Fang Chen
[1] M.B. Ahmed, F. Majeed, C. Sanin, and E. Szczerbicki,Smart virtual product development (SVPD) system to supportproduct inspection planning in industry 4.0, Procedia ComputerScience, 176, 2020, 2596–2604. [2] S. Ruland, L. Luthmann, J. B¨urdek, S. Lity, T. Th¨um, M.Lochau, and M. Ribeiro, Measuring effectiveness of sample-based product-line testing, ACM Sigplan Notices, 53(9), 2020,119–133. [3] H. W¨urschinger, M. M¨uhlbauer, M. Winter, M. Engelbrecht,and N. Hanenkamp, Implementation and potentials of amachine vision system in a series production using deep learningand low-cost hardware, Procedia CIRP, 90, 2020, 611–616. [4] F. Frustaci, S. Perri, G. Cocorullo, and P. Corsonello, Anembedded machine vision system for an in-line quality check ofassembly processes, Procedia Manufacturing, 42, 2020, 211–218. [5] J. Lewis, Machine vision system identifies and locates pickableparts for robotic assembly cell, Vision Systems Design, 24(5),2019, 16–18. [6] Y. Zhang, H.G. Soon, D. Ye, J.Y.H. Fuh, and K. Zhu,Powder-bed fusion process monitoring by machine vision withhybrid convolutional neural networks, IEEE Transactions onIndustrial Informatics, 16(9), 2020, 5769–5779. [7] M. Talaat, I. Arafa, and H. Metwally, Advanced automationsystem for charging electric vehicles based on machine visionand finite element method, IET Electric Power Applications,14(13), 2020, 2616–2623. [8] O. Dahhani, A. El-Jouni, and I. Boumhidi, Torque control bysupport vector machines for a dfig-based marine current turbine,Mechatronic Systems and Control, 47(4), 2019, 209–215. [9] L.J. Zhao, J. Cheng, Z.Y, Yin, H. Yang, M.J. Chen, and X.D.Yuan, Research on precision automatic tool setting technologyfor KDP crystal surface damage mitigation based on machinevision, Journal of Manufacturing Processes, 64, 2021, 750–757. [10] K. Joshi and B. Patil, Prediction of surface rough-ness by machine vision using principal components basedregression analysis, Procedia Computer Science, 167, 2020,382–391. [11] C.K. Groschner, C. Choi, and M.C. Scott, Machine learningpipeline for segmentation and defect identification from high-resolution transmission electron microscopy data, Microscopyand Microanalysis, 27(3), 2021, 549–556. [12] X. Wei, D. Wei, D. Suo, L.M, Jia, and Y.J. Li, Multi-targetdefect identification for railway track line based on imageprocessing and improved YOLOv3 Model, IEEE Access, 8,2020, 61973–61988. [13] J. Yu, X. Zheng, and J. Liu, Stacked convolutional sparsedenoising auto-encoder for identification of defect patterns insemiconductor wafer map, Computers in Industry, 109, 2019,121–133. [14] W. Tang, Q. Yang, K. Xiong, and W.J. Yan, Deep learningbased automatic defect identification of photovoltaic moduleusing electroluminescence images, Solar Energy, 201, 2020,453–460. [15] P. Dutta and D. Mukhopadhyay, Eco-sustainable molecularquantum dot cellular automata based radiography in defectidentification of industrial product using renewable energysource, Encyclopedia of Renewable and Sustainable Materials,3, 2020, 437–446. [16] A.W. Hashmi, H.S. Mali, A. Meena, and S. Saffe, Machinevision for the measurement of machining parameters: A review,Materials Today: Proceedings, 56, 2022, 1939–1946. [17] S.A. Singh and K.A. Desai, Automated surface defect detectionframework using machine vision and convolutional neuralnetworks, Journal of Intelligent Manufacturing, 34(4), 2023,1995–2011. [18] M. Kovacova, J. Horak, and M. Higgins, Behavioral analytics,immersive technologies, and machine vision algorithms in theWeb3-powered Metaverse world, Linguistic and PhilosophicalInvestigations, 21, 2022, 57–72. [19] Z. Ren, F. Fang, N. Yan, and Y. Wu, State of the art in defectdetection based on machine vision, International Journal ofPrecision Engineering and Manufacturing-Green Technology,9(2), 2022, 661–691. [20] A. Aa, C. Rahmoune, and D. Benazzouz, An early gear faultdiagnosis method based on RLMD, hilbert transform andcepstrum analysis, Mechatronic Systems and Control, 49(2),2021, 115–123.
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