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Phantom for robot neurosurgery
Product designerJul 2013 - Jul 2016 (3 years)
Design of new parts and mechanisms for printers Designing and changing parts on printers due to customers requirements
Bachelor of Mechanical engineering2010 - 2014 (4 years)
Master of Mechanical engineering2014 - 2016 (2 years)
Tool condition monitoring in stone drilling with artificial neural network
This paper explores the possibility of tool wear classification in stone drilling. Wear model is based on Radial Basis Function Neural Network which links tool wear features extracted from motor drive current signals and acoustic emission signals with two wear levels – sharp and worn drill. The results indicate tool wear monitoring capacity of the proposed model in stone drilling, and its potential for simple and cost-effective integration with CNC machine tools.
A new phantom design for neurosurgical robotics
In this paper we propose a novel phantom design for measuring application accuracy of neurosurgical robotic systems and stereotactic frames. We develop a novel phantom (T-Phantom) which enables simultaneous localization of translational displacements in entry and target points.