing on the spatial assembly sequencing and evaluating, a set of geometric computation methods and algorithms are studied systematically. A method entitled 3D geometric constraint analysis (3D-GCA) is proposed based on the planar GCA method combined with the techniques of oriental bounding boxes and the separation axis theorem. With 3D-GCA, the assembly precedence relations and the spatial geometric feasible assembly sequences can be reasoned out correctly and automatically. Furthermore, four evaluation criteria, viz. assembly angle, assembly direction, reorientation, and stability, and related algorithms are defined for evaluating the assembly’s complexity. For selecting the optimal sequence, a comprehensive evaluation function is constructed by integrating the four criteria and the weights are quantitatively allocated referring to fuzzy set theory, clustering analysis, and entropy theory. In addition, a software prototype system is developed and two case assemblies are studied. The analysis results and findings demonstrate that the proposed approaches and algorithms can provide significant assistance in the spatial assembly sequencing and the optimal sequence selection.     

序列拼接中重复子串屏蔽的KMP算法

在序列拼接中，为了解决重复序列这个难题，本文提出了利用KMP匹配算法来识别并屏蔽重复序列的方法．该方法利用模式序列中的失效函数计算得到失效链接值，也就是当前一位置匹配失败后，下一次匹配开始的位置．利用这一函数避免了可预见的无用搜索，将穷举搜索算法所需的计算量大大减少．通过计算机模拟，验证了对重复序列的屏蔽，该算法将穷举算法所需时间复杂度由原来的减少到了．     

白车身焊装过程小样本采样误差的计算机仿真

对白车身焊装过程中小样本采样误差进行了研究，提出了一种基于蒙特卡罗仿真的小样本采样误差的量化方法。通过与数理统计理论中采样误差的量化结果进行对比，验证了提出的蒙特卡罗仿真方法量化采样误差的正确性。     

Design of a real-time AND/OR assembly scheduler on an optimization neural network

The problem of finding an AND/OR precedence-constraint assembly schedule using optimization neural computation is presented. The precedence relationships of assembly operation result from the geometric constraints of subtasks. Because of the existence of geometric constraints among assembly subtasks, the assembly operation involves AND/OR precedence relationships; that is, the order of assembly crucially determines whether the desired task can be achieved. A feasible assembly schedule is a schedule that satisfies these AND/OR precedence constraints.It has been shown that all the feasible assembly schedules can be generated by transforming geometric constraints of subtasks to the pattern-matching operation. Using the question-answer pattern and pattern-matching operation, the assembly scheduling problem can be transformed into an AND/OR precedence-constrained traveling salesman problem (TSP). Two precedence-constrained TSPs, cost-constrained TSP (CCTSP) and state-constrained TSP (SCTSP), are discussed. The CCTSP artificially sets the cost of the prohibited moves to a very large value which ensures that the constraints are satisfied, while the SCTSP restricts the movement of next assembly subtasks. The advantage of the SCTSP over CCTSP in the generation of the assembly schedule will be illustrated.A novel method proposed here is to obtain the best AND/OR precedence-constraint assembly schedule using neural network computation. The geometric constraints of an assembled object are transformed into the elements of the connection matrix which specifies the connection strength among neurons. A modified Hopfield network is used to tackle the AND/OR precedence-constraints assembly scheduling problem. Multirobot assembly sequences generation is also discussed. The designed algorithm can accommodate various constraints and applications. Detailed algorithms, examples and experiments are presented.     

A virtual environment for complex products collaborative assembly operation simulation

As complex products such as automobiles and ships are usually developed at geographically dispersed locations, it brings the difficulty for component model verification and assembly process evaluation. To cope with this challenge, a relatively comprehensive system scheme which supports complex product real-time collaborative interactive assembly is proposed. To get high performance, the technologies such as parallel rendering for complex scene based on pc-cluster, high-efficient collision detection method, the mechanism of event synchronization based on HLA/RTI (High level Architecture/Run-time Infrastructure) are applied. A prototype system named Distributed Parallel Virtual Assembly Environment (DPVAE) is developed. With DPVAE, users at different location can do assembly operation collaboratively by hand or by tool, to conduct the component model verification and assembly process evaluation at the same time. Through a case study of RA750 car assembly, the validity of system functions has been demonstrated successfully.     

基于立体视觉的汽车安全气囊装配精度测评方法

本文提出了一种基于双目立体视觉检测而实现汽车安全气囊装配精度测评的方法,将CCD摄像机提供的待测安全气囊的图像与标准安全气囊的三维数模相结合,建立了汽车安全气囊轮廓检测点的位置公差V_x、V_y、V_z以及点所在轮廓曲线段的形状公差K作为装配精度参数的评价指标,自动完成轮廓尺寸参数的计算,判定待测安全气囊是否合格.实际验证结果表明,所建立的立体视觉安全气囊装配精度测评方法和指标可以快捷精确的评价汽车安全气囊的装配质量.     

多目标遗传算法在混流装配线排序中的应用

混流装配线实现在一条流水线上装配多种不同类型的产品。该文在总结混流装配线排序问题的基础上建立了二种排序的目标函数：最小化工作站的闲置与超载时间和保持均匀的零部件消耗速率。引入了基于Pareto理论和小生镜单元技术的适应度函数及选择算子构建了多目标遗传算法用于混流装配线的排序优化问题。通过一个混流装配线的多目标排序实验,验证了该方法的有效性。     

Optimization of a multi-Constant Work-in-Process semiconductor assembly and test factory based on performance evaluation

This paper considers a semiconductor assembly and test factory which is a three-segment-Constant Work-in-Process (CONWIP) system with overlapping machines. In the system, three types of carts circulate for meeting the physical requirements. The optimization problem in setting the suitable total Work-in-Process (WIP) level and the distribution in the three loops from the view of the trade-off between the throughput and the WIP level for the system is addressed. In the proposed model, the system is firstly modeled as a three-loop closed queue network and we propose an approximate method to evaluate the performance. The accuracy of the evaluation method was illustrated by numerical experiments, indicating that the method is fairly precise. Secondly, a Genetic Algorithm is designed to obtain near optimal results based on the performance evaluation. The semiconductor assembly and test system case as well as the application procedure were carried out in detail.     

A hierarchical approach on assembly sequence planning and optimal sequences analyzing

Assembly sequence planning (ASP) is a critical technology that bridges product design and realization. Deriving and fulfilling of the assembly precedence relations (APRs) are the essential points in assembly sequences reasoning. In this paper, focusing on APRs reasoning, ASP, and optimizing, a hierarchical ASP approach is proposed and its key technologies are studied systematically. APR inferring and the optimal sequences searching algorithms are designed and realized in an integrated software prototype system. The system can find out the geometric APRs correctly and completely based on the assembly CAD model. Combined with the process APRs, the geometric and engineering feasible assembly sequences can be inferred out automatically. Furthermore, an algorithm is designed by which optimal assembly sequences can be calculated out from the immense geometric and engineering feasible assembly sequences. The case study demonstrates that the approach and its algorithms may provide significant assistance in finding the optimal ASP and improving product assembling.