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Interface strengthening for thermal sprayed WC-10Co4Cr coating subjected to pulsed magnetic treatment

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Rare Metals 2024年第2期43卷 780-795页

作者： Cheng-Kai Qian Qu Liu Heng Wang Ke-Jian Li Zhi-Peng Cai department of Mechanical Engineering Tsinghua UniversityBeijing100084China Tianjin Research Institute for Advanced equipment Tsinghua UniversityTianjin300304China State Key Laboratory of Tribology in Advanced equipment Tsinghua UniversityBeijing100084China

The reliability of the coated industry components demands ideal fatigue properties of the coating,and it is mainly determined by the performance of the interfaces.In this study,pulsed magnetic treatment(PMT)was applied to the thermal sprayed WC-10Co4Cr coating,and the fatigue lifetime of the coated bolt increased by 219.82%under an imitation of the operating mode condition.Scratch tests further proved that both the adhesion and cohesion strength were improved after PMT,and they benefit from the interface strengthening effects.The formation of coherent WC/Co interfaces was characterized by in-situ transmission electron microscopy(TEM),and the molecular dynamic simulations indicate that the work of separation of these interfaces is much higher than the original disordered ones.Residual stress was relaxed and distributed more homogeneously after PMT,and it mainly contributes to the coating/substrate strengthening.This work provides a new post-treatment method focusing on the interfaces in the WC-based coating and gives insight into its mechanism so that it is hopeful to be applied to other kinds of coatings.

关键词： Cemented carbide coating Pulsed magnetic treatment Interface strengthening Fatigue properties Adhesion strength

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A General Simulation Method for Complex Deformation of Irregular-Shaped Origami Configurations

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Acta Mechanica Solida Sinica 2024年第1期37卷 90-98页

作者： Zhaochen Dong Yichao Zhu Xu Guo State Key Laboratory of Structural Analysis for Industrial equipment Department of Engineering MechanicsInternational Research Center for Computational MechanicsDalian University of TechnologyDalian 116023China

Most existing treatments for origami-folding simulations have focused on regular-shaped configurations.This article aims to introduce a general strategy for simulating and analyzing the deformation process of irregular shapes by means of computational capabilities nowadays.To better simulate origami deformation with folding orders,the concept of plane follow-up is introduced to achieve automated computer simulation of complex folding patterns,thereby avoiding intersection and penetration between planes.Based on the evaluation criteria such as the lowest storage energy with tightening and the fastest pace from tightening to unfolding,the optimal crease distribution patterns for four irregular(‘N’-,‘T’-,‘O’-,and‘P’-shaped)origami configurations are then presented under five candidates.When the dimensions of the origami are fixed,it is discovered that simpler folding patterns lead to faster deformation of the origami configuration.When the folding complexity is fixed,higher strain energy results in more rapid origami expansion.

关键词： Irregular-shaped origami Complex deformation Computer-aided origami design General simulation

Path signature-based XAI-enabled network time series classification

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Science China(Information Sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and equipment Technology(CICAEET) Nanjing University of Information Science and Technology School of Computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification methods.These include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 215-228页

作者： Shuyong Duan Jialin Zhang Heng Ouyang Xu Han Guirong Liu State Key Laboratory of Reliability and Intelligence of Electrical equipment Hebei University of TechnologyTianjin 300401China department of Aerospace Engineering and Engineering Mechanics University of CincinnatiCincinnati 45221USA

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

关键词： Parameter identification Ultrasonic echo group High-precision modeling Artificial neural network NDT

A Detailed Thermal Resistance Network Analysis of FCBGA Package

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Journal of Thermal Science 2024年第1期33卷 18-28页

作者： DANG Hao LU Yang DU Yanzheng ZHANG Xiu ZHANG Qian MA Weigang ZHANG Xing Key Laboratory for Thermal Science and Power Engineering of Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China Zhongxing Telecom equipment Corporation Shanghai 201203China

Using thermal models to describe the heat dissipation process of FCBGA is a significant topic in the field of packaging.However,the thermal resistance model considering the structure of each part of the chip is still ambiguous and rare,but it is quite desirable in engineering.In this work,we propose a detailed thermal resistance network model,and describe it by using thermal conduction resistance and thermal spreading resistance.For a striking FCBGA case,we calculated the thermal resistance of each part of the structure according to the temperature field simulated by COMSOL.The thermal resistance network can be used to predict the temperatures in the chip under different conditions.For example,when the power changes by 40%,the relative error of junction temperature prediction is only 0.24%.The function of the detailed thermal resistance network in evaluating the optimization space and determining the optimization direction is clarified.This work illustrates a potential thermal resistance analysis method for electronic devices such as FCBGA.

关键词： FCBGA package thermal resistance network thermal spreading resistance junction temperature

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Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 647-663页

作者： Xukai Ren Huanwei Yu Xianfeng Chen Yantong Tang Guobiao Wang Xiyong Du School of Mechanical Engineering Tianjin UniversityTianjin300354China Quality department Shaoxing Key Laboratory of Special Equipment Intelligent Testing and EvaluationShaoxing312071China

Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.

关键词： Stirred reactor fault diagnosis vibration signal CatBoost

Kinematic calibration method with high measurement efficiency and robust identification for hybrid machine tools

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Chinese Journal of Aeronautics 2024年第3期37卷 468-482页

作者： Liping WANG Mengyu LI Guang YU Weitao LI Xiangyu KONG State Key Laboratory of Tribology Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Beijing Key Laboratory of Precision/Ultra-precision Manufacturing equipment and Control Beijing 100084China

Geometric error is the main factor affecting the machining accuracy of hybrid machine tools.Kinematic calibration is an effective way to improve the geometric accuracy of hybrid machine tools.The necessity to measure both position and orientation at each pose,as well as the instability of identification in case of incomplete measurements,severely affects the application of traditional calibration methods.In this study,a kinematic calibration method with high measurement efficiency and robust identification is proposed to improve the kinematic accuracy of a five-axis hybrid machine tool.First,the configuration is introduced,and an error model is derived.Further,by investigating the mechanism error characteristics,a measurement scheme that only requires tool centre point position error measurement and one alignment operation is proposed.Subsequently,by analysing the effects of unmeasured degrees of freedom(DOFs)on other DOFs,an improved nonlinear least squares method based on virtual measurement values is proposed to achieve stable parameter identification in case of incomplete measurement,without introducing additional parameters.Finally,the proposed calibration method is verified through simulations and experiments.The proposed method can efficiently accomplish the kinematic calibration of the hybrid machine tool.The accuracy of the hybrid machine tool is significantly improved after calibration,satisfying actual aerospace machining requirements.

关键词： Hybrid machine tool Calibration Measurement scheme Improved nonlinear least squares method Virtual measurement values

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Machine learning for fault diagnosis of high-speed train traction systems: A review

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Frontiers of Engineering Management 2024年第1期11卷 62-78页

作者： Huan WANG Yan-Fu LI Jianliang REN department of Industrial Engineering Tsinghua UniversityBeijing 100084China Zhibo Lucchini Railway equipment Co. Ltd.Taiyuan 030032China

High-speed trains(HSTs)have the advantages of comfort,efficiency,and convenience and have gradually become the mainstream means of transportation.As the operating scale of HSTs continues to increase,ensuring their safety and reliability has become more imperative.As the core component of HST,the reliability of the traction system has a substantially influence on the train.During the long-term operation of HSTs,the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures,thus threatening the running safety of the train.Therefore,performing fault monitoring and diagnosis on the traction system of the HST is necessary.In recent years,machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis.Machine learning has made considerably advancements in traction system fault diagnosis;however,a comprehensive systematic review is still lacking in this field.This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint.First,the structure and function of the HST traction system are briefly introduced.Then,the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed.Finally,the challenges for accurate fault diagnosis under actual operating conditions are revealed,and the future research trends of machine learning in traction systems are discussed.

关键词： high-speed train traction systems machine learning fault diagnosis

Analysis on carbon emission reduction intensity of fuel cell vehicles from a life-cycle perspective

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Frontiers in Energy 2024年第1期18卷 16-27页

作者： Ziyuan TENG Chao TAN Peiyuan LIU Minfang HAN Fuel Cell and Energy Storage Center Department of Energy and Power EngineeringState Key Laboratory of Control and Simulation of Power Systems and Generation EquipmentTsinghua UniversityBeijing 100084China

The hydrogen fuel cell vehicle is rapidly developing in China for carbon reduction and neutrality.This paper evaluated the life-cycle cost and carbon emission of hydrogen energy via lots of field surveys,including hydrogen production and packing in chlor-alkali plants,transport by tube trailers,storage and refueling in hydrogen refueling stations(HRSs),and application for use in two different cities.It also conducted a comparative study for battery electric vehicles(BEVs)and internal combustion engine vehicles(ICEVs).The result indicates that hydrogen fuel cell vehicle(FCV)has the best environmental performance but the highest energy cost.However,a sufficient hydrogen supply can significantly reduce the carbon intensity and FCV energy cost of the current system.The carbon emission for FCV application has the potential to decrease by 73.1%in City A and 43.8%in City B.It only takes 11.0%–20.1%of the BEV emission and 8.2%–9.8%of the ICEV emission.The cost of FCV driving can be reduced by 39.1%in City A.Further improvement can be obtained with an economical and“greener”hydrogen production pathway.

关键词： hydrogen energy life-cycle assessment(LCA) fuel cell vehicle carbon emission energy cost