Authors: Madhu Vadali; Chao Ma; Xiaochun Li; Frank E. Pfefferkorn; Neil A. Duffie
Addresses: Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar - 382355, India ' Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA ' University of California Los Angeles, Los Angeles, CA 90095-1597, USA ' University of Wisconsin Madison, Madison, WI 53706, USA ' University of Wisconsin Madison, Madison, WI 53706, USA
Abstract: The objective of this work is to generate irregular, smooth, adaptive laser scan trajectories for pulsed laser polishing. Traditionally pulsed laser polishing, like other surface finishing processes has used zig-zag scan paths. Zig-zag trajectories are simple in nature, are comprised of sharp turns, the dynamics of the positioning system are not considered, and more importantly are not adaptable because the path generation is independent of surface condition. In this paper, the authors present an intelligent scan trajectory generation scheme that can overcome these limitations. These trajectories are based on the artificial potential fields method of path planning that take the surface condition into account. Computer simulations are presented to illustrate the characteristics of the path and guidelines are developed for choosing the trajectory generation parameters. Experiments show that these trajectories result in marginal improvements in the average surface roughness when compared to the traditional zig-zag trajectories, all the while overcoming the limitations. Finally, smooth, irregular scan trajectories are generated for a micro end milled Ti6Al4V surface with a feature that needs no polishing, thus illustrating the versatility of the trajectory generations scheme.
Keywords: laser; polishing; intelligent; path planning; surface roughness; zig-zag; artificial fields; Marangoni flows; smoothness; micro melting.
International Journal of Mechatronics and Manufacturing Systems, 2018 Vol.11 No.2/3, pp.101 - 119
Received: 04 Sep 2017
Accepted: 19 Nov 2017
Published online: 19 Jun 2018 *