针对未来信息化战争条件下航天复杂装备维修保障面临的型号种类多、模块可视化程度低、战时保障前后端衔接不足、人员训练量化评估考核手段缺乏、维修大数据难以挖掘和复用等瓶颈问题,以典型武器装备为对象,提出一种基于增强现实技术、数字孪生内核驱动的维修保障与人员训练系统框架,阐述了数字孪生模型构建、故障在线诊断、维修流程诱导、人员动态评估等关键环节的技术途径,论述了航天复杂装备辅助维修系统的开发与集成要点并展示了原型系统,为实现靠前保障、快速保障提供了可行的技术路径.

机器可读标准是当前国内外标准数字化转型的发展方向和研究重点.本文从机器可读标准的基本概念出发,阐述机器可读标准的基本定义和发展阶段,提出机器可读标准在航天智能制造中的应用方法,最后以运载火箭焊缝质量智能判读为对象打通机器可读标准的建模、转化以及应用全链路,初步实现了焊缝缺陷判读标准由单纯的文本阅览向知识驱动工业场景的模式转变,为机器可读标准在航天领域的应用提供指导.

机器人原位测量是当前航天大型构件制造重要的发展趋势,针对大型金属构件机器人原位测量面临的非朗伯表面光学测量和弱几何微纹理表面点云匹配两大技术挑战,本文提出了一种基于光度-面结构光多传感器融合的高精度测量方法.该方法将一定数量的LED光源添加到传统条纹投影测量装置中,实现对高亮反射表面的高精度法向量估计,并将表面法向量信息与条纹投影测量的点云信息进行融合,同时设计多模态几何特征描述子提高点云匹配精度,实现测量精度的整体优化.该方法用于贮箱壁板面型测量,实验结果验证了其有效性.

机器可读标准是标准数字化转型的重要内容和发展方向,有助于提升标准化工作的效率,缩短标准的制修订周期,提升标准使用的便利性和维护效率.机器可读标准在智能制造领域的应用通过将标准进行语义化描述和定义,实现跨领域、跨地域、跨系统的生产装备、产品和工厂间的信息交换和共享.因而智能制造行业是"机器可读标准"的重点应用领域之一.

针对新形势下航天飞行器关重件机加车间日益显现的工艺优化周期长、资源配置效率低、质量保证手段单一等瓶颈问题,引入数字孪生(Digital twin)的理念、手段、工具和方法,结合机加车间的生产运行特点,提出了基于数字孪生技术的航天关重件机加车间集成框架;阐述了孪生环境、映射枢纽以及使能平台等核心功能要素的内涵;论述了通过孪生数据实现加工工艺优化、混流排产与调度以及质量诊断、预测与控制的技术途径;讨论了机加车间孪生系统的开发与集成要点,从而实现车间动态迭代优化与高效协同管控的目标.

随着航天精密产品复杂度不断提升,以文本、图片或影像为主的传统培训方式已无法适应当前高质量、高效率的技能培训需求.本文提出了一种基于云架构的航天产品虚拟装配培训技术,构建了"平台+APP"模式的基于云架构的航天产品虚拟装配培训系统,设计了基于虚拟现实(Virtual Reality,VR)/增强现实(Augmented Reality,AR)的交互式航天产品虚拟装配培训场景,构建了相应的培训效果综合评价体系.本技术可有效缩短技能人员的培训周期,推进航天产品快速制造,具有较强的应用价值和市场推广前景.

In this study, we propose an inverse reflectance model based on co-located images to precisely model the nonlinear reflection behavior of the non-diffuse reflective surfaces. The proposed model can accurately map the pixel value to the product of the normal vector and the light direction. We need to capture only one co-located image and one RGB image under multispectral conditions to ensure that photometric stereo vision can achieve a high-precision performance, so the time required to capture images is considerably reduced. To perform surface inspection in case of mass production, the proposed method can realize online detection of the moving surfaces at a microsecond shooting rate because the co-located image can be acquired in advance and used for the subsequent workpiece. However, the iterative steps applied in the traditional methods arc omitted, and the robustness with respect to outliers, such as shadow points and highlights, is improved, because a neural network is used in the proposed method to train the near-field photometric stereo model. Furthermore, the results of simulation and experiment show that the algorithm can recover the normal vector of the non-diffuse surface well under the condition of very few images.

针对目前航天制造车间生产管控中存在的效率低、精细度差、动态响应能力不足等难题,研究了基于数字孪生的制造车间生产管控方法,设计了基于跨网段信息异步交互的航天数字孪生车间架构,提出了航天数字孪生车间的基本组成和虚实融合的制造车间分层管控模式,阐述了面向不同对象的制造智能(MI)和商业智能(BI)场景应用,为军工企业车间生产管控提供了可行的技术途径.

This paper presents an intelligent conceptual design framework for complex machine tools, which features a back and forth interaction mechanism between human intelligence and machine intelligence, towards a more productive design process. In particular, the focus hinges on the transition between concept generation and concept improvement, where a heuristics-based method is proposed to solve physical contradictions via a human-computer interaction mechanism. Focusing on the design of ultra-precision grinding machine, a case study presents how the framework facilitated resolving the key contradiction between high position servo stiffness and force control compliance, by a novel active grinding force control strategy.

This paper presents a dynamic cell-list method for realizing large scale molecular dynamics (MD) simulations with more than 50 million atoms on a single consumer graphics card. It adapts the cell-list algorithm by introducing an efficient two-step atom location scheme and a dynamic memory allocation scheme such that only those cells containing atoms consume device memory. In addition, a large amount of memory is saved since it does not use the neighbour list. The computational efficiency is improved by reducing the memory loading times and maximizing coalesced memory access as compared to methods utilizing neighbour lists, since memory bandwidth is becoming the bottle-neck of the latest GPUs. As a result, MD simulations with more than 50 million atoms utilizing advanced three-body interaction potential are made possible on a consumer graphics card with just 11 GB of graphics memory. The proposed framework is designed to run totally on the graphics card, with all the data stored in the graphics memory to avoid the time-consuming data transfer between host and device. It achieves 2.5 times the speed and 20 times the atom number of the latest Lammps GPU package on the NVIDIA GTX 1080Ti GPU. The proposed framework is expected to help adapt existing MD packages for supporting large scale MD simulations on personal desktops and thereby extend MD to a wider range of researchers and engineers.

Thermal error is one of the main errors in ultra-precision machine tools. This paper presents a thermodynamics-based structure optimization method to reduce the thermal displacements of machine tools during operation. The method makes use of the thermal–structure coupled model to analyze the thermal behavior considering the thermal contact resistance and the temperature rise of the oil film in hydrostatic spindle. The structure of the motor link, spindle, and headstock of grinder are optimized by setting appropriate gaps in the contact region of two neighboring parts to change the heat transfer distribution and minimize the thermal displacement of the spindle center position. The proposed method is validated by an equivalent thermal conductivity-based simulation method and experiment on an ultra-precision grinding machine tool. Experimental results show that the proposed method can provide an important instruction on how to reduce the thermal error for the design of the precision machine tools, especially for those with key parts placed near the heat sources.

Although four-dimensional (4D) light field imaging has many advantages over traditional two-dimensional (2D) imaging, its high computation cost often hinders the application of this technique in many fields, such as object detection and tracking. This paper presents a hybrid method to accelerate the object detection in light field imaging by integrating the deep learning with the depth estimation algorithm. The method takes full advantage of computation imaging of the light field to generate an all-in-focus image, a series of focal stacks, and multi-view images at the same time, and convolutional neural network and defocusing are consequently used to perform initial detection of the objects in three-dimensional (3D) space. The estimated depths of the detected objects are further optimized based on multi-baseline super-resolution stereo matching while efficiency is maintained, as well by compressing the searching space of the disparity. Experimental studies are conducted to demonstrate the effectiveness of the proposed method.

This paper proposed a human–machine integrated conceptual design method based on ontology, aiming at eliminating the uncertainties and blindness during the design process of ultra-precision grinding machine, especially for its key component–the ultra-precision hydrostatic guideways. Both the required knowledge and the database of hydrostatic guideways are modelled using ontologies to provide a consensual understanding among collaborators. Moreover, a formalized knowledge searching interface is developed to obtain similar instances as references according to the design principles and rules. Based on the imaginal thinking theory, the search process and the results are attempted to be presented in the form of image in order to fit human's customary intuitive thinking frame, facilitating the decision making process. Finally, our design of hydrostatic guideways for an ultra-precision grinding machine is used to validate the effectiveness of the method.

Despite of the rapid development of computer science and information technology, human-machine integrated design of complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this paper proposes an ontology-based model to reach at an unambiguous and normative representation of the knowledge. Firstly, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Secondly, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

This paper proposes an ontology-based design method which integrates human's knowledge and experience with computer's inference and computational capabilities for the spindle of ultra-precision grinding machine. A complete design framework is initiated based on a unify ontology base, which is built to integrate human's experience with computer's database. The spindle's bearing and drive type are automatically selected by defining the experience-based fuzzy inference rules, and applying the similarity-based instance search method. After the geometric model and finite element model are conducted, the static, dynamic and thermodynamic behaviors of the spindle are optimized. Consequently, the design indices of the spindle of an ultra-precision grinding machine have been satisfied successfully.

Thermal conductivity is the decisive factor in the accuracy analysis of thermal deformation, which is one of the key reasons affecting the precision of ultra-precision machine. In order to ensure the accuracy of temperature field distribution analysis and improve the machine precision, a thermal conductivity analysis model based on thermodynamic theory and a structure optimization method of machine tools through the finite element analysis are presented. To improve the analysis accuracy of the thermal conductivity, free electron gas model and Debye model are applied to calculate the thermal conductivity of main materials of ultra-precision optical machine tools. The temperature field distribution of spindle, carriage and bed can be obtained through the finite element analysis software such as ANSYS. Based on the study of temperature rise law of the spindle and the complete machine, a structure optimization method of ultra-precision optical machine is finally presented in the error sensitive direction by reducing the contact area between the motor,spindle and carriage, optimizing the connections between the motor,spindle and carriage. As a result, the thermal deformation is reduced and the machine accuracy is improved.

Aiming at the complex mechatronic design of ultra-precision grinding machine, especially its key component -- the ultra-precision hydrostatic guideways system, this paper proposed a design knowledge capture method based on ontology, which helps create a consensual knowledge model as well as the database of hydrostatic guideways. Based on the imaginal thinking theory, the search process and the results are attempted to be presented in the form of image in order to fit human's customary intuitive thinking frame, facilitating the decision making process. Moreover, the epistemological descriptions toward the design parameters of the hydrostatic guideways are provided to specify the corresponding design principles. Finally, our design of hydrostatic guideways in an ultra-precision grinding machine is used to exemplify the effectiveness of the method.

In order to ensure the accuracy of temperature field distribution analysis and improve the machine precision ,a thermal conductivity anal-ysis model based on thermodynamic theory and fi-nite element analysis is presented .Firstly ,free e-lectron gas model and Debye model are applied to calculate the thermal conductivity of main materi-als of ultra precision optical machine spindle . T hen the temperature rise law of the spindle under different structures and situations is analyzed ,a structure optimization method of ultra precision optical machine spindle is finally presented by re-ducing the contact area between the heat source and spindle ,optimizing the connections between spindle and point contact components .As a re-sult ,the thermal deformation is reduced and the machine accuracy is improved .