A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors

Computer numerical control machine tool information reusability within virtual machining systems

Virtual Machining (VM) allows simulation of the machining process by realistically representing kinematic, static and dynamic behaviour of the intended machine tools. Using this method, manufacturing related issues can be brought to light and corrected before the product is physically manufactured. Machining systems utilised in the manufacturing processes are represented in the VM environment and there is a plethora of commercial VM software used in the industry. Each software system has a different focus and approach towards virtual machining; more than one system may be needed to complete machining verification. Thus, the significant increase in the use of virtual machining systems in industry has increased the need for information reusability. Substantial time and money has been put into the research of virtual machining systems. However, very little of this research has been deployed within industrial best practice and its acceptance by the end user remains unclear. This paper reviews current research trends in the domain of VM and also discusses how much of this research has been taken on board by software venders in order to facilitate machine tool information reusability. The authors present a use cases which utilises the novel concept of Machining Capability Profile (MCP) and the emerging STEP-NC compliant process planning framework for resource allocation. The use cases clearly demonstrate the benefits of using a neutral file format for representing MCPs, as opposed to remodelling and or reconfiguring of this information multiple times for different scenarios. The paper has shown through the use cases that MCPs are critical for representing recourse information from a kinematic, static and dynamic perspective that commercial software vendors can subsequently use. The impact of this on mainstream manufacturing industry is potentially significant as it will enable a true realisation of interoperability

Monitoring Water and Energy Cycles at Climate Scale in the Third Pole Environment (CLIMATE-TPE)

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented

Directional Selection from Host Plants Is a Major Force Driving Host Specificity in Magnaporthe Species

One major threat to global food security that requires immediate attention, is the increasing incidence of host shift and host expansion in growing number of pathogenic fungi and emergence of new pathogens. The threat is more alarming because, yield quality and quantity improvement efforts are encouraging the cultivation of uniform plants with low genetic diversity that are increasingly susceptible to emerging pathogens. However, the influence of host genome differentiation on pathogen genome differentiation and its contribution to emergence and adaptability is still obscure. Here, we compared genome sequence of 6 isolates of Magnaporthe species obtained from three different host plants. We demonstrated the evolutionary relationship between Magnaporthe species and the influence of host differentiation on pathogens. Phylogenetic analysis showed that evolution of pathogen directly corresponds with host divergence, suggesting that host-pathogen interaction has led to co-evolution. Furthermore, we identified an asymmetric selection pressure on Magnaporthe species. Oryza sativa-infecting isolates showed higher directional selection from host and subsequently tends to lower the genetic diversity in its genome. We concluded that, frequent gene loss or gain, new transposon acquisition and sequence divergence are host adaptability mechanisms for Magnaporthe species, and this coevolution processes is greatly driven by directional selection from host plants

A Novel Measurement Method for Linear Thermal Expansion Coefficient of Laminated Composite Material Tubular Specimen

Materials of satellite integration truss frame are required to withstand temperature that range from about – 250 °C ~ + 150 °C. In order to reduce structural components deformation caused by such temperature change, material of truss frame mostly adopts laminated composite material tubes, whose linear thermal expansion coefficient (LTEC) is very small. Therefore, accurate measurement of LTEC of truss frame materials over a broad temperature range is essential for successful mission. To address this issue, this paper proposes a general experiment platform for measuring LTEC of laminated composite material specimen reaching length up to one meter in the temperature range from – 100 °C to +100 °C. The platform uses light-density optical fiber probe to measure length variation and thermocouple to record temperature variation. Thereafter, the thermal expansion coefficient and its measurement uncertainty can be obtained by establishing and solving mathematical model. Finally, LTEC measurement of a tubular composite materials specimen is conducted. The experiment result demonstrates the validity and practicality of the experiment platform and the measurement accuracy of LTEC which can reach up to 10-7/°C.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9708</p

An Improved Surface Treatment Process of 304 Stainless Steel Based on Low-Temperature Chromizing and Ultrasonic Vibration Extrusion

The ultrasonic vibration extrusion process is a widely used surface treatment process of stainless steel, e.g., 304 stainless steel, to improve the surface quality and increase hardness and wear resistance. However, for high-hardness 304 stainless steel, the traditional process, i.e., a single ultrasonic vibration extrusion process, does not fulfil its application requirements. To cope with this problem, this paper proposes an improved surface treatment method based on low-temperature chromizing and ultrasonic vibration extrusion to obtain the expected surface quality of 304 stainless steel. Using orthogonal design and multivariate regression, the influence of ultrasonic impact parameters on the surface integrity of 304 stainless steel was studied in this work. Finally, the experimental results show that the hardness of the surface processed by the proposed method is increased by about 2.55 times compared with the ultrasonic vibration extrusion process, and the surface roughness of the composite process is reduced by an average of 60.8% compared with that of unfinished surface. In addition, the optimal combination of process parameters is obtained: the spindle speed of 240 rpm, the feed of 0.1 mm/r, and the static extrusion of 40 μm, which can provide the optimal process parameter support for the surface treatment of 304 stainless steel

An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies

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Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time

Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters

Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively