Abstract
Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.
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Acknowledgements
This work is financially supported by the National Natural Science Foundation of China (No. 51875303). Support through the start-up foundation from Sun Yat-sen University is also gratefully acknowledged. Xiaobin Hu acknowledges the funding from the China Scholarship Council (CSC).
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Xiaobin HU. He obtained his M.S. degree from Hunan University, Changsha, China in 2017. Then, he is supervised by Prof. Bjoern Menze as a Ph.D. student in informatics at Technical University of Munich, Garching, Germany. His research interests include image processing (e.g., deblurring and super resolution), medical image analysis, and uncertainty quantification.
Jian SONG. He received his Ph.D. degree from Tsinghua University in 2018. Following a postdoctoral period at Techinical University of Munich in Germany, he is now working as an associate professor in School of Biomedical Engineering, Sun Yat-sen University. His interests in biomedical engineering have ranged from biotribology to biofabriaction.
Yuhong LIU. She received her Ph.D. degree in Chinese Academy of Sciences Key Laboratory of Molecular Nanostructure & Nanotechnology Institute of Chemistry, CAS, Beijing, China, in 2005. She is an associate professor at the State Key Laboratory of Tribology of Tsinghua University, China, from 2005. Her research areas cover nanotribology, nanostructure, nanotechnology of surface and interface, chemicalmechanical planarization, and water-based lubrication.
Weiqiang LIU. He received his Ph.D. degree from Tsinghua University, Beijing, China in 1991, and started his career as a professor in Department of Mechanical Engineering at Tsinghua University in 2003. He is now the leader of the Advanced Materials & Biotechnology Research Institute and Key Laboratory of Biomedical Materials and Implant Devices, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China. His research areas cover orthopedic implants design, biomechanical and biotribological properties evaluation.
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Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
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Hu, X., Song, J., Liao, Z. et al. Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints. Friction 10, 560–572 (2022). https://doi.org/10.1007/s40544-021-0516-2
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DOI: https://doi.org/10.1007/s40544-021-0516-2