基于UG的拖拉机大灯注塑模具设计

以拖拉机大灯为例,分析了UG注塑模具建模的设计方法和工艺方案,建立了拖拉机大灯模型,并完成了拖拉机大灯注塑模具的设计。研究结果表明:经过UG模拟开模出来的塑料件没有出现气泡、变形、缺料和银线等缺陷,整体也没有发现熔合条纹,设计的拖拉机大灯注塑模具满足设计需求。

基于UG动画仿真的喷雾机喷杆形变控制优化

在植保作业领域,为预防大田作物的病虫害,传统农业生产存在农药使用粗放、滥用问题,对农村生产、生活、生态“三生同步”工作开展造成了十分不利的影响。为进一步提高农药使用合理性,顺应绿色农业、精确农业、生态农业的发展理念,宽幅喷杆喷雾机逐渐具备了变量喷雾的技术特征。在生产实践中,耕地表面的地形、地势等不确定因素对喷杆喷雾机喷药架造成的振动、歪斜、变型等影响会明显降低喷雾覆盖精度,导致植保质量降低。为此,以我国生产常用的28m幅宽喷药架为研究对象,分析了喷药架的形变类型,利用UG软件仿真了喷药架变形后对喷雾质量的影响,并在分析动画仿真数据基础上有针对性地进行了喷药架结构优化与技术升级,且优化后的喷药架对于多种地形的适应性均得到提升。

基于UG二次开发的多材料零件3D打印建模方法研究

随着制造业的不断创新与改进,多材料三维(Three-Dimensions,3D)打印工艺的重要性日益显现,多材料建模技术是3D打印工艺的核心模块。本研究提出一种基于UG二次开发的多材料3D打印建模方法。首先利用现有3D建模软件UG(Unigraphics NX)创建待打印零件的3D模型,通过包围盒原理及交集算法,对3D模型所处的材料空间微分化;其次通过UG对其单个或局部小立方体添加颜色,将颜色信息通过材料颜色映射表映射为材料信息,输出多材料模型文件;最后将3D模型导入材料空间,打印出满足功能要求与外观要求的多材料零件。该建模方法可为多材料3D打印建模技术研究提供参考。

基于UG的发动机轻量化设计与节能优化研究

机体轻量化是优化发动机能量利用的主要手段之一.为合理节约能源利用量,针对基于UG的发动机轻量化设计与节能优化方法展开研究.设置UG/Post Builder后置处理器,针对相关参数进行求解,完成基于UG的发动机设计模块开发.控制机体壁厚,在此基础上减少螺栓个数,并改用轻量化曲轴箱,在保证机体高压密封状态的同时,完成发动机组件的轻量化设计.仿真实验结果表明,依照轻量化方法所设计的发动机机体自重相对较小,不会因机体过重而造成能源过量消耗的问题.

基于局部剖视图的UG软件应用技巧

本文介绍了利用UG软件创建二维、立体局部剖视图的方法,研究总结了几何体创建具有相同剖切分界线的二维、立体局部剖视图的应用技巧和符合国家标准的局部剖视图表达方法。局部剖视图相同剖切分界线的创建,分界线在二维、立体视图中的切换,是整个过程比较关键的操作,关系到视图能否创建成功。通过本文介绍的方法,绘图者可以轻松实现正交的局部剖、轴测局部剖视图的绘制,熟练掌握后可以大大提高绘图的工作效率,为绘图工作提供方便。

基于轴类零件的SolidWorks与UG NX的建模方法比较

UG NX与SolidWorks是目前机械设计中较常用的三维设计软件,而轴类零件也是机器中很常见的零件。文章多角度比较用两种软件进行轴类零件造型时,绘制草图和运行特征命令的先后顺序不同对造型过程和结果的影响,同时也比较了轴类零件上常见结构如退刀槽、键槽的不同造型方法。对于两种软件在轴类零件造型中的不足提出了解决办法。

基于UG12.0自动编程的复杂槽类零件数控铣削仿真与加工

计算机辅助设计与制造技术是制造业的趋势和方向。对于轮廓形状复杂的零件,手动数控编程技术呈现出适应能力差,一定程度制约我国制造业质量水平的快速发展。以复杂槽类零件特征分析为基础,采用UG12.0 CAD模块开展复杂槽类零件三维建模,应用CAM模块设置刀具、建立坐标系、创建几何体、建立加工工序、设置刀轨参数及切削层参数,开展复杂槽类零件自动编程,使用仿真加工功能验证程序的正确性,后处理导出569型Oi mate MD加工中心能识别的处理器为“FANUC-BJ”的程序。将程序导入数控系统,安装毛坯和刀具,对刀找中心点,将两把刀具的长度补偿输入参数表中,最后进行加工生产。实践表明,对于复杂槽类零件,采用UG12.0进行自动编程、数控铣削仿真和生产加工是可行的。

混合式教学在中职UG三维造型设计课程中的应用研究

为革新中职院校的课堂教学模式,提升课堂教学效果,现以混合式教学在中职UG三维造型设计课程的应用为研究对象,采用调查研究法,得到了中职UG三维造型设计课程的现状以及存在的问题,在研究“线上+线下”教学设计基础上,提出“以学生为中心”和“以学习活动为中心”的课堂教学策略,并依托“超星学习通”平台,设计了“线上+线下”混合式教学新模式,并将该模式运用于中职UG三维造型设计课程中。研究结果表明,该模式提升了中职课堂教学效果,为中职课堂教学模式的改革提供参考。

基于UG NX的数控多轴加工工艺改良和配套夹具设计

首先,简要介绍了UG NX编程软件的相关知识;其次,以金属半环和电机外壳为研究对象,分析了其原有的数控加工工艺并提出了优化改进措施;再次,利用UG对研究对象进行了配套工装的仿真设计;最后,利用UG编制研究对象的目标零件多轴数控加工程序,有效保障了产品的加工质量,节省了人力、物力成本,提高了生产效率。

Integrated analysis of plasma rotation effect on HL-3 hybrid scenario

The hybrid scenario,which has good confinement and moderate MHD instabilities,is a proposed operation scenario for international thermonuclear experimental reactor(ITER).In this work,the effect of plasma rotation on the HL-3 hybrid scenario is analyzed with the integrated modeling framework OMFIT.The results show that toroidal rotation has no obvious effect on confinement with a high line averaged density of n_(bar)~(7)×10^(19)m^(-3).In this case,the ion temperature only changes from 4.7 keV to 4.4 keV with the rotation decreasing from 10^(5) rad/s to 10^(3) rad/s,which means that the turbulent heat transport is not dominant.While in the scenarios characterized by lower densities,such as n_(bar)~4×10^(19)m^(-3),turbulent transport becomes dominant in determining heat transport.The ion temperature rises from 3.8 keV to 6.1 keV in the core as the rotation velocity increases from 10^(3) rad/s to 10^(5) rad/s.Despite the ion temperature rising,the rotation velocity does not obviously affect electron temperature or density.Additionally,it is noteworthy that the variation in rotation velocity does not significantly affect the global confinement of plasma in scenarios with low density or with high density.

Freeze-thaw cycles and associated geomorphology in a post-glacial environment:current glacial,paraglacial,periglacial and proglacial scenarios at Pico de Orizaba volcano,Mexico

The glacial history of Pico de Orizaba indicates that during the Last Glacial Maximum,its icecap covered up to~3000 m asl;due to the air temperature increasing,its main glacier has retreated to 5050 m asl.The retraction of the glacier has left behind an intense climatic instability that causes a high frequency of freeze-thaw cycles of great intensity;the resulting geomorphological processes are represented by the fragmentation of the bedrock that occupies the upper parts of the mountain.There is a notable lack of studies regarding the fragmentation and erosion occurring in tropical high mountains,and the associated geomorphological risks;for this reason,as a first stage of future continuous research,this study analyzes the freezing and thawing cycles that occur above 4000 m asl,through continuous monitoring of surface ground temperature.The results allow us to identify and characterize four zones:glacial,paraglacial,periglacial and proglacial.It was found that the paraglacial zone presents an intense drop of temperature,of up to~9℃ in only sixty minutes.The rock fatigue and intense freeze-thaw cycles that occur in this area are responsible for the high rate of rock disintegration and represent the main factor of the constant slope dynamics that occur at the site.This activity decreases,both in frequency and intensity,according to the distance to the glacier,which is where the temperature presents a certain degree of stability,until reaching the proglacial zone,where cycles are almost non-existent,and therefore there is no gelifraction activity.The geomorphological processes have resulted in significant alterations to the mountain slopes,which can have severe consequences in terms of risk and water.

Abnormal Action Detection Based on Parameter-Efficient Transfer Learning in Laboratory Scenarios

Laboratory safety is a critical area of broad societal concern,particularly in the detection of abnormal actions.To enhance the efficiency and accuracy of detecting such actions,this paper introduces a novel method called TubeRAPT(Tubelet Transformer based onAdapter and Prefix TrainingModule).Thismethod primarily comprises three key components:the TubeR network,an adaptive clustering attention mechanism,and a prefix training module.These components work in synergy to address the challenge of knowledge preservation in models pretrained on large datasets while maintaining training efficiency.The TubeR network serves as the backbone for spatio-temporal feature extraction,while the adaptive clustering attention mechanism refines the focus on relevant information.The prefix training module facilitates efficient fine-tuning and knowledge transfer.Experimental results demonstrate the effectiveness of TubeRAPT,achieving a 68.44%mean Average Precision(mAP)on the CLA(Crazy LabActivity)small-scale dataset,marking a significant improvement of 1.53%over the previous TubeR method.This research not only showcases the potential applications of TubeRAPT in the field of abnormal action detection but also offers innovative ideas and technical support for the future development of laboratory safety monitoring technologies.The proposed method has implications for improving safety management systems in various laboratory environments,potentially reducing accidents and enhancing overall workplace safety.

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

Physical-Layer Secret Key Generation for Dual-Task Scenarios

Physical-layer secret key generation(PSKG)provides a lightweight way for group key(GK)sharing between wireless users in large-scale wireless networks.However,most of the existing works in this field consider only group communication.For a commonly dual-task scenario,where both GK and pairwise key(PK)are required,traditional methods are less suitable for direct extension.For the first time,we discover a security issue with traditional methods in dual-task scenarios,which has not previously been recognized.We propose an innovative segment-based key generation method to solve this security issue.We do not directly use PK exclusively to negotiate the GK as traditional methods.Instead,we generate GK and PK separately through segmentation which is the first solution to meet dual-task.We also perform security and rate analysis.It is demonstrated that our method is effective in solving this security issue from an information-theoretic perspective.The rate results of simulation are also consistent with the our rate derivation.

基于UG的塑料模型芯数控铣编程

先对模具型芯的结构进行分析,并区分型芯上的成型面、分型面、流道、推杆孔、斜顶孔、定位凸台等结构,为了设计合理的刀路,将推杆孔、斜顶孔等不需要用数控铣床切削的孔位封堵。根据型芯的结构,先用“型腔铣”命令编写粗加工刀路,再用“剩余铣”命令编写残余刀路,然后用“等高铣”命令编写半精加工刀路。在编写粗加工和半精加工刀路时,可以将各种结构合在一起编程,但在编写精加工刀路时,应针对不同的结构编写不同的精加工刀路:对曲面编写平行铣刀路;对斜面编写等高切削刀路;对根部小圆角编写清根刀路;对流道用成形刀按外形刀路往复切削。

基于UG NX软件往复铣削不抬刀的程序研究

针对往复铣削过程中的重复抬刀问题进行分析,结合UG/NX12.0软件建立三维模型编制刀路轨迹进行研究,主要提供了三种方法,并进行了比对,实现了面铣加工中不抬刀,缩短了加工时间,减少了机床的不必要移动,提高了生产效率,对实际加工有着重要的意义。

基于UG的吸乳护罩模具设计

运用UG软件对吸乳护罩进行实体造型,并对拔模角度和壁厚均匀程度进行分析,设计了吸乳护罩模具。针对护罩圆盖内壁的4个扣位,设计了由一个燕尾槽块带动4个抽芯的组合滑块结构,并根据模具结构设计了冷却效果良好的冷却水路。经批量生产证明:模具结构合理,脱模动作可靠。

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Application of Patient Simulators Combined with Internet plus Scenario Simulation Teaching Models on Intravenous Infusion Nursing Education in China

Objective: To explore the effectiveness of applying patient simulators combined with Internet Plus scenario simulation teaching models on intravenous (IV) infusion nursing education, and to provide scientific evidence for the implementation of advanced teaching models in future nursing education. Methods: Enrolled 60 nurses who took the IV infusion therapy training program in our hospital from January 2022 to December 2023 for research. 30 nurses who were trained in traditional teaching models from January to December 2022 were selected as the control group, and 30 nurses who were trained with simulation-based teaching models with methods including simulated patients, internet, online meetings which can be replayed and scenario simulation, etc. from January to December 2023 were selected as the experimental group. Evaluated the learning outcomes based on the Competency Inventory for Nursing Students (CINS), Problem-Solving Inventory (PSI), comprehensive learning ability, scientific research ability, and proficiency in the theoretical knowledge and practical skills of IV infusion therapy. Nursing quality, the incidence of IV infusion therapy complications and nurse satisfaction with different teaching models were also measured. Results: The scientific research ability, PSI scores, CINS scores, and comprehensive learning ability of the experimental group were better than those of the control group (P 0.05), and their assessment results of practical skills, nursing quality of IV infusion therapy during training, and satisfaction with teaching models were all better than those of the control group with statistical significance (P < 0.05). The incidence of IV infusion therapy complications in the experimental group was lower than that in the control group, indicating statistical significance (P < 0.05). Conclusions: Teaching models based on patient simulators combined with Internet Plus scenario simulation enable nursing students to learn more directly and practice at any time and in any place, and can improve their proficiency in IV infusion theoretical knowledge and skills (e.g. PICC catheterization), core competencies, problem-solving ability, comprehensive learning ability, scientific research ability and the ability to deal with complicated cases. Also, it helps provide high-quality nursing education, improve the nursing quality of IV therapy, reduce the incidence of related complications, and ensure the safety of patients with IV therapy.