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https://icsmd2022.aconf.org/specialsession.html

• Session Organizers:
• Prof. Ruqiang Yan, School of Mechanical Engineering, Xi’an Jiaotong University
  • Email: yanruqiang@xjtu.edu.cn
• Dr. Laihao Yang, School of Mechanical Engineering, Xi’an Jiaotong University 
  • Email: yanglaihao@xjtu.edu.cn
• Prof. Xin Dong, Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham
• Email: Xin.Dong@nottingham.ac.uk

Intelligent inspection is of great significance for the maintenance and safety insurance of mechanical equipments in the field of aero-engine, aircraft, and nuclear power. However, the harsh inspection environment, long-narrow space, and scarce label data make it very challenging for the intelligent, efficient, and accurate inspection of these equipments. Under this context, the concept of “robotics + autonomous intelligence” emerges in the field of inspection and maintenance for mechanical equipment, and attracts extensive attention from OEMs, end-users, and academics. The new paradigm of inspection and maintenance will promote the robotics, sensors, and detection technology to an upper level due to the high demand on reachability in crammed space, multi-mode environment sensing, pattern identification and classification accuracy. This topic mainly addresses the emerging theory and technology in the field of intelligent robotics-based in-situ inspection for high-end mechanical equipments, and focuses on the intelligent robotics, multi-mode sensing, and explainable machine learning, which is intended for the intelligent, efficient, and accurate inspection. 

The topics of interest include, but are not limited to:

• Latest progress of state-of-the-art technologies in the field of robotics, sensing, and pattern identification for intelligent inspection
• Emerging robotics technologies such as continuum robotics, soft robotics, micro-crawling robotics, and bio-inspired robotics
• Intelligent modeling, control and path planning for in-situ inspection
• Intelligent sensing technologies such as flexible sensing, multi-mode sensing integration, and damage imaging method
• Deep learning methods in vision, vibration, and ultrasonic -based damage detection
• Explainable machine learning in the field of intelligent structure design, modeling, control, and detection