A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper, a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed. Firstly, the invariable parameters in the forming process including shot size, mass flow, peening distance and peening angle are determined according to the empirical and machine type. Then, the optimal value of air pressure for the whole shot peening is selected by the experimental data. Finally, the feeding speed for every shot peening path is predicted by regression equation. The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object, and four experiments are conducted. In order to design specimens for acquiring the forming data, one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens, which were peened along the middle of the two stringers. The most striking finding of this experiment is that the outer shape error range is below 3.9%, so the plate specimens can be used in predicting feeding speed of the integral panel. The second experiment is performed and results show that when the coverage reaches the limit of 80%, the minimum feeding speed is 50 mm/s. By this feeding speed, the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object, and the optimal air pressure is 0.15 MPa. Then, the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment, and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method. The algorithm is validated by forming a test part and the average deviation is 0.496 mm. It is shown that the approach can realize the forming of the integral panel precisely.     

Modelling ignition probability with pairwise mixing-reaction model for flame particle tracking

Reduced order models for ignition analysis can offer insights into ignition processes and facilitate the combustor optimization. In this study, a Pairwise Mixing-Reaction (PMR) model is formulated to model the interaction between the flame particle and the surrounding cell mixture during Lagrangian flame particle tracking. Specifically, the model accounts for the two-way coupling of mass and energy between the flame particle and the surrounding shell layer by modelling the corresponding turbulent mixing, chemical reaction and evaporation process if present. The state of a flame particle, e.g., burnt, hot gas or extinguished, is determined based on particle temperature. This model can properly describe the ignition process with a spark kernel being initiated in a nonflammable region, which is of practical importance in certain turbine engines and has not been rigorously accounted for by the existing models based on the estimation of local Karlovitz number. The model is integrated into an ignition probability analysis platform and is demonstrated for a methane/air bluff-body flame with the flow and fuel/air mixing characteristics being extracted from a non-reacting simulation. The results show that for the spark location being at the extreme fuel-lean outer shear layer of the recirculation zone, PMR can yield ignition events with a significant number of active flame particles. The mechanisms for the survival of the initial flame particles and the entrainment of the survived flame particles into the recirculation zone are analyzed. The results also show that the ignition probability map from PMR agrees well with the experimental observation: a high ignition probability in the shear layer of the recirculation zone near the mean stoichiometric surface, and low ignition probabilities inside the recirculation zone and the top stagnation region of the recirculation zone. The parametric study shows that the predicted shape of the ignition progress factor and ignition probability is in general insensitive to the model parameters and the model is adequate for quantifying the regions with high ignition probabilities.     

航天先进轻合金材料及成形技术研究综述

轻合金是现代航天装备轻量化首选材料,高性能轻合金构件制造能力决定了我国航天装备的功能水平与竞争力。为推动先进轻合金材料及成形技术在航天领域的应用,对高性能轻合金材料、铸造、钣金成形、增材制造等技术领域在基础理论、工艺开发、装备研制、工程应用等方面的发展现状进行了梳理,提出了高强耐热铸造镁合金材料、高性能钛铝合金材料、高性能镁合金熔模精密铸造、数字化铸造、旋压成形、超塑成形、钛/铝合金电弧熔丝增材制造等相关技术的后续发展方向。     

基于数字孪生的智能脉动管控

为提升飞机总装脉动生产线智能化水平,开展了基于数字孪生的智能脉动管控研究。基于数字孪生五维模型理论构建了飞机总装脉动生产线数字孪生应用架构,从反应式计划调度、物流精准配送、智能作业指导、产线健康监视、资源迭代优化配置5个方面开展了技术研究,为复杂产品装配的智能制造应用提供参考。     

ZL205A合金舱体低压工艺设计及数值仿真应用

针对某ZL205A合金舱体铸件壁薄难充型、壁厚差异大、易产生裂纹等特点,设计了低压底注式浇注工艺。采用立筒缝隙式浇注系统分配充型过程中合金的流量,局部厚大部位放置成形冷铁加快该部位的冷却速度,以控制铸造缺陷的产生。利用ProCast数值仿真软件对工艺进行仿真计算,成功预测了缺陷的产生位置,对工艺方案进行优化,包括倒角过渡、改变型砂使用等,并在优化方案的基础上浇注了舱体铸件,铸件质量良好,没有缩松、缩孔、裂纹等缺陷,符合相关技术要求。     

基于随机维纳过程的航空发动机性能衰退研究

针对发动机下发间隔预测的问题，研究了发动机性能衰退规律。基于发动机排气温度裕度（EGTM）数据，建立了随机维纳模型，并使用严酷度因子修正模型，表征不同的工作条件对发动机的影响。利用期望最大化（EM）算法结合贝叶斯定理，对模型参数求解，最终得到了基于性能衰退的发动机平均下发间隔。研究结果表明，在严酷度因子为1.2696的使用条件下，某型号航空发动机性能衰退平均下发间隔为8219循环；基于该模型预测的发动机平均下发间隔与厂商建议的下发时间之间的误差仅为1.7%。     

云制造环境下机床装备资源选择方法

针对云制造环境下机床装备资源具有数量大、异质异构、服务制约因素多等问题，提出了基于多准则决策的机床装备资源选择方法。首先，利用本体论对机床装备资源建模，将物理资源虚拟化封装，实现虚拟资源与物理资源的逻辑映射。进一步地，使用语言变量量化决策者评估信息，采用模糊决策与试验评价实验室（FDEMATEL）和熵值法（EW）相结合的组合赋权法，引入模糊VIKOR（FVIKOR）多属性决策方法对机床装备资源进行综合评估，建立FDEMATEL-EW-FVIKOR的多准则混合决策模型。最后，将该方法应用于某生产制造企业，实验对比决策结果验证了多准则混合决策模型的可行性和有效性。     

基于产品结构的航天器AIT过程多维数据管理系统

文章为解决航天器AIT过程中数据集成度低、数据应用不佳等问题,从数据模型构建、数据集成等方向出发,分析了AIT过程数据在产品结构、时间周期、阶段型号3个维度的演变过程,构建了多维数据模型,并开发基于产品结构的多维数据管理系统。该系统以产品结构为核心,对AIT过程数据进行重新组织,定义不同系统间、不同维度数据之间的关联关系,实现了AIT过程全数据的结构化整合,在此基础上生成了AIT过程数据包。     

低温磨料气射流加工PDMS实验研究

通过自研的低温磨料气射流加工装置进行低温磨料气射流加工聚二甲基硅氧烷(polydimethylsiloxane,PDMS)实验研究,分析了加工时间、加工距离、冲蚀角度和磨料粒径对冲蚀率、孔深和孔横截面形貌的影响。结果表明:随着加工时间的增加,冲蚀率先增大后减小,在第1阶段加工过程中,孔深与加工时间大致呈线性关系,增加加工时间还可使孔底部变平整;存在一个最佳加工距离使孔深最大,当加工距离大于最大加工距离时,孔深将随着加工距离的增加而急剧下降,孔的锥度随着加工距离的增大而增大;当冲蚀角度处于30°～60°之间时,冲蚀率最大,随着冲蚀角度的增加,孔的形状逐步由椭圆形变成圆形;存在一个最佳磨料粒径,使冲蚀率和孔深达到最大;当冲蚀角度小于90°时,低温磨料气射流加工PDMS材料去除机理为塑性去除和脆性去除的结合。     

热塑性定型剂对干铺丝-RTM工艺及力学性能影响

为了研究热塑性定型剂的添加对干铺丝-树脂传递模塑（RTM）工艺的影响，采用PA-6作为热塑性定型剂，通过T-剥离确定定型剂的质量分数，结合试验和工艺仿真，研究了定型剂对预制体面内渗透率的影响，并对其充模过程进行工艺仿真和分析，在此基础上研究了定型剂对环氧复合材料制品层间性能及韧性的影响。渗透率试验和仿真结果表明:加入定型剂后，纤维预制体的沿纤维和垂直纤维的面内渗透率分别降低了55.5%和52.0%；工艺仿真结果与试验结果一致，不同渗透长度下的注胶时间偏差均小于10%。力学性能试验结果表明:加入热塑性定型剂可显著提升环氧树脂复合材料的韧性，Ⅰ型和Ⅱ型层间断裂韧性分别提高了89.3%和54.1%，冲击韧性提高了18.7%，复合材料的层间剪切强度降低了23.6%。