垫片标准查询系统开发中的关键技术

鉴于国内外密封垫片标准众多、数据量庞大,设计了基于VisualBasic与MSAccess的垫片标准查询系统,实现了垫片标准的自动化查询。扩展了部分VB控件的功能,通过捕捉鼠标事件,实现了Picturebox控件的滚轮缩放以及MSHFlexGrid控件的滚轮翻页功能。可对查询结果进行二次筛选并输出至word文档,操作便捷。     

基于VB技术的SolidWorks二次开发与应用

本文介绍了SolidWorks软件的特点及其二次开发原理。通过实例论述了利用VB对SolidWorks进行二次开发的关键技术,并阐述了二次开发过程中调用Access数据库中参数的方法。     

基于聚四氟乙烯表面滴状冷凝的传热分析

表面材料对滴状冷凝传热影响很大,本文建立聚四氟乙烯复合材料表面传热的数学物理模型,分析滴状冷凝的传热机理,计算表面液滴和液膜的冷凝传热,获得冷凝面的总传热系数.应用ANSYS软件模拟液滴,液膜传热,液膜区换热对整体滴状冷凝影响很大.     

Target tracking with incomplete detection

In this paper, we address the multiple target tracking problem as a maximum a posteriori problem. We adopt a graph representation of all observations over time. To make full use of the visual observations from the image sequence, we introduce both motion and appearance likelihood. The multiple target tracking problem is formulated as finding multiple optimal paths in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regions and similarly one foreground region may correspond to multiple objects. To deal with this problem, we propose merge, split and mean shift operations to generate new hypotheses to the measurement graph. The proposed approach uses a sliding window framework, that aggregates information across a fixed number of frames. Experimental results on both indoor and outdoor data sets are reported. Furthermore, we provide a comparison between the proposed approach with the existing methods that do not merge/split detected blobs.     

Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles

Rather severe parametric uncertainties and uncertain nonlinearities exist in the dynamic modeling of a parallel manipulator driven by pneumatic muscles. Those uncertainties not only come from the time-varying friction forces and the static force modeling errors of pneumatic muscles but also from the inherent complex nonlinearities and unknown disturbances of the parallel manipulator. In this paper, a discontinuous projection-based adaptive robust control strategy is adopted to compensate for both the parametric uncertainties and uncertain nonlinearities of a three-pneumatic-muscles-driven parallel manipulator to achieve precise posture trajectory tracking control. The resulting controller effectively handles the effects of various parameter variations and the hard-to-model nonlinearities such as the friction forces of the pneumatic muscles. Simulation and experimental results are obtained to illustrate the effectiveness of the proposed adaptive robust controller.     

Ultraprecise 3D Printed Graphene Aerogel Microlattices on Tape for Micro Sensors and E-Skin

3D printed graphene aerogels hold promise for flexible sensing fields due to their flexibility, low density, conductivity, and piezo-resistivity. However, low printing accuracy/fidelity and stochastic porous networks have hindered both sensing performance and device miniaturization. Here, printable graphene oxide (GO) inks are formulated through modulating oxygen functional groups, which allows printing of self-standing 3D graphene oxide aerogel microlattice (GOAL) with an ultra-high printing resolution of 70 µm. The reduced GOAL (RGOAL) is then stuck onto the adhesive tape as a facile and large-scale strategy to adapt their functionalities into target applications. Benefiting from the printing resolution of 70 µm, RGOAL tape shows better performance and data readability when used as micro sensors and robot e-skin. By adjusting the molecular structure of GO, the research realizes regulation of rheological properties of GO hydrogel and the 3D printing of lightweight and ultra-precision RGOAL, improves the sensing accuracy of graphene aerogel electronic devices and realizes the device miniaturization, expanding the application of graphene aerogel devices to a broader field such as micro robots, which is beyond the reach of previous reports.     

AR-based deep learning for real-time inspection of cable brackets in aircraft

In the process of aircraft assembly, there exist numerous and ubiquitous cable brackets that shall be installed on frames and subsequently need to be manually verified with CAD models. Such a task is usually performed by special operators, hence is time-consuming, labor-intensive, and error-prone. In order to save the inspection time and increase the reliability of results, many researchers attempt to develop intelligent inspection systems using robotic, AR, or AI technologies. However, there is no comprehensive method to achieve enough portability, intelligence, efficiency, and accuracy while providing intuitive task assistance for inspectors in real time. In this paper, a combined AR+AI system is introduced to assist brackets inspection in a more intelligent yet efficient manner. Especially, AR-based Mask R-CNN is proposed by skillfully integrating markerless AR into deep learning-based instance segmentation to generate more accurate and fewer region proposals, and thus alleviates the computation load of the deep learning program. Based on this, brackets segmentation can be performed robustly and efficiently on mobile devices such as smartphones or tablets. By using the proposed system, CAD model checking can be automatically performed between the segmented physical brackets and the corresponding virtual brackets rendered by AR in real time. Furthermore, the inspection results can be directly projected on the corresponding physical brackets for the convenience of maintenance. To verify the feasibility of the proposed method, experiments are carried out on a full-scale mock-up of C919 aircraft main landing gear cabin. The experimental results indicate that the inspection accuracy is up to 97.1%. Finally, the system has been deployed in the real C919 aircraft final-assembly workshop. The preliminary evaluation reveals that the proposed real-time AR-assisted intelligent inspection approach is effective and promising for large-scale industrial applications.     

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.     

Efficient skeleton curve-guided five-axis sweep scanning based on optimal traversing of multiple connected skeleton curves

The five-axis sweep scanning approach is an emerging surface inspection technology which could tremendously boost the inspection efficiency through working in the way of continuous scanning. While inspecting the surfaces with multiple connected skeleton curves, the topological complexity brings conflict between achieving efficient inspection and working in continuous manner. Recently, a skeleton curve-guided five-axis sweep scanning method was proposed to tackle this problem but the resulting inspection path has to inspect the entire surface in a round-trip way. The manner of round-trip inspection pulls down the entire inspection efficiency and should be avoided as much as possible. In this paper, we present an improved skeleton curve-guided five-axis sweep scanning method to generate a more efficient five-axis scanning path for the surface with multiple connected skeleton curves. The proposed method starts from the framework of existing skeleton curve-guided five-axis sweep scanning method. Under the unique kinematic requirements of efficient five-axis sweep scanning, an integer linear programming optimization approach is utilized to optimally connect the inspection paths on independent surface patches and form a shorter skeleton curve-based sweep scanning path as compared with the existing skeleton curve-guided five-axis sweep scanning method. The resulting inspection path is composed of the single-pass inspection for most of the surface and the round-trip inspection for a small part of the surface. The comparison experiments are conducted on two surfaces with multiple connected skeleton curves. Experiment results show that the proposed method significantly outperforms the method provided by the leading commercial software Apexblade and the original skeleton curve-guided five-axis sweep scanning method.     

Multiple source partial knowledge transfer for manufacturing system modelling

Transfer learning has shown its attractiveness for manufacturing system modelling by leveraging previously acquired knowledge to assist in training the target model, whereas most techniques focus on single-source transfer settings. Since there are usually multiple source domains available in practice, multi-source transfer learning is attracting more attention. Existing researches regard the source instance or the source model as the basic information granularity, which makes it difficult to reduce the global shift and the local discrepancy across domains simultaneously. Therefore, this paper presents a multiple source partial knowledge transfer method (MSPKT) for manufacturing system modelling tasks, in which partial knowledge is defined as a novel information granularity between the instance granularity and model granularity. Firstly, TSK (Takagi-Sugeno-Kang) fuzzy system is introduced as the basic learner to represent partial knowledge effectively. Then, we design a transferability measurement of partial knowledge by considering the similarity and reliability to support transfer learning with multiple source domains. Finally, a synthetic dataset and two manufacturing system datasets are used to verify the effectiveness of the proposed method.