Breakthrough detection for orthopedic bone drilling via power spectral density estimation of acoustic emission
Yunis Torun, Ahmet Öztürk, Nursu Hatipoglu, Zekeriya Öztemür
- 发表年份
- 2018
- 引用次数
- 8
摘要
Breakthrough detection of robotic drilling process for orthopedic surgery is proposed. Features are extracted from Power Spectral Density (PSD) with Welch methods from acoustic emission signal during the drilling operation. According to the power spectrum, Band-Pass Infinitive Impulse Filter is performed to efficient frequency band selection. Most meaningful features which have been derived as median frequency and mean frequency are selected to create a projected feature. A simple breakthrough algorithm, which compares the current value of the projected feature with an adaptive threshold, is developed.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002