面向装配序列规划的模因算法研究

针对遗传算法在求解装配序列规划问题中收敛速度慢、产生重复解等问题,提出一种基于模因算法的装配序列规划方法。将模因算法中全局搜索与局部搜索相结合动态更新种群的策略引入,采用装配优先约束矩阵和干涉矩阵建立装配规划模型,并以装配单元之间的相异性之和构建适应度函数。在非干涉解空间中进行全局搜索,获得装配规划方案,再通过二叉树中序遍历调序算法将较优方案转化为可行解。通过交叉操作和变异操作后,在可行解空间内进行局部搜索,获得较优解。通过典型柱塞油泵装配规划验证了该算法的可行性和可靠性;并将其与遗传算法进行比较,证明其更有效。     

基于Pareto解集蚁群算法的拆卸序列规划

为提高产品拆卸序列规划的效率,分析拆卸序列规划问题中的多个优化目标平衡问题,提出一种基于Pareto解集的多目标蚁群优化算法求解此类拆卸规划问题,并给出拆卸序列的构建过程。通过利用拆卸矩阵推导拆卸可行条件,获得可以执行拆卸操作的零件及其可行的拆卸方向。通过利用零件的轴向包围盒(Axis aligned bounding boxes,AABB)计算零件的拆卸行程。考虑拆卸方向改变次数、拆卸总行程、拆卸零件数量为优化目标,通过利用蚁群算法搜索可行解并计算各个解之间的支配关系,得到Pareto解集,实现求解优化的拆卸序列,给出算法的具体步骤。最后以单杠发动机为拆卸实例,利用所提方法进行拆卸序列规划求解,通过分析试验结果,并对比典型的单目标蚁群规划算法,证明了该方法的高效性和可行性。     

横向剪切干涉测量中准确的相位恢复算法

提出一种剪切干涉测量中实现由相位差分准确高效恢复被测波前相位的算法.该算法是基于被测波前相位与其差分完全的点对点对应关系,及最小二乘算法原理.首先由被测波前相位与其差分完全的点对点对应关系及最小二乘原理,建立一特殊的线性等式,被测相位能通过解此等式直接获得.由于该线性等式的系数矩阵为稀疏矩阵,能转化为一新的小矩阵,以降低计算机存储空间和计算量;同时,由于该矩阵为正定矩阵,Choleski因式化分解方法能用来实现该线性等式方便的解.进行了计算机数值分析和相关试验测试,结果表明:该恢复算法可行,且计算精度高、计算复杂性低;可实现由相位差分准确恢复被测波前相位,同时具有良好的噪声误差传输特性.     

不确定离散模糊系统的保性能控制

对一类具有范数有界、时变参数不确定性的离散模糊时间系统和一个二次型性能指标的保性能状态控制律的设计问题进行研究。通过采用线性矩阵不等式的方法，导出了存在保性能控制律的一个充要条件，证明了该条件等价于一组线性矩阵不等式的可解性问题，并用这组线性矩阵不等式的可行解给出了保性能控制律的一个参数化表示。在此基础上，通过建立并求解一个凸优化问题，给出了保性能控制律的设计方法。     

一种型材直角定位焊装夹具的设计应用

针对常规的型材直角定位焊装夹具功能单一,型材组焊后件搬移困难,通用性差等不足,设计了一种外扣式直角定位焊装夹具,该夹具不受型材形状的限制,通过双向定位和锁紧来控制焊接应力和变形,保证了焊件装配准确性和夹紧定位的可靠性。通过测试和实地验证,可有效地减少定位辅助工作时间和焊接变形,保证焊接质量。     

车身复杂结构大规模问题的缩减计算

针对车身设计中复杂结构多参数大规模问题,提出了一种基于减基法和有限元的混合算法来进行缩减计算。该方法首先通过计算系统在有限个样本下的响应构造近似解空间,然后基于有限元方法分离出刚度矩阵中的设计参数,接着将矩阵向解空间进行投影,最后构建减缩计算系统。在新参数条件下,通过减缩系统得到大规模问题的响应,极大地提高了结构响应的计算效率。     

基于粒子群和约束满足的钢轧一体化批量计划优化

在分析钢铁生产中的钢轧一体化批量计划编制问题基本特征的基础上给出了一体化编制策略,并建立了问题的约束满足优化模型。针对模型的NP难特性,提出了一种将改进离散粒子群算法、约束满足和邻域搜索相结合的混合算法。算法采用自然数矩阵编码,每个粒子代表其相应任务分配问题的解;在构造启发式解的基础上,利用邻域搜索方法计算粒子的适应值;为提高算法的收敛速度,利用约束满足技术生成初始化可行种群并修复迭代过程中产生的不可行解。基于企业实际生产数据的仿真实验结果验证了模型和算法的有效性。     

基于TRIZ冲突解决原理的焊接工艺创新方法研究

工艺创新主要依靠企业内有限的知识积累和少数人灵感的随机性,创新难度大、周期长。为了实现工艺创新的系统化求解方法,本文以焊接工艺为研究对象,提出了基于TRIZ冲突解决原理的焊接工艺创新方法,用焊接工艺冲突矩阵来表达冲突参数和解决原理间的关系,据此求出焊接工艺问题的一般解,将这些一般解转化为具体焊接工艺问题的特定解,得到焊接工艺创新问题的求解方法,为具体的焊接工艺技术实施提供有效的创新解。最后以改善大型储罐的焊接变形问题为例,验证了焊接工艺创新方法的有效性。     

基于倍四元数的机器人逆运动学分析

采用倍四元数进行建模求解机器人逆运动学问题.将齐次变换矩阵转以倍四元数形式表示,建立了倍四元数形式的机器人运动学方程,再通过消元和运算得到机器人逆运动学的解.最后,以SCARA机器人为实例求解验证,证实该算法简单可行.     

基于方向变换矩阵的三维装配草图生成方法

针对求解方案设计获得的可行方案的空间布局及可视化表达问题，提出了三维装配草图的一种自上而下设计方法，建立了方向变换矩阵，并通过其分解描述子装配体在空间的布局。基于三维实体造型平台，提出了相应的算法加以实现。通过一个设计实例验证该生成方法及算法可行、有效。     

一维下料问题的启发式方法

针对单一规格的一维下料问题,提出一种新的随机启发式搜索算法,在求出所有可能切割方式的情况下,搜索出最优解,而且保证最后一根原材料余料长度最长,方便以后下料.对于多规格原材料情况的一维下料分解为多个单一原材料的一维下料问题来进行求解.计算表明,与启发式算法或者遗传算法相比较,随机启发式搜索算法结构简明,易于编程,计算速度快,节材效果优.     

Optimization of operation sequencing in CAPP using simulated annealing technique (SAT)

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an over constrained overall solution space and the final solution is normally far from optimal or even non-feasible. The operation-sequencing problem in process planning is considered to produce a part with the objective of minimizing the sum of machine, setup and tool change costs. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix and reward–penalty matrix using simulated annealing technique (SAT), a meta-heuristic. A number of benchmark case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. The proposed SAT integrates robustness, convergence and trapping out of local minima.     

基于Hopfield神经网络求解作业车间调度问题的新方法

对作业车间调度问题的换位矩阵表示方法进行了改进,给出新的作业车间调度问题的Hopfield神经网络计算能量函数表达式,然后提出改进的Hopfield神经网络作业车间调度方法。为了避免Hopfield神经网络容易收敛到局部极小的缺点,将模拟退火算法应用于Hopfield神经网络求解,提出随机神经网络作业车间调度方法。与已有算法相比,改进算法能够保证神经网络稳态输出为可行的作业车间调度方案。     

The Adaptive Branch and Bound Method of Tolerance Synthesis Based on the Reliability Index

Tolerance synthesis is a stochastic optimisation problem. It can be converted into deterministic optimisation by replacing stochastic design function constraints with deterministic reliability index constraints. This problem was first approached and solved with both a heuristic algorithm TOL-M and a feasible directional method. In this paper, we propose an adaptive branch and bound method, which is a coarse to fine positioning technique, to resolve it. The proposed heuristic algorithm significantly outperforms the previous algorithms for the optimum solution. Although the adaptive branch and bound algorithm is heuristic and does not guarantee a global optimum, we give a theoretical reason why it provides a superior local minimum to both the TOL-M and the feasible directional method. Issues of the relationship between yield and reliability index are addressed. Various related results in the literature are compared with ours to show that some reliability indices are less important than others and can be violated without suffering too much loss of yield.     

16MnR钢在硝酸盐环境下应力腐蚀性能试验研究

用楔形张开加载(WOL)预裂纹试样研究了16MnR钢在硝酸盐环境中的抗应力腐蚀性能。测定了16MnR钢母材及焊接接头在硝酸盐环境下的临界应力强度因子KISCC和应力腐蚀裂纹扩展速率；同时研究了缓蚀剂、材质、溶液温度对16MnR钢应力腐蚀开裂的影响，结果表明，缓蚀剂谈明显的降低应力腐蚀敏感性，增加裂纹的孕育时间；焊接接头与母材相比，由于没有元素Ni的不良作用，具有较高的应力腐蚀抗力；16MnR钢的硝酸盐应力腐蚀敏感性随温度升高而降低。     

混沌野草扩展Kalman滤波器辨识六维力传感器下E膜模型参数

为解决动载环境下噪声污染导致六维力传感器测量精度急剧下降,以及扩展卡尔曼滤波器难以获得最优系统干扰矩阵的问题,提出了一种基于混沌野草算法优化的扩展卡尔曼滤波（CIWO-EKF）算法。根据挠度与应变之间的关系,构建了六维力传感器下E膜非线性模型。基于野草繁殖算法,以前6阶主振型信息构成的系统干扰阵为均值进行高斯采样,产生初始化的可行解。将混沌搜索技术与野草算法相融合,利用野草算法进行全局搜索,通过混沌序列对群体中适应度高于平均值的个体执行给定步数的局部搜索,指导种群向最优解方向逼近,避免搜索过程陷入局部最优。采用改进的野草算法对扩展Kalman滤波中的系统干扰矩阵进行优化处理。仿真实例表明,改进扩展卡尔曼滤波器在提高六维力传感器测量精度的同时,可以保持较好的稳定性和鲁棒性。     

Optimal Disassembly Sequence Using Genetic Algorithms Considering Economic and Environmental Aspects

In this paper, a genetic algorithm (GA)-based approach for an optimal disassembly sequence considering economic and environmental aspects is presented. All feasible disassembly sequences are generated by a disassembly tree or an AND/OR graph. Using the disassembly precedence and the disassembly value matrix, a disassembly sequence is optimised. The precedence of disassembly is determined through a disassembly tree or an AND/OR graph and the value of disassembly is induced by considering both economic and environmental aspects in the disassembly, recycling, and disposal phases. Economic and environmental factors can be compared by the same measure through converting environmental factors into economic cost. To solve the disassembly sequence problem, a heuristic algorithm based on GAs is developed. The proposed GA can search for and dynamically explore the disassembly node through the highest disassembly value, keeping their precedence in order to identify an optimal disassembly sequence. It can also help to explore the search space, and an optimal solution can be obtained by applying the optimisation criteria. A refrigerator is used as an example to illustrate the procedure.     

Functional observer design using linear matrix inequalities

It is shown that the problem of observer design for estimating a set of linear combinations of state variables of a plant can be formulated in terms of linear matrix inequalities. An algorithm for constructing functional observers is proposed based on a nonsingular transformation of a plant model in the state space by matrix canonization with subsequent solution of the system of linear matrix inequalities.     

An approach to multi-criteria assembly sequence planning using genetic algorithms

This paper focuses on multi-criteria assembly sequence planning (ASP) known as a large-scale, time-consuming combinatorial problem. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. Genetic algorithm (GA) is the most widely known type of evolutionary computation method, incorporating biological concepts into analytical studies of systems. In this research, an approach is proposed to optimize multi-criteria ASP based on GA. A precedence matrix is proposed to determine feasible assembly sequences that satisfy precedence constraints. A numerical example is presented to demonstrate the performance of the proposed algorithm. The results of comparison in the provided experiment show that the developed algorithm is an efficient approach to solve the ASP problem and can be suitably applied to any kind of ASP with large numbers of components and multi-objective functions.     

基于CCD激光测微传感器的螺旋焊管周长在线检测技术

针对大尺寸螺旋焊管周长在线检测的难题,提出了一种基于CCD激光测微传感器的技术方案.该方案利用4组CCD测微传感器对螺旋焊管截面2个正交外径进行数据采集,结合螺旋焊管卷焊速度,构造截面点坐标,通过最小二乘椭圆算法对焊管截面圆进行拟合,计算出截面椭圆的周长,作为该段螺旋焊管中心截面周长的最优估计值.搭建了一套实验系统,对...