矿物质粉体对砂浆及混凝土Cl-渗透性的影响

研究了不同水胶比、不同矿物质粉体掺量的砂浆和混凝土，经标准养护至56天、90天时的导电量。在相同水胶比和相同矿物质粉体掺量下，混凝土的导电量远低于砂浆的导电量。含矿物质粉体的砂浆及混凝土的导电量均低于基准砂浆及混凝土的导电量。导电量随水胶比的降低而降低，也随龄期的增长而降低。     

Multi-objective Optimization of Sheet Metal Forming Die Using Genetic Algorithm Coupled with RSM and FEA

Present study describes the approach of applying response surface methodology (RSM) with a Pareto-based multi-objective genetic algorithm to assist engineers in optimization of sheet metal forming. In many studies, finite element analysis and optimization technique have been integrated to solve the optimal process parameters of sheet metal forming by transforming multi-objective problem into a single-objective problem. This paper aims to minimize objective functions of fracture and wrinkle simultaneously. Design variables are blank-holding force and draw-bead geometrical parameters (length and diameter). RSM has been used for design of experiment and finding relationship between variables and objective functions. Forming limit diagram has been used to define objective functions. Finite element analysis applied for simulating the process. Proposed approach has been investigated on a fuel tank drawing part and it has been observed that it is more effective and accurate than traditional finite element analysis method and the “trial and error” procedure.     

Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization

The optimal truss design using problem-oriented evolutionary algorithm is presented in the paper. The minimum weight structures subjected to stress and displacement constraints are searched. The discrete design variables are areas of members, selected from catalogues of available sections. The integration of the problem specific knowledge into the optimization procedure is proposed. The heuristic rules based on the concept of fully stressed design are introduced through special genetic operators, which use the information concerning the stress distribution of structural members. Moreover, approximated solutions obtained by deterministic, sequential discrete optimization methods are inserted into the initial population. The obtained hybrid evolutionary algorithm is specialized for truss design. Benchmark problems are calculated in numerical examples. The knowledge about the problem integrated into the evolutionary algorithm can enhance considerably the effectiveness of the approach and improve significantly the convergence rate and the quality of the results. The advantages and drawbacks of the proposed method are discussed.     

金属超声振动塑性成形技术研究现状及其发展趋势

金属超声振动塑性成形,由于能够有效降低设备成形力,提高材料成形极限,改善成形零件质量,在塑性成形领域逐渐发挥了重要作用。介绍了金属超声振动塑性成形的原理与主要特点,以及理论机制中"体积效应"和"表面效应"的研究进展。综述了超声振动在铝/镁合金等轻合金塑性成形工艺中应用的研究现状。提出了目前金属超声振动塑性成形在理论和应用研究中存在的主要问题,并对该技术未来的发展趋势进行了展望。     

Quantum-Inspired Evolutionary Algorithm for Topology Optimization of Modular Cabled-Trusses

This article proposes an alternative optimization framework applied to topology optimization of modular lightweight cabled-truss structures. These structures are described as a system of intrinsically positioned cables and triangular bar formations jointed at their ends by hinged connections to form a rigid framework. The optimized topologies are determined through a stochastic discrete optimization procedure that uses ground structure approach, nonlinear finite element analysis, and quantum-inspired evolutionary algorithms. The optimization searches for optimal mass reduction with minimal losses in stiffness, such relation, is expressed by the stiffness-to-mass ratio parameter. Nonlinear finite element analysis is used to evaluate the static structural response. In order to decrease computation time, kinematically instable and structurally invalid individuals are filtered before evaluation. Modular design approach is taken into consideration to reduce the number of design variables and increase the productibility of cabled-trusses. Symmetric structural response is desired since in several mechanical applications forces can assume different directions during the working cycle. A modular ground structure with 300 elements is optimized, and optimal truss and cabled-truss topologies are compared. Complementary analyses comprise the investigation of the structural performance under different number of modules and slenderness ratios. The results indicate that the proposed optimization framework leads to optimized structures. In addition, it was observed that cabled trusses presented significant improvements in structural performance when compared with trusses.     

基于NSGA-II遗传算法的磁流变悬置多目标优化

磁流变悬置集总参数优化是设计高性能发动机悬置的关键。为克服以往悬置优化中优化目标单一、优化目标选取不合理、未考虑实际加工可行性等问题,建立单自由度磁流变悬置隔振系统数学模型,提出倍程区间灵敏度分析法,对各集总参数灵敏度进行分析,并以此为依据选取优化变量。以发动机常用转速激振频率段的力传递率积分为优化目标,采用改进型非支配排序遗传算法(NSGA-II)进行多目标优化。在一定范围内将结构尺寸进行离散化处理,计算各组离散尺寸对应的集总参数值,以离散集总参数与集总参数Pareto非劣解之间的综合距离为准则筛选最优解。     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

板料冲压成形中坯料优化设计方法研究

较为系统地介绍了冲压件坯料的优化设计方法,并比较分析了其各自的优缺点.相比于其他方法,一步模拟方法效率高、适应面广、功能较强,可以在产品的设计之初提供较为丰富的信息.因此,对其作了较为详细的论述.实例证明,一步模拟法可用于板料成形工艺方案的快速评价及参数优化.     

带调整时间的多目标流水车间调度的优化算法

为高效地求解带调整时间的多目标流水车间调度问题,提出了一种多目标混合遗传算法,此算法依据基于Pareto优于关系的个体排序数和密度值计算适应度,保持解的多样性,并采用非劣解并行局部搜索策略,提高算法的搜索效率.此外,引入精英策略保证算法的收敛性,在进化过程中通过淘汰掉个别最差个体,进一步加快解的收敛速度.仿真结果表明,新算法能够有效地解决带调整时间的多目标流水车间调度问题.     

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.     

Reliability Based Economical Optimization of Sheet Metal Forming Processes

Titanium and its alloys have rapidly become a strategic material in aerospace industry due, for example, to high strength vs. weight ratio, excellent mechanical proprieties, corrosion resistance and galvanic compatibility with fiber reinforced composite materials. A big problem that has to be considered is, however, the higher cost of components as compared to other alloys. In fact, the market price of the base metal has to be added to manufacturing ones. To reduce production costs, Titanium net shape technologies have been continuously developed. Superplastic forming (SPF) technology can be considered in this frame. Through the SPF some alloys can be slowly formed well beyond their normal limitations at elevated temperatures, allowing very deep forming sequences. In this study, the SPF process to produce aircraft parts was investigated. The well-known Titanium alloy Ti6Al4V was chosen as workpiece material. The investigation was carried out by numerical analysis to check the predicting capability of these simulation tools. In detail, FE analyses were developed using two different codes, namely Marc and Ls-Dyna. The aim was the optimization of pressure trend to obtain the desired shape with a reasonable accuracy and without material breaking. Finally, some considerations on the experimental manufacturing of the components are reported.     

基于粒子群与聚类的多目标优化算法

针对粒子群优化算法容易陷入局部最优的问题,提出了一种基于粒子群优化与分解聚类方法相结合的多目标优化算法。算法基于参考向量分解的方法,通过聚类优选粒子策略来更新全局最优解。首先,通过每条均匀分布的参考向量对粒子进行聚类操作,来促进粒子的多样性。从每个聚类中选择一个具有最小聚合函数适应度值的粒子,以平衡收敛性和多样性。动态更新全局最优解和个体最优解,引导种群均匀分布在帕累托前沿附近。通过仿真实验,与4种粒子群多目标优化算法进行对比。实验结果表明,提出的算法在27个选定的基准测试问题中获得了20个反世代距离(IGD)最优值。     

应用蜜蜂繁殖进化型粒子群算法求解车辆路径问题

为了提高粒子群算法求解车辆路径问题时收敛速度和全局搜索能力,将蜜蜂繁殖进化机制与粒子群算法相结合,应用到CVRP问题的求解。该算法中,最优的个体作为蜂王与通过选择机制选择的雄蜂以随机概率进行交叉,增强了最优个体信息的应用能力;同时,随机产生一部分雄蜂种群,并将其与蜂王交叉增加了算法的多样性。实例分析表明该算法具有较好的全局搜索能力,验证了该算法的可行性。     

IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version. The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems. The outcomes of IRKO are compared with seven state-of-the-art algorithms, including the basic RKO algorithm. Compared to other algorithms, the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems. The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems, including real-world optimization problems.     

一种基于蜜蜂进化选择算子的布局遗传算法

三维矩形布局问题属于NP难问题,对于三维矩形布局问题的求解大多依赖于各种启发式算法.该文以布局物体体积递减为定序规则,结合布局物体在布局空间中的几何可行域,以吸引子法为定位规则,利用蜜蜂进化型遗传算法优化吸引子函数中的参数来求解三维矩形布局问题（BEGA）,得到新型布局遗传算法.最后对不同的算例进行了计算,并与以标准比例选择作为选择算子的传统布局遗传算法（SPGA）等对比证明了该算法的有效性.     

An Evolutionary Lagrange Method For Mixed-Integer Constrained Optimization Problems

This study proposes a method for solving mixed-integer constrained optimization problems using an evolutionary Lagrange method. In this approach, an augmented Lagrange function is used to transform the mixed-integer constrained optimization problem into an unconstrained min—max problem with decision-variable minimization and Lagrange-multiplier maximization. The mixed-integer hybrid differential evolution (MIHDE) is introduced into the evolutionary min—max algorithm to accomplish the implementation of the evolutionary Lagrange method. MIHDE provides a mixed coding to denote genetic representations of teal and integer variables, and a rounding operation is used to guide the genetic evolution of integer variables. To fulfill global convergence, self-adaptation for penalty parameters is involved in the evolutionary min—max algorithm so that small penalty parameters can be used, not affecting the final search results. Some numerical experiments are tested to evacuate the performance of the proposed method. Numerical experiments demonstrate that the proposed method converges to better solutions than the conventional penalty function method     

基于多目标粒子群算法的多传感器图像融合

针对图像融合中参数优化的问题,提出了一种基于多目标粒子群优化算法的多传感器图像融合方法。首先采用非采样Contourlet变换(NSCT)对源图像进行多尺度、多方向分解;然后选取图像融合的客观评价指标为优化目标函数,采用多目标粒子群优化算法对低频系数的融合参数进行优化,带通方向子带系数采用取绝对值最大的融合规则;最后通过NSCT逆变换得到融合图像。分别对多聚焦图像融合和红外与可见光图像进行融合实验,并对融合图像进行主客观评价,实验结果表明,得到的融合图像具有较好的主观视觉效果和客观评价指标。     

基于PSO–BP–GA混合算法的激光熔覆工艺多目标优化

目的 为了提高镍基高温合金熔覆涂层的综合质量,提出了一种基于PSO–BP–GA混合算法的激光熔覆工艺优化方法。方法 选取工艺参数（激光功率、扫描速度、送粉速率）为优化变量、熔覆层质量（稀释率、显微硬度、热影响区深度）为优化目标,根据正交试验结果建立PSO–BP神经网络预测模型,采用线性加权法和层次分析法建立熔覆层质量的综合评价体系,结合GA算法探寻综合质量最优的工艺参数组合。结果 PSO–BP神经网络模型预测值与试验值之间的相对误差不超过6%,最优工艺参数组合如下：激光功率为2 158 W、扫描速度为10.4 mm/s、送粉速率为2.9 r/min,其熔覆层稀释率降低了70.4%、显微硬度增大了25.4%、热影响区深度减少了41.8%。结论 该算法为制备出高性能镍基高温合金熔覆涂层提供了一定的参考与借鉴。     

基于双向渐进结构拓扑优化算法的创新设计研究

目的 在数字化设计的背景下,探索基于结构性能化的算法找形方法,运用双向渐进结构拓扑优化算法(Bi-Directional Evolutionary Structural Optimization,BESO)展开创新设计实践研究。方法 在理解双向渐进结构拓扑优化算法的基本内涵、相关理论、历史发展和现状应用的基础上,分析其算法生成的优势及可行性,并以算法的组织模式与生形原理为前提,对其进行几何划分、约束条件、优化技术、结构模拟、材料设定、迭代生形等内容协同一体的生成策略研究,提供了多元选择的设计机会。结果 得到了运用双向渐进结构拓扑优化算法进行的基于初始形态设计、拓扑优化设计和后处理与制造三步骤创新设计实践结果。结论 此设计实践方案验证了该算法生成方法的设计应用可行性,同时也为多领域应用该算法提供了新思路和新方向。     

难变形金属小模数齿形件电流辅助分形旋压成形工艺优化

目的 针对30CrMnSiA合金结构钢小模数齿形件电流辅助分形旋压成形工艺,探究工艺参数对小模数齿形件成形质量的影响规律,分析工艺参数间的相关性,获得优化后的工艺参数组合。方法 选取小模数齿形件齿廓偏差、齿距偏差、轮齿饱和度为成形质量响应指标,以电流密度、占空比以及旋轮进给速度为影响因素,设计响应面试验方案;根据有限元模拟结果建立成形质量回归预测模型,分析电流密度、占空比以及旋轮进给速度对小模数齿形件成形质量的影响规律,计算获得最优的参数组合并进行试验验证。结果 电流密度、脉冲占空比、旋轮进给速度对小模数齿形件成形质量有显著影响,成形质量随着电流密度与占空比的升高而升高,随旋轮进给速度的升高而先升高后降低;且电流密度与旋轮进给速度、脉冲占空比与旋轮进给速度在对轮齿饱和度的影响上有一定的交互性;通过建立的回归模型预测得到的优化工艺参数如下:电流密度为17.5 A/mm²、脉冲占空比为40%、旋轮进给速度为0.6 mm/min。结论 回归模型预测值与试验值的相对误差小于8%,说明所构建的成形质量回归预测模型是准确的,利用优化后的参数可制备出成形质量良好的小模数齿形件。