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Micro-pores formation mechanism and porosity on the fatigue performance of dual laser-powder bed fusion of Ti6Al4V

International Journal of Fatigue 2025年 199卷

作者： Guotai Li Peichen Hu Gang Chen Mingjun Chen Tianyu Yu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education Harbin Heilongjiang 150001 China Institute of Electronic Engineering China Academy of Engineering Physics Mianyang Sichuan 621000 China

Porosity significantly impacts the fatigue performance and stability of dual laser additively manufactured metallic materials. This study presents a comparative analysis of the fatigue behavior of samples fabricated by single-laser powder bed fusion (SL-PBF) and dual-laser PBF (DL-PBF). It shows that porosity defects in the overlap region reduced the fatigue strength of DL-PBF samples by nearly 20 %. Micro-CT analysis revealed that micro-pores in the SL-PBF samples were predominantly located in the contour regions. Machining with a removal depth of 0.5 mm has effectively eliminated over 90 % of internal porosity defects. For monolithic specimens, increasing the sample size and reducing the layer-wise slice area can improve the sphericity of the internal defects (0.71 to 0.85) and reduce the porosity density (0.19 % to 0.011 %). Molecular dynamics simulations further examined the influence of pore size, spacing, and quantity on fatigue behavior. Larger pore sizes promote the formation of {11 2 ¯ 1} < 1 ¯ 1 ¯ 26> twin variants, as the pore diameter increased from 20 Å to 80 Å, the proportion of variants structure increased from 3.6 % to 11.5 %. Fatigue failure initiated from a single pore, with stress propagating towards a distant pore, forming crack paths. Stress was relieved along these crack trajectories, leading to secondary crack branches that spread into low-stress zones around other defects, which resulted in fatigue cracks with vein-like morphologies. This work provides systematic insights into the fatigue degradation mechanisms induced by porosity and offers actionable strategies for improving the fatigue resistance of multi-laser PBF components through process control.

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A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array for Fiber Bragg Grating Interrogation

A 4-Channel Ultrafast Wavelength-Swept REC-DFB Laser Array f...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Lingxin Meng Pan Dai Qilu Ban Kaichuan Xu Jiacheng Wang Zhen Li Feng Wang Jie Zeng Shaobo Li Xiang Ma Xiangfei Chen Key Laboratory of Intelligent Optical Sensing and Manipulation of the Ministry of Education & Engineering National Laboratory of Solid State Microstructures Research Center of Precision Photonics Integration and System Application of the Ministry of Education College of Engineering and Applied Sciences Nanjing University Nanjing China State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Optical Communication Research and Development Center the 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

We present a four-channel ultrafast wavelength-swept distributed feedback (DFB) semiconductor laser array based on the reconstruction-equivalent-chirp (REC) technique using instantaneous injection current modulation. With the aid of our designed laser driving circuit system consists of the microcontroller unit (MCU), field-programmable gate array (FPGA) and negative metal-oxide-semiconductor field effect transistors (n-MOSFETs), the precise pulse driving current for the 4-channel wavelength-swept REC-DFB laser array can be generated and a fast tuning of 16.424 nm within 10 μs is achieved. To further investigate the tuning performance of the proposed wavelength-swept laser source, we then applied it to the fiber Bragg grating (FBG) interrogation system and four FBG temperature sensor arrays were successfully demodulated. The development of high-efficient optical sensing systems heavily relies on the significance of the ultrafast wavelength-swept laser source, which has the potential to greatly enhance FBG interrogator performance in the future.

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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Dual-Layer Day-Ahead Dispatching Strategy for Regional Integrated Distributed Energy system with Intelligent Heating Schemes

SSRN

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SSRN 2023年

作者： Yang, Yunxi Hao, Junhong Feng, Xiaolong Wang, Xingce Chen, Zhuoxian Du, Xiaoze Key Laboratory of Power Station Energy Transfer Conversion Ministry of Education School of Energy Power and Mechnical Engineering North China Electric Power University Beijing102206 China The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Energy Power and Mechanical Engineering North China Electric Power University Beijing102206 China Lanzhou University of Technology China

Deep exploration and effective coordination of customer-side electrical and thermal loads significantly promote renewable energy consumption on the customer side. This paper proposes a distributed integrated energy system (DIES) by coupling a power distribution grid, heat supply network, wind power, photovoltaic, and combined heat and power generation. Based on the standard thermal resistance method, the dynamic thermal power flow model of the heating network and the overall power flow model of the DIES are constructed. On this basis, the flexible heating load under the user-following intelligent heating scheme is obtained by considering users' behavior and physical comfort. Based on the elastic heating load, a dual-layer dispatching model of the DIES is constructed, and a multi-scenario day-ahead dispatching is conducted to maximize renewable energy consumption. Compard with traditional heating schemes, the dispatch results show savings in heating demand of 7.8% and 6.2% with the intelligent heating scheme and ultimate intelligent heating scheme, respectively, and an increase in regional PV power consumption of 27.5% and 35.8%, respectively. Overall, improving heating scenarios in DIES can bring comprehensive benefits to the system regarding energy saving and emission reduction and increase the flexibility of system dispatching. © 2023, The Authors. All rights reserved.

关键词： Wind power

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Wide-Area Damping Control Enhanced by Non-Invasive Data Verification Against False Data Injection Attacks

SSRN

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SSRN 2024年

作者： Sun, Zhenglong Long, Hao Li, Zewei Gao, Bo Wei, Xiaoqiang Belikov, Juri Levron, Yoash Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy State Grid Jiashan County Power Supply Company Jiaxing China State Grid Heilongjiang Electric Power Company Limited Heilongjiang China The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia Northeast Electric Power University China

The increasing interconnectivity of power systems and the integration of renewable energy sources into the grid have exacerbated the low-frequency oscillation problem, which directly impacts the stability of the power grid. The performance of wide-area damping control (WADC), which is an effective oscillation suppression technique , is susceptible to false data injection attacks (FDIA). This study proposes a non-intrusive data verification method using an adaptive variational autoencoder (AVAE) combined with a deep convolutional neural network (DCNN) to enhance WADC’s resistance to FDIA. The method integrates data dimensionality reduction of AVAE and reconstruction capabilities with DCNN’s feature extraction advantages. Itemploys adaptive thresholds to dynamically track grid data fluctuations, accurately distinguishing between normal and abnormal measurements and improving attack detection and performance discrimination. In addition, the study proposes an intelligent repair process based on latent representations for anomalous data in the repair module, using hierarchical clustering and Gaussian mixture models post-Bayesian inference to repair data. AVAE then reconstructs the repaired data, mitigating attack impacts. Simulations on IEEE 39-Bus, 68-Bus, and 118-bus systems demonstrate that this method effectively detects network attacks and restores manipulated signals with minimal reconstruction errors, thus ensuring grid stability and reliability. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Two points are enough

arXiv

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arXiv 2024年

作者： Liu, Hao Zhao, Yanbin Zheng, Huarong Fan, Xiulin Deng, Zhihua Chen, Mengchi Wang, Xingkai Liu, Zhiyang Lu, Jianguo Chen, Jian State Key Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Ocean College Zhejiang University Hangzhou China State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Hangzhou China Nanyang Technological University Singapore School of Control Science and Engineering Zhejiang University Hangzhou China School of Materials Science and Engineering Tianjin University Tianjin China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

Prognosis and diagnosis play an important role in accelerating the development of lithium-ion batteries, as well as reliable and long-life operation. In this work, we answer an important question: What is the minimum amount of data required to extract features for accurate battery prognosis and diagnosis? Based on the first principle, we successfully extracted the best two-point feature (BTPF) for accurate battery prognosis and diagnosis using the fewest data points (only two) and the simplest feature selection method (Pearson correlation coefficient). The BTPF extraction method is tested on 820 cells from 6 open-source datasets (covering five different chemistry types, seven manufacturers, and three data types). It achieves comparable accuracy to state-of-the-art features in both prognosis and diagnosis tasks. This work challenges the cognition of existing studies on the difficulty of battery prognosis and diagnosis tasks, subverts the fixed pattern of establishing prognosis and diagnosis methods for complex dynamic systems through deliberate feature engineering, highlights the promise of data-driven methods for field battery prognosis and diagnosis applications, and provides a new benchmark for future studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Mitigating the Strength-Ductility Inversion in Nano-B4c/Al Composites by Constructing Heterogeneous Structure

SSRN

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SSRN 2023年

作者： Han, Bingzhuo Jiang, Longtao Han, Huimin Zhang, Runwei Du, Shanqi Luo, Tian Gong, Deng Xue, Wei Chao, Zhenlong Chen, Guoqin School of Materials Science and Engineering Harbin Institute of Technology No 92 West Dazhi Street Harbin150001 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology No 92 West Dazhi Street Harbin China Aero-Engine Control System Institute Aero Engine Corporation of China Wuxi214063 China

Heterogeneous nano-B4C/Al composites with soft and hard zones (SZs and HZs) were fabricated through a new approach combining segmented ball milling with pressure infiltration. It indicated that with the increase of step Ⅰ ball milling time, the width of SZs reduced. The heterogeneous composite with an SZ width of ~2.71 μm (Hetero-2μm) exhibited a 161 % increase in elongation compared to the homogeneous composite, with almost no decrease in ultimate tensile strength. Due to the mechanical incompatibility between the SZs and HZs, geometrically necessary dislocations (GND) accumulated near the interface. However, the length of GND pile-ups was finite, resulting in the presence of non-interface-affacted zones (NIAZ), which was harmful to the strength. The high strength of Hetero-2μm was attributed to the reduction of NIAZ width. Furthermore, the uniform distribution of plastic strain and some external toughening mechanisms, such as crack proliferation and deflection, were believed to be the reason for the ductility enhancement. © 2023, The Authors. All rights reserved.

关键词： Ball milling

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Thickness Variation Effect on the Compressive Properties of Ultra-Thick Cfrp Laminates

SSRN

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SSRN 2023年

作者： Gao, Yu Zhu, Shiyang Ding, Huiming Song, Xiaowen Hu, Huanyi Wang, Han Ke, Yinglin State Key Laboratory of Fluid Power and Mechatronic System School of Mechanical Engineering Zhejiang University Hangzhou310027 China Wuhan Second Ship Design and Research lnstitute Wuhan430060 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Hangzhou AME Aerospace Manufacturing Equipment Co Ltd Hangzhou311200 China

As ultra-thick laminates are widely used in the field of aerospace and marine as the main load-bearing structure, extreme service conditions lead to increasing thickness of composite structures. However, the effect of the increase in thickness on the compressive properties of ultra-thick laminates is still unclear. Herein parameters including fiber volume fraction, fiber waviness, void content and free edge effect that affect the compressive strength with increasing thickness are studied. The results show that the fiber volume fraction of ultra-thick laminates does not change significantly with the increase of thickness due to the adoption of the zero-bleeding process, while the fiber waviness and void contribute to the thickness effect on compressive strength. On the other hand, the high interlaminar stress of the free edge contributes to the premature delamination of the specimen, which dominates the compressive strength of the ultra-thick laminate. Finally, compared with the thin laminate (2mm), the compressive strength of ultra-thick laminate drops sharply (nearly 28%), but when the thickness increases from 30 to 70mm, the drop of compressive strength is not obvious (only 10%). When the material and curing process are consistent, the weak interlaminar properties of ultra-thick laminates does not change significantly with the increase of thickness, which leads to the insignificant decrease in compression strength of ultra-thick laminates with the increase of thickness. The proposed empirical relationship between the compressive strength and thickness of quasi-isotropic laminates can provide important theoretical basis and experimental reference for the design of ultra-thick composite structures. © 2023, The Authors. All rights reserved.

关键词： Compressive strength

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Research on GPR image recognition based on deep learning

Research on GPR image recognition based on deep learning

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2019 International Conference on Computer Science Communication and Network Security, CSCNS 2019

作者： Gong, Zhimin Zhang, Huaiqing State Key Laboratory of Transmission Equipment and System Safety and Electrical New Technology Chongqing University Chongqing400044 China

It is difficult for traditional image recognition methods to accurately identify ground penetrating radar (GPR) images. This paper proposes a deep-learning based Faster R-CNN algorithm for the automatic classification and recognition of GPR images. Firstly, GPR images with different features were obtained by using gprMax, a professional GPR simulation software. Then, the feature of the target in the image was taken as the recognition object and the data set was made. Finally, Faster R-CNN's recognition ability of GPR images was analyzed from various accuracy, average accuracy and other indicators. The results showed that Faster R-CNN could successfully identify GPR images and accurately classify them, with an average accuracy rate of 93.9%. © The Authors, published by EDP Sciences, 2020.

关键词： Image recognition

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