DOI QR코드

DOI QR Code

하이브리드 프로토타입 듀얼 로드 셀 구조 개발

Development of Hybrid Prototype Dual Load Cell Structure

  • 함주혁 (한라대학교 메카트로닉스공학과)
  • Ham, Juh-Hyeok (Departmemt of Mechatronics Engineering, Halla University)
  • 투고 : 2020.02.06
  • 심사 : 2020.08.28
  • 발행 : 2020.12.20

초록

We have developed the hybrid prototype load cell structures. These developed load cell structures may increase the reliability of the load sensing by deriving the load values through the double sensing method through the vertical maximum deflection and bending stress of the simple beams. For this purpose, the structure design was performed so that the load value, the deflection and stress value could be output to the same value through the optimal structure design. The structurally designed dimensions reaffirmed the accuracy of the design through the structural analysis program and the matching of the load value and the deflection value. Based on the designed structural dimension, the prototype form was constructed through laser cutting and production using hot rolled steel materials. The developed prototype load cell structure can be used as good educational material in various subjects such as material mechanics, steel structure design, measurement engineering, and mechatronics engineering. It is also believed that the measurement system ideas can inform the occurrence of errors in the event of a problem, and if a major accident caused by a sensing error is predicted, it will show good utilization to prevent accidents.

키워드

참고문헌

  1. Andreas, M., & Seragado, M., 2017. Introductory to Machine Learning with Python, Hanbit Media.
  2. Beckwith, T.G., Marangoni, R.D., & Lienhard, J.H., 2007. Mechnical Measurements, Young.
  3. Green Economic Daily, 2018. "Walker death." Uber autonomous car crash shock. Why?, URL: http://www.greened.kr [Accessed 01 April 2018].
  4. Ham, J.H. 2019a. A study on the prediction of major prices in the shipbuilding industry using artificial intelligence. Autumn Conference of the Society of Naval Architects of Korea, Kyungju.
  5. Ham, J.H. 2019b. A study on the prediction of stock preice of large shipyards using deep learning. Autumn Conference of the Society of Naval Architects of Korea, Kyungju.
  6. Ham, J.H. & Kim, U.N., 1997. The development of advanced buckling strength estimation system. Journal of the Society of Naval Architects of Korea, 34(3), pp.53-60.
  7. Kujira, H., 2017. Introduction to machine running, deep learning practical development using python, Wikibooks.
  8. Lasdon, L.S. & Waren, A.D., 1978. Generalized reduced gradient software for linearly and nonlinearly constrained problems. in : Greenberg, H.J. (Ed.) Design and implementation of optimization software, Sijthoff and Noordhoff: holland.
  9. Min, H. K., 2017. Touching Data with Python, BJ Public.
  10. MSC/Nastran, 2005. Nastran commercial manual & technical note, The MacNeal-Schwendler Corporation.
  11. Timoshenko, S & Young, D.H., 1971. Element of strength of material, 5th Edition MARUZEN co., LTD Tokyo, Japan.