孙维方-博士

发布时间:2020-07-08 浏览量:

 所在部门

(系  部)工业工程系

(研究所)高端装备动力学与智能诊断维护研究所

  E-Mail:swf@wzu.edu.cn

研究方向

机器视觉、制造系统检测技术

基本介绍

孙维方,男,1988年生,博士。2018年6月毕业于厦门大学航空航天学院并获得工学博士学位,同年进入温州大学机电工程学院。主要研究方向为:机器视觉、制造系统检测技术。截至目前,发表SCI期刊论文20余篇。

主要工作经历

2018年7月-至今,温州大学机电工程学院专技岗

获奖情况


承担项目

作为负责人承担项目情况:

浙江省自然科学基金:数字图像驱动的钛合金高速铣削表面质量自适应控制方法研究

温州市基础性工业科技项目:动态加工条件下自由曲面表面质量自适应控制方法研究

温州市重大科技创新攻关工业项目:大型室内游乐设施虚拟设计、制造与智能维护关键技术研究(合作单位负责人)

横向课题若干

学术任职

多个SCI学术期刊评审

学术成果

2020年以来发表论文和授权专利(截至20213月)

[1] W. Sun, Y. Zhou, J. Xiang, B. Chen, W. Feng. Hankel matrix-based condition monitoring of rolling element bearings: an enhanced framework for time-series analysis[J]. IEEE Transactions on Instrumentation and Measurement, 2021,70. (SCI收录)

[2] B. Chen, Y. Li, X. Cao, W. Sun, S. Zhuang. Recovery of undersampled force measurement from high-speed milling process using approximate sparsity in frequency domain[J]. Measurement, 2021,175. (SCI收录)

[3] W. Feng, K. Zhang, S. Cai, C. Sun, W. Sun, B. Liu. A force model for face grinding using digital graphic scanning (DGS) method[J]. The International Journal of Advanced Manufacturing Technology, 2021. (SCI收录)

[4] G. Zhi, D. He, W. Sun, Y. Zhou, X. Pan, C. Gao. An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples[J]. Measurement Science and Technology, 2021. (SCI收录)

[5] X. Cao, W. Liu, B. Yao, Q. Lan, W. Sun. detection and classification of surface defects of magnetic tile based on SE-U-Net[J]. International Journal of Performability Engineering, 2020. (EI收录,通讯作者)

[6] J. Huang, B. Chen, Y. Li, W. Sun. Fractal geometry of wavelet decomposition in mechanical signature analysis[J]. Measurement, 2020, 108571. (SCI收录,通讯作者)

[7] Y. Zhou, B.Sun, W. Sun, Z. Lei. Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process[J]. Journal of Intelligent Manufacturing, 2020. (SCI收录,通讯作者)

[8] W. Sun, X. Cao, B. Chen, Y. Zhou, Z. Shen, J. Xiang. A two-stage vision-based method for measuring the key parameters of ball screws[J]. Precision Engineering, 2020, 66: 76-86. (SCI收录)

[9] W. Sun, Y. Zhou, X. Cao, B. Chen, W. Feng, L. Chen. A two-stage method for bearing fault detection using graph similarity evaluation[J]. Measurement, 2020, 108138. SCI收录)

[10] W. Sun, X. Cao. Curvature enhanced bearing fault diagnosis method using 2D vibration signal[J]. Journal of Mechanical Science and Technology, 2020,34: 2257-2266. SCI收录)

[11] Z. Lei, Y. Zhou, B. Sun, W. Sun. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process[J]. The International Journal of Advanced Manufacturing Technology, 2020,106:12032-1212. SCI收录)

[12] 孙维方,向家伟,周余庆,钟永腾. 一种基于傅里叶变换的弹簧节距测量方法:中国, ZL 201811069120.1[P].2020.04.07(发明专利)


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