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41 results on '"ZHONG Qunpeng"'

1. Application of artificial intelligence technology in failure analysis

Author

LI Zhuohan, YOU Yiliang, ZHAO Zihua, LUO Hongyun, WU Sujun, ZHANG Zheng, and ZHONG Qunpeng

Subjects
artificial intelligence, machine learning, natural language processing, failure analysis, failure diagnosis, life prediction, failure prevention, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

This paper explores the application and development trends of artificial intelligence (AI) technology, particularly machine learning and natural language processing in the field of failure analysis. Failure analysis is a crucial method for ensuring the reliability and safety of equipment, and is widely used in aerospace, automotive manufacturing, electronic devices, and other fields. Traditional failure analysis methods often rely on expert experience, which is time-consuming and laborious. By integrating AI’s powerful data processing capabilities with traditional methods, the accuracy and efficiency of analysis have been significantly enhanced. In terms of failure mode diagnosis, AI can rapidly and accurately identify various fault modes and provide precise diagnostic results. In failure cause diagnosis, AI integrates data from multiple sources to uncover complex failure factors and potential causal relationships, improving diagnostic reliability. In failure prediction, machine learning can accurately forecast material lifespan and strength, reducing experimental time and costs. In failure prevention, AI offers new approaches to effectively reduce the risk of failure and lower product maintenance costs. The paper also looks forward the future development prospects of AI in failure analysis and highlights challenges and recommendations in the areas, such as data quality improvement, model optimization, interdisciplinary collaboration, and ethical and safety issues.

Published
2024
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6. Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks and Data Fusion Techniques in Tensile Test

Author

Wang, Hongwei, Luo, Hongyun, Han, Zhiyuan, Zhong, Qunpeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Huang, Ronghuai, editor, Yang, Qiang, editor, Pei, Jian, editor, Gama, João, editor, Meng, Xiaofeng, editor, and Li, Xue, editor

Published
2009
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20. Investigation of damage identification of 16Mn steel based on artificial neural networks in tensile test

Author

Hongwei Wang, Zhong Qunpeng, Hongyun Luo, and Zhiyuan Han

Subjects
Yield (engineering), Materials science, business.industry, Alloy steel, Work hardening, Structural engineering, engineering.material, Strain hardening exponent, Acoustic emission, engineering, Deformation (engineering), business, Necking, Tensile testing
Abstract

In order to identify the damage modes of 16Mn steel, the tensile test of 16Mn steel plate specimens was developed and monitored with acoustic emission technique. Based on the acoustic emission signature analysis during material damage, 3-layer back-propagation neural networks (BPNN) model with Tansig-Logsig transfer function was applied to identify the damage modes of 16Mn steel in tensile test. In the model, amplitude, counts, energy, duration and rise time of acoustic emission parameters were selected as input neurons, and elastic deformation, yield deformation, strain hardening and necking deformation of damage modes were selected as output neurons. After the model has been trained with the experimental data, the discrimination rate of damage modes was equal to approximately 83%. It showed that it is feasible to identify the damage modes of 16Mn steel in tensile test based on acoustic emission technique and artificial neural network.

Published
2009

21. Investigation of Damage Identification of 16Mn Steel Based on Artificial Neural Networks and Data Fusion Techniques in Tensile Test

Author

Hongwei Wang, Zhong Qunpeng, Hongyun Luo, and Zhiyuan Han

Subjects
Artificial neural network, business.industry, Noise (signal processing), Computer science, Pattern recognition, Machine learning, computer.software_genre, Sensor fusion, Back propagation neural network, Identification (information), Acoustic emission, Feature (machine learning), Artificial intelligence, business, computer, Tensile testing
Abstract

This paper proposes a damage identification method based on back propagation neural network (BPNN) and dempster-shafer (D-S) evidence theory to analyze the acoustic emission (AE) data of 16Mn steel in tensile test. Firstly, the AE feature parameters of each sensor in 16Mn steel tensile test are extracted. Secondly, BPNNs matching sensor number are trained and tested by the selected features of the AE data, and the initial damage decision is made by each BPNN. Lastly, the outputs of each BPNN are combined by D-S evidence theory to obtain the finally damage identification of 16Mn steel in tensile test. The experimental results show that the damage identification method based on BPNN and D-S evidence theory can improve damage identification accuracy in comparison with BPNN alone and decrease the effect of the environment noise.

Published
2009

22. Uniform magnetic chains of hollow cobalt mesospheres from one-pot synthesis and their assembly in solution

Author

Guo, Lin, Liang, Fang, Wen, Xiaogang, Yang, Shihe, He, Lin, Zheng, Wangzhi, Chen, Chinping, Zhong, Qunpeng, Guo, Lin, Liang, Fang, Wen, Xiaogang, Yang, Shihe, He, Lin, Zheng, Wangzhi, Chen, Chinping, and Zhong, Qunpeng

Abstract

Magnetic chains up to 10 mu m in length formed of hollow cobalt mesospheres (480-850 nm) with a 60 nm thick shell are synthesized by a new soft-assembly protocol. The obtained chains show a saturation magnetization of 37.5 emu g(-1), a remnant magnetization of 1.55 emu g(-1), and a coercivity of ca. 66 Oe at 300 K. A possible mechanism for the formation of the chainlike hollow structures is proposed.

Published
2007

24. One-step solution fabrication of magnetic chains consisting of jingle-bell-shaped cobalt mesospheres

Author

Liang, Fang, Guo, Lin, Zhong, QunPeng, Wen, Xiaogang, Yang, Shihe, Zheng, Wangzhi, Chen, Chinping, Zhang, Nina, Chu, Weiguo, Liang, Fang, Guo, Lin, Zhong, QunPeng, Wen, Xiaogang, Yang, Shihe, Zheng, Wangzhi, Chen, Chinping, Zhang, Nina, and Chu, Weiguo

Abstract

Using a one-step solution phase approach, the authors have synthesized uniform jingle bell-shaped cobalt mesopheres (550-750 nm) and assembled the mesospheres into long magnetic chains (20-30 mu m). All of the cobalt spheres are hollow with similar to 40 nm thick shells but each contains an similar to 200 nm diameter solid ball. The nano- to mesoscale structures were realized via reaction of CoCl2 center dot 6H(2)O and N2H4 center dot H2O in the presence of polyvinylpyrrolidone (PVP) in an ethylene glycol solution. Magnetic measurements show a coercivity of about 75 Oe with a remnance of 9.6 emu/g at 300 K. We propose a possible mechanism for the formation of the nanoto mesoscale structures. (c) 2006 American Institute of Physics.

Published
2006

40. Nanorods of ammine cobalt chloride [Co(NH3)6Cl3]: Synthesis, characterization, and magnetic properties

Author

Zhao, Zihua, Liang, Fang, Hu, Penghao, Guo, Lin, Zhong, Qunpeng, He, Lin, and Chen, Chinping

Subjects
*SUPERCONDUCTORS, *SCANNING electron microscopy, *MAGNETIC properties, *NANOPARTICLES
Abstract

Abstract: A novel Co complex [Co(NH3)6Cl3] nanorods with diameters of 150–200nm and 2–2.5μm in length were synthesized successfully through a simple solution chemical route. The products were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and superconducting quantum interference device (SQUID) system. We propose a possible reaction process for the formation of Co(NH3)6Cl3. A control experiment shows that PVP plays a soft-template to uniform the morphology and size of the products. [Copyright &y& Elsevier]

Published
2007
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41. Controlling the aspect ratio of one-dimensional of cobalt complex ([(NH3)4Co(OH)2Co(NH3)4]Cl4(H2O)4)

Author

Liang, Fang, Li, Linglong, Guo, Lin, Zhang, Rui, Yin, Penggang, and Zhong, Qunpeng

Subjects
*COBALT compounds, *TRANSITION metals, *CHROMIUM group, *XYLENE
Abstract

Abstract: One-dimensional cobalt complex ([(NH3)4Co(OH)2Co(NH3)4]Cl4(H2O)4) was successfully prepared by using a simple CTAB-mediated microemulsion method in a xylene–ethanol–water system. Experiments were designed to investigate the morphologies of samples in different cetyltrimethyl ammonium bromide (CTAB) concentration. The diameter decrease and the aspect ratio of as-product increase with the increasing of the molar ratio of CTAB to Co2+ (RCTAB:Co). The Raman properties was investigated and the possible formation mechanism was supposed. [Copyright &y& Elsevier]

Published
2007
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