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| 研究生： |
Anton Royanto Ahmad Anton - Royanto Ahmad |
|---|---|
| 論文名稱： |
DEVELOPMENT OF THE 6-AXIS FORCE/MOMENT SENSOR DEVELOPMENT OF THE 6-AXIS FORCE/MOMENT SENSOR |
| 指導教授： |
林其禹
Chyi-Yeu Lin |
| 口試委員: |
李維楨
Wei-chen (George) Lee 劉孟昆 Meng-Kun (Jason) Liu |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工程學院 - 機械工程系 Department of Mechanical Engineering |
| 論文出版年： | 2015 |
| 畢業學年度： | 103 |
| 語文別： | 英文 |
| 論文頁數： | 63 |
| 中文關鍵詞： | 六軸力感應器 、應變規配置 、校正方法 、類神經網路 、多項式法 |
| 外文關鍵詞： | Six-axis force sensor, strain gauge arrangement, calibration method, artificial neural network, polynomial equation. |
| 相關次數： | 點閱：195 下載：18 |
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六軸力感應器已經廣泛地被應用且在以機器手臂為基礎的智慧自動化上，並扮演重要角色。經過成功地整合結構設計、應變規設計和規劃、和校正程序，本論文發展出一個新的六軸力感應器設計。
為了發展出經濟型六軸力感應器，本研究選用了Maltese cross形態當作力感應器基礎。本文也採用了一種新型的雙平行線性應變規的應變規排列方式。應用在此創新力感應器上的兩種不同的校正方式包含神經網路法，和多項式法。
使用六軸力感應器的實驗顯示在單一力和力矩受力情況下，類神經網路法可產生較好的效果。相反的，當混和力和力矩的多重受力情況下時，多項式法則可獲得較好的效果。
The six-axis force sensors have been widely used and become very important components in robot-based intelligent automation. Structure design, strain gauge arrangement, and calibration are successfully integrated to obtain an accurate and decoupled six-axis force sensor design.
In order to develop economical decoupled six-axis force sensor, Maltese cross type is chosen in this study. A new arrangement of strain gauge is applied and double parallel linear strain gauge is used. Two calibration methods for this novel arrangement are artificial neural network and polynomial equation.
Experimental results on single force and single moment show the artificial neural network approach performs better than the polynomial equation approach. However, experimental results on combined forces and moments situations show that the polynomial equation approach performs better than the artificial neural network approach.
Agilent Technologies Inc. (1999). Practical Strain Gage Measurements.
Chao, L.-P., & Chen, K.-T. (1997). Shape optimal design and force sensitivity evaluation of six-axis force sensors. Sensors and Actuators A, 63(2), 105-112.
Daponte, P., & Grimaldi, P. (1998). Artificial Neural Networks in Measurements. Measurement, 93-115.
Hoffmann, K. (n.d.). Applying the Wheatstone Bridge Circuit. HBM.
Joo, J., Na, K., & Kang, D. (2002). Design and evaluation of a six-component load cell. Measurement, 125-133.
Kang, M.-K., Lee, S., & Kim, J.-H. (2014). Shape optimization of a mechanically decoupled six-axis force/torque sensor. Sensors and Actuators A, 209, 41-51.
Kim, G.-S., Shin, H.-J., & Yoon, J. (2008). Development of 6-axis force/moment sensor for a humanoid robot’s intelligent foot. Sensors and Actuators A, 141, 276-281.
Kuribayashi, K., Shimizu, S., Yuzawa, T., & Taniguchi, T. (1993). A New Robot Finger Force Sensor Using Neural Network. International Conference on Intelligent Robots and Systems. Yokohama: IEEE/RSJ.
Park, J.-J., & Kim, G.-S. (2005). Development of the 6-axis force/moment sensor for an intelligent robot’s gripper. Sensors and Actuators A, 127-134.
Patra, J. C., & Bos, A. v. (2000). Modeling of an intelligent pressure sensor using functional link arti®cial neural networks. ISA Transactions, 15-27.
Wang, Z., Li, Z., He, J., Yao, J., & Zhao, Y. (2013). Optimal design and experiment research of a fully pre-stressed six-axis force/torque sensor. Measurement, 46, 2013-2021.
Xu, K.-J., & Li, C. (2000). Dynamic Decoupling and Compensating Methods of Multi-Axis Force Sensors. Transactions on Instrumentation and Measurement, 49(5).
Yao, Z., Wang, F., Wang, W., & Qin, Y. (2010). Neural-Network-Based Six-axis Force/Torque Robot Sensor Calibration. 2010 International Conference on Electrical and Control Engineering. IEEE.
