Research on Robotic Compliance Control for Ultrasonic Strengthening of Aviation Blade Surface

被引:0
|
作者
Fang, Shanxiang [1 ,2 ]
Du, Yao [3 ]
Zhang, Yong [1 ]
Meng, Fanbo [4 ]
Ang Jr, Marcelo H. H. [2 ]
机构
[1] Peng Cheng Lab, Dept Math & Theory, Shenzhen 518055, Peoples R China
[2] Natl Univ Singapore, Dept Mech Engn, Singapore 117575, Singapore
[3] Univ Bourgogne Franche Comte, VIBOT ImViA, IUT, 9 Ave Alain Savary,BP 47870, F-21078 Dijon, France
[4] Xidian Univ, Sch Mechanoelect Engn, Xian 710071, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
industry robot; compliance control; neural network fuzzy PID control; ultrasonic strengthening; aviation blade surface; NEURAL-NETWORK; MANIPULATORS;
D O I
10.3390/mi14040730
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In order to satisfy the requirement of the automatic ultrasonic strengthening of an aviation blade surface, this paper puts forward a robotic compliance control strategy of contact force for ultrasonic surface strengthening. By building the force/position control method for robotic ultrasonic surface strengthening., the compliant output of the contact force is achieved by using the robot's end-effector (compliant force control device). Based on the control model of the end-effector obtained from experimental determination, a fuzzy neural network PID control is used to optimize the compliance control system, which improves the adjustment accuracy and tracking performance of the system. An experimental platform is built to verify the effectiveness and feasibility of the compliance control strategy for the robotic ultrasonic strengthening of an aviation blade surface. The results demonstrate that the proposed method maintains the compliant contact between the ultrasonic strengthening tool and the blade surface under multi-impact and vibration conditions.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Research of Stochastic Control on Aviation Supplies Inventory
    Wei, Guo
    Zhao, Shiwei
    Qu, Wenfang
    ADVANCED MANUFACTURING TECHNOLOGY, PTS 1-4, 2012, 472-475 : 3339 - +
  • [42] Forward Dynamics Compliance Control (FDCC): A New Approach to Cartesian Compliance for Robotic Manipulators
    Scherzinger, Stefan
    Roennau, Arne
    Dillmann, Ruediger
    2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, : 4568 - 4575
  • [43] Research on variation of grinding temperature of wind turbine blade robotic grinding
    Dai, Shijie
    Wang, Xiaojun
    Zhang, Huibo
    Wen, Birong
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2021, 235 (03) : 367 - 377
  • [44] High surface integrity machining of typical aviation difficult-to-machine material blade
    Wu, Dongbo
    Liu, Shibo
    Wang, Hui
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2023, 129 (7-8): : 2861 - 2873
  • [45] Aviation-engine blade surface anomaly detection based on the deformable neural network
    Song, Min
    Zhang, Yinlong
    SIGNAL IMAGE AND VIDEO PROCESSING, 2025, 19 (01)
  • [46] High surface integrity machining of typical aviation difficult-to-machine material blade
    Dongbo Wu
    Shibo Liu
    Hui Wang
    The International Journal of Advanced Manufacturing Technology, 2023, 129 : 2861 - 2873
  • [47] EDM Surface Strengthening Research on Rubber Mold
    Liu, Jie
    Liu, Zuo Jing
    Zhang, Ming
    ADVANCED POLYMER PROCESSING III, 2013, 561 : 291 - 295
  • [48] Robotic ultrasound probe handling auxiliary by active compliance control
    Onogi, Shinya
    Urayama, Yasuhiro
    Irisawa, Sachie
    Masuda, Kohji
    ADVANCED ROBOTICS, 2013, 27 (07) : 503 - 512
  • [49] Fuzzy reinforcement learning control for compliance tasks of robotic manipulators
    Tzafestas, SG
    Rigatos, GG
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2002, 32 (01): : 107 - 113
  • [50] An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger
    Hussain, Irfan
    Spagnoletti, Giovanni
    Salvietti, Gionata
    Prattichizzo, Domenico
    FRONTIERS IN NEUROROBOTICS, 2016, 10