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张栋 , 杨培 , 黄哲轩 , 等 . 爬壁机器人悬摆式磁吸附机构的设计与优化 [J ] . 工程设计学报 , 2023 , 30 ( 3 ): 334 - 341 . DOI: 10.3785/j.issn.1006-754X.2023.00.031 http://doi.org/10.3785/j.issn.1006-754X.2023.00.031 为了解决爬壁机器人壁面适应能力差、运动灵活性低等问题，分析了现有爬壁机器人磁吸附机构存在的不足，并以轮式爬壁机器人为研究对象，根据爬壁机器人的功能要求，设计了一种壁面自适应悬摆式磁吸附机构。通过Ansoft软件对悬摆式磁吸附机构和传统磁吸附轮进行了对比分析。为了进一步减小磁吸附机构的质量，同时提高其吸附可靠性，基于高磁能利用率的目标，采用SNLP（sequential non-linear programming，连续非线性规划）算法对悬摆式磁吸附机构的结构参数进行优化，优化后磁吸附机构的吸附力增大了25.52%。研制了悬摆式磁吸附车轮样机，开展了吸附力测试实验和卸磁实验，并将其安装在爬壁机器人上，开展了运动性能测试实验，验证了磁吸附机构结构参数优化结果的合理性和结构设计的实用性。研究结果为提高爬壁机器人的工作性能提供了参考。

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贾晶晶 , 张治民 , 于建民 , 等 . 基于响应面法的轻质尾翼均匀挤压成形数值模拟及模具结构优化 [J ] . 兵工学报 , 2024 , 45 ( 6 ): 1824 - 1839 . DOI: 10.12382/bgxb.2023.0187 http://doi.org/10.12382/bgxb.2023.0187 针对轻质镁合金尾翼正挤压成形金属流动不均匀和力学性能各向异性大的问题,提出一种可调控金属均匀流动的金属蓄料池模具结构。基于Deform-3D有限元模拟分析,采用Box-Behnken响应曲面法以金属蓄料池结构参数:深度h、池壁距尾翼芯部型腔长度D₁、池壁距翼片型腔长度D₂和池壁与翼片型腔之间夹角α为变量,建立出模口金属流速均方差和成形载荷与金属蓄料池结构参数之间的响应关系。响应曲面分析结果表明:最佳的金属蓄料池结构参数为h=7mm,D₁=13mm,D₂=6mm,α=11°。采用优化后的模具结构参数进行模拟验证和物理试验,金属流动不均匀和力学性能各向异性均得到改善,并成功试制出形状尺寸和力学性能指标满足要求的尾翼形零件。

JIA J J , ZHANG Z M , YU J M , et al. Numerical simulation of uniform extrusion forming of lightweight tail based on response surface method and optimization of die structure [J ] . Journal of Military Engineering , 2024 , 45 ( 6 ): 1824 - 1839 . (in Chinese)

张鑫 , 邹德旋 , 肖鹏 , 等 . 自适应简化粒子群优化算法及其应用 [J ] . 计算机工程与应用 , 2019 , 55 ( 8 ): 250 - 263 . DOI: 10.3778/j.issn.1002-8331.1809-0183 http://doi.org/10.3778/j.issn.1002-8331.1809-0183 针对粒子群优化算法（Particle Swarm Optimization，PSO）寻优速度慢、收敛精度不高且搜索结果波动性较大的缺点，提出了一种自适应简化粒子群优化算法（Self-Adjusted Simplified Particle Swarm Optimization，SASPSO）。在每次迭代过程中，粒子只受全局最优解影响，且加入按一定规律分布的锁定因子，令粒子受影响的程度有规律性。同时，利用锁定因子和当前粒子位置令惯性权重自适应配置，更有效地利用惯性权重对粒子群优化算法的影响。引入4种近期提出的改进粒子群算法同时搜索不同维度时的18个基准函数，与SASPSO的搜索结果对比，并使用T-test进行差异性分析。为了进一步分析算法性能，统计5个改进算法搜索100维函数达到期望值时的成功率与平均迭代次数。实验结果证明，SASPSO在无约束问题寻优中的收敛速度、寻优精度有了明显提升，且搜索结果异常值较少，波动性弱。将SASPSO应用于机床主轴结构参数优化问题，结果显示SASPSO优化性能更好。

ZHANG X , ZOU D X , XIAO P , et al. Adaptive simplified particle swarm optimization algorithm and its applications [J ] . Computer Engineering and Applications , 2019 , 55 ( 8 ): 250 - 263 . (in Chinese) DOI: 10.3778/j.issn.1002-8331.1809-0183 http://doi.org/10.3778/j.issn.1002-8331.1809-0183 In order to overcome the disadvantages of slow search speed, low convergence accuracy and high search volatility of particle swarm optimization, a Self-Adjusted Simplified Particle Swarm Optimization（SASPSO） algorithm has been proposed. During the evolutionary process, the particles are only affected by the global optima, and the locking factor distributed according to a certain rule is added so that the degree of influence of the particles can be traced. At the same time, the inertia weight is adaptively adjusted by using the locking factor and the current particle position, which makes the influence of inertia weight on the particle swarm optimization algorithm more effective. Four improved particle swarm algorithms proposed recently are introduced to simultaneously search for eighteen benchmark functions with different dimensions. The results are compared and T-test is used to analyze their differences. The success rates and the average iteration times are recorded when five improved algorithms search for 100-dimensional functions, which is beneficial for analyzing the performance of the proposed algorithm. Experimental results show that the convergence speed and optimization accuracy of SASPSO have been improved significantly, and the search results have lower objective function values and smaller standard deviations. The SASPSO is applied to the optimum design of machine-tool spindle parameters. The results show that the SASPSO optimization performance is better.