Advanced exergy analysis of an absorption cooling machine: Effects of the difference between the condensation and absorption temperatures

The water–LiBr absorption machine was analyzed with the purpose of identifying the endogenous, exogenous, avoidable and unavoidable fractions of the exergy destruction. The latter was observed to be largely of endogenous nature, with the desorber and absorber being the major contributors. When the difference between the absorption and condensation temperatures was raised, both the first and second law efficiencies degraded. Furthermore, the absorber endogenous avoidable irreversibility was much larger than that at the desorber when the absorption temperature was lower than the condensation temperature, and the situation reversed when the absorption temperature became higher than the condensation temperature. The same trends were observed in terms of the exogenous avoidable exergy destruction. However, the endogenous unavoidable exergy destruction at the absorber was initially smaller than that at the desorber, and the two components equally contributed to the total endogenous unavoidable irreversibility when the absorption temperature became higher than the condensation temperature.     

柔性后缘单索传动机构的设计与分析

为满足柔性后缘结构空间的限制条件,设计一种用于驱动柔性后缘变形的单索传动机构.建立基板和单索耦合的非线性微分方程,给出了求解方法和具体算例.分析结果表明:所设计的单索传动机构可以实现后缘结构在0°和20°偏角范围内的任意变形,偏转角和驱动力之间以及偏转角和索位移之间均具有较好的线性关系;索施加在各个限位滑轮上的压力随着后缘偏转角的增加而增大,作用在限位滑轮的最大压力仅为输入力的20%左右.所研究的成果可为柔性后缘的驱动结构设计提供一定的理论依据.     

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.     

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.     

Analysis on quantifiable and controllable assembly technology for aeronautical thin-walled structures

Assembly affects the product's performance and reliability directly. The current assembly method based on geometric deviation quantity controlling, cannot guarantee the physical performance for complex aeronautical thin-walled structures effectively, such as assembly geometry accuracy and internal interactive stress. And assembly performance controlling is taken as the bottleneck problem that restricting the new aviation requirement of sub-millimeter assembly. In this paper, by proposing the accurate prediction and process-oriented adjustment&controlling strategies on assembly quality, construction on working mode with “quantifiable and controllable” characteristic was proposed, whose aim was to reduce the phenomenon of out-of-tolerance and deformation rebound error, and the ultimate goal is to reduce the uncertainty of assembly performance parameters. At the technical level, the academic development context and existing problems for assembling thin-walled structures were reviewed and analyzed, such as assembly process parameters optimization, assembly error transfer and accumulation, comprehensive adjustment on assembly quality, and virtual assembly simulation validation. Then the key future research trends for aeronautical structure assembly were also put forward, i.e. the force/deformation coordination among multi-type finite units for non-ideal assemblies, the dynamic construction of stiffness matrix for intermediate assemblies considering geometric nonlinearity, the adaptive balance on assembly performance driven by physical modeling and measured data, and the inverse optimization on assembly quality and parameters with intelligent data processing. This paper would lay a solid foundation for achieving the accurate assembly mode with the characteristics of “intelligent/scientific, and active/collaborative controlling on geometric shape and physical performance”, and higher assembly quality and efficiency could also be gained.     

Digital twin-driven manufacturing equipment development

Currently, expectations of shorter time-to-market and improved product performance are placing greater demands on manufacturing companies. However, the optimization and redesign work between the design stage and the prototype design and manufacturing stage in the traditional product development process lengthens the required product development cycle time (which lasts up to several years in extreme cases). The manufacturing phase for the physical prototype of the product is especially time-consuming and costly. The above reasons make the common product development process increasingly unable to meet the demands of market needs. Motivated by this need, the digital twin (DT)-driven manufacturing equipment (ME) development method is studied in this paper. This method contains three main core elements of the design method based on axiomatic design (AD) theory, the construction of DT models related to ME development, and DT-based validation analysis. The advantage of this method is that it can incorporate the physical prototype manufacturing stage into the digital space with the high-fidelity model provided by the DT technology, which ensures the confidentiality of the design scheme validation while freeing it from the physical prototype stage. This avoids the cost of physical prototyping, shortens the product development cycle, and improves the efficiency of new ME development. At the end of this paper, a case study of the development of a virtual machining dynamic performance test bench (VM-TB) is carried out to show the implementation flow of this proposed method, and its operability and effectiveness are verified.     

Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.     

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.     

Facile synthesis of rutile TiO2/carbon nanosheet composite from MAX phase for lithium storage

Titanium oxide (TiO₂), with excellent cycling stability and low volume expansion, is a promising anode material for lithium-ion battery (LIB), which suffers from low electrical conductivity and poor rate capability. Combining nano-sized TiO₂ with conductive materials is proved an efficient method to improve its electrochemical properties. Here, rutile TiO₂/carbon nanosheet was obtained by calcinating MAX (Ti₃AlC₂) and Na₂CO₃ together and water-bathing with HCl. The lamellar carbon atoms in MAX are converted to 2D carbon nanosheets with urchin-like rutile TiO₂ anchored on. The unique architecture can offer plentiful active sites, shorten the ion diffusion distance and improve the conductivity. The composite exhibits a high reversible capacity of 247 mA h g⁻¹, excellent rate performance (38 mA h g⁻¹ at 50 C) and stable cycling performance (0.014% decay per cycle during 2000 cycles) for lithium storage.     

高速率成形技术进展

高速率成形技术包括爆炸成形技术、液电成形技术和电磁成形技术。高速率成形技术成形力量大,成形时间短,成形装置简单,可在室温下对高强度难成形材料进行成形加工。综述了高速率成形技术的原理、特点和应用,重点就高速率成形目前的两项研究重点——成形效率和成形性能,分析了结合传统准静态液压预成形和高速率最终成形的混合成形加工工艺方面的研究进展,混合成形加工工艺有效提高了成形效率和成形性能,对高速率成形条件对材料成形性能的影响进行了归纳和讨论。