Improving feasibility of robotic milling through robot placement optimisation

Vosniakos, GC; Matsas, E

dc.contributor.author	Vosniakos, GC	en
dc.contributor.author	Matsas, E	en
dc.date.accessioned	2014-03-01T01:33:38Z
dc.date.available	2014-03-01T01:33:38Z
dc.date.issued	2010	en
dc.identifier.issn	0736-5845	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20500
dc.subject	Robotic milling	en
dc.subject	Genetic algorithms	en
dc.subject	Inverse dynamics	en
dc.subject	Manipulability	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.classification	Robotics	en
dc.subject.other	SCULPTURED SURFACE	en
dc.subject.other	ARTICULATED ROBOT	en
dc.subject.other	GENETIC ALGORITHM	en
dc.subject.other	LOCATION	en
dc.subject.other	PERFORMANCE	en
dc.subject.other	PATH	en
dc.title	Improving feasibility of robotic milling through robot placement optimisation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.rcim.2010.04.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.rcim.2010.04.001	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Milling performed with robots is quite demanding, even for low-strength materials, due to the high accuracy requirements, the generally high and periodically varying milling forces and the low stiffness of robots compared to CNC machine tools. In view of the generally improved recently robot stiffness, it is desirable to perform the milling operation in regions of the robot's workspace where manipulability, both kinematic and dynamic, is highest, thereby exhausting the robot's potential to cope with the process. In addition, by selecting the most suitable initial pose of the robot with respect to the workpiece, a reduction in the range of necessary joint torques may be reached, to the extent of alleviating the heavy requirements on the robot. Two genetic algorithms (GAs) are employed to tackle these problems. The values of several robot variables, such as joint positions and torques, which are needed by the genetic algorithms, are calculated using inverse kinematics and inverse dynamics models. In addition, initial positions and poses leading to singularities along the milling path are penalized and, thus, avoided. The first GA deals solely with robot kinematics to maximize manipulability. The second GA takes into account milling forces, which are computed numerically according to the particular milling parameters, to minimise joint torque loads. (C) 2010 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING	en
dc.identifier.doi	10.1016/j.rcim.2010.04.001	en
dc.identifier.isi	ISI:000279307600012	en
dc.identifier.volume	26	en
dc.identifier.issue	5	en
dc.identifier.spage	517	en
dc.identifier.epage	525	en