Analysis of Vibration Level for a Power Tool Using Neural Network

  • W. H. Tan School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia.
  • E. A. Lim Institute of Engineering Mathematics, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia.
  • K. S. Ong School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia.
Keywords: Neural network, Power tool, Vibrometer, Vibration level

Abstract

Power tool produced vibration when human use it for the activities of construction, repairing or finishing. Long-term mechanical vibration exposure of power tools causes worker's fingers to feel prickle and numbness, which lead to the phenomenon of hand arm vibration syndrome (HAVS). Thus, the vibration of power tool was studied and analysed in this study. First, the results of vibration level of pistol-grip corded drill were collected by using vibrometer, respectively to x, y and z-axis direction. Furthermore, to study the effect of mass on the vibration level, the vibrations results have been collected when mass attached to power tool are 1 kg and 2 kg respectively. To predict the vibration amplitudes, neural network was used to build the model from the collected experimental data and generated the required prediction results. MATLAB software has been used to analyse measurement results and predict new vibration results. Simulated vibration results have lower acceleration compared with measured vibration results, especially at the peak of vibration amplitude. The neural network model was developed in this study can be considered as reliable and applied in the design of mechanical and electrical component of power tool to reduce the vibration generated during its operating.

Published
2019-10-04
How to Cite
Tan, W. H., Lim, E. A., & Ong, K. S. (2019). Analysis of Vibration Level for a Power Tool Using Neural Network. International Journal of Automotive and Mechanical Engineering, 16(3), 7121-7132. https://doi.org/10.15282/ijame.16.3.2019.20.0532
Section
Articles