In the multi-objective optimization design of automotive seats based on Approximation-Based Design Optimization, a single approximation model may not adequately address the requirement of accurately fitting highly nonlinear feature data. For this reason, the Hybrid Approximation Models based on the Multi-Species Approximation Model (HAM-MSAM) is proposed to meet the requirement for high fitting accuracy. Subsequently, this study introduces a HAM-MSAM-based Approximation-Based Global Multi-Objective Optimization Design (ABGMOOD) strategy. This strategy is employed in the multi-objective optimization of the rear seat of a passenger car. HAM-MSAM was constructed from an experimentally validated finite element model and a training set generated through experimental design. The advantages of HAM-MSAM in capturing the highly nonlinear response under seat crash conditions were validated through comparison with hybrid model construction methods reported in existing literature. Finally, the optimization results obtained by the ABGMOOD strategy were compared to those of the classical local multi-objective optimization strategy, demonstrating the substantial advantages of the ABGMOOD optimization scheme in economy and weight reduction. In addition, the safety of the rear seats is slightly lower than that of the local optimization scheme but remains in compliance with regulatory requirements. The final optimized rear seat demonstrates notable improvements in safety, economy, and weight reduction, validating the feasibility of the ABGMOOD strategy and providing valuable insights for similar engineering optimization challenges.

This study explores the adoption of carbon fiber reinforced plastic (CFRP) in automotive seat backrests, aiming to improve compliance with regulatory standards and enhance performance. By replacing conventional materials with CFRP, and employing mechanical performance tests alongside the principle of equal stiffness, the study tackles material failure issues. A novel multi-criteria decision-making method, coupled with an optimal Latin hypercube experimental design, is used to optimize the layup sequence. The optimized design not only meets regulatory requirements but also significantly enhances driver and passenger safety and comfort during collisions.

In this paper, an improved grey Euclidean relational analysis method based on Euclidean distance was proposed, which was based on the traditional grey relational analysis method. This method was applied to the material-structure integrated design of the backrest skeleton of a car seat. Firstly, the steel backrest skeleton of a car seat was replaced by carbon fiber reinforced plastics (CFRP), and the backrest skeleton was designed as an integrated structure. Its weight was reduced by 1.69 kg (39.12 %) while still meeting various seat performance test requirements. Then, the effect of the ply design on the performance of car seats with CFRP was studied, and the optimal layer scheme of CFRP backrest skeleton of car seats was obtained by combining the grey Euclidean relational analysis method. Finally, the method proposed in this paper was compared with the widely used method. The results showed that the optimization schemes obtained by the two methods greatly improved performance indicators, especially L_R_2 which had an improvement rate of 13.31 %. Additionally, the proposed method reduced computing resources by 95 %, indicating its ability to effectively balance optimization results and calculation efficiency.

以某全复合材料无人机机翼为分析基础,提出了基于粒子群算法的无人机机翼结构优化设计方法.首先基于无人机设计要求及机翼主要技术指标,确定了三梁多肋式机翼结构;然后结合优化区域,采用基于粒子群算法的无人机机翼结构优化设计方法,确定了机翼最优结构布局;最后对优化前后的机翼结构仿真结果对比分析,结果表明机翼质量降低了 28.26％,且满足机翼强度、刚度性能设计要求.

随着智能制造产业不断转型升级,企业设备的自动化程度越来越高,对创新型、发展型、复合型人才以及高素质从业人员的需求越来越迫切.因此,健全新时代高质量职业人才培养体系,为实现学生更高质量就业和实体经济高质量发展将发挥重要作用.基于此,文章结合智能制造产业的发展需求、面临的机遇和挑战进一步审视新阶段人才培养的背景,以智能制造装备运维专业群建设为研究对象,以高技能人才培养为抓手,探索和实践了基于"岗课赛证融通"的智能制造专业群人才培养模式,为智能制造专业群高技能人才培养模式的创新提供参考.

[目的]为避免无人机飞行中出现严重的气动弹性问题,针对无人机机翼刚度分布设计不合理之处,开展无人机机翼模态分析与结构优化设计.[方法]基于正交试验设计提出一种基于模态分析的机翼变截面结构布局轻量化设计研究方案.[结果]基于无人机机翼有限元仿真模型,开展机翼约束模态仿真分析,发现机翼在翼梁、翼肋等方面需要进行尺寸优化设计,进而改善机翼刚度.并提出一种变截面翼梁结构,通过TOPSIS方法进行排序获取了最优解.结果表明,优化后的机翼结构质量降低34％,机翼约束模态频率得到极大改善.[结论]通过模态分析开展无人机机翼结构优化设计,可在满足刚度合理分布的同时,大幅度降低机翼总质量.

In order to improve the fitting accuracy and optimization efficiency of the surrogate model, a multi-response weighted adaptive sampling (MWAS) approach based on the hybrid surrogate model was proposed and implemented to a multi-objective lightweight design of car seats. In this approach, the sample discreteness index in the input design space was calculated by the maximum and minimum distance approach (MDA), the fitting uncertainty index of output response was calculated by a strategy based on the weighted prediction variance (WPV), and the two indices are combined by the weight coefficients. In the iterative process, the weight coefficients of the two indices were determined according to the accuracy of the hybrid surrogate model. The balance of global and local accuracy was realized by considering the sample dispersion and the fitting uncertainty of the surrogate model comprehensively. Numerical examples of single-response and multi-response systems showed that the proposed approach has excellent sampling efficiency and robustness. Moreover, the results of actual engineering application showed that the hybrid surrogate model constructed through MWAS could significantly improve the efficiency of model optimization. Hence, a high-precision optimization solution to the multi-objective lightweight design of passenger car rear seat was obtained.

提出第二代非劣排序遗传算法(NSGA-Ⅱ)结合响应面法(RSM)-径向基神经网络方法(RBF)混合近似模型和逼近理想解排序(TOPSIS)方法对某乘用车后排座椅进行结构-材料一体化多目标轻量化设计研究.结合有限元理论建立仿真模型,并通过行李箱碰撞试验验证仿真模型的正确性,根据工程经验和座椅靠背骨架吸能分析确定了6个优化部件厚度、材料的设计变量及取值范围;采用RSM-RBF混合近似模型方法拟合设计变量与响应之间的关系;利用NSGA-Ⅱ算法对优化问题进行求解,得到Pareto最优解集.最后采用基于熵权TOPSIS方法对Pareto最优解集进行排序确定最佳折中解.结果表明:在满足各项安全性能法规的前提下,乘用车后排座椅减重3.57 kg.

At present, the safety performance and lightweight of passenger car seat has become more and more important. The multi-objective lightweight optimization of the passenger car seat frame is carried out in this study. The novelty of this study is that we propose a detailed optimization design method and a design process in lightweight optimization for passenger car seat. In more detail, firstly, according to the ratio of energy absorption to mass method, the lightweight design components are selected. Secondly, different seat safety tests are conducted to optimize the corresponding components by considering both continuous thickness variables and discrete material variables. Thirdly, the strain index, the displacement of key points, the material cost, and the total mass of the components which need to be optimized (opti-components) are considered objectives. After that, the grey relational analysis (GRA) is adopted to optimize the material thickness scheme of the lightweight design components. Besides, the optimized coefficient of variation (OCV) method is applied to evaluate the corresponding weighting values of the objectives. Meanwhile, the availability of the grey relational analysis and optimized coefficient of variation (GRA&OCV) is assessed through comparing advantages among the method of GRA&OCV, the grey relational analysis and coefficient of variation (GRA&CV), the technique for order preference by similarity to ideal solution and coefficient of variation (TOPSIS&CV), as well as the technique for order preference by similarity to ideal solution and optimized coefficient of variation (TOPSIS&OCV) in the multi-objective lightweight optimization of passenger car seat frame. As a result, the total material cost and mass of the passenger car seat frame are reduced by 17.00% and by 2.08 kg (12.46%), respectively, with guaranteed vibrational and safety performance. Therefore, GRA&OCV can be effectively applied in multi-objective lightweight optimization of the passenger car seat frame.