loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Evgenia Selinger 1 and Lars Linsen 2

Affiliations: 1 Jacobs University, Germany ; 2 Westfälische Wilhelms-Universität Münster and Jacobs University, Germany

Keyword(s): NURBS Curves, 5-axis Machining, G2-continuity.

Related Ontology Subjects/Areas/Topics: CAGD/CAD/CAM Systems ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling

Abstract: Automated machining with 5-axis robots require the generation of tool paths in form of positions of the tool tip and orientations of the tool at each position. Such a tool path can be described in form of two curves, one for the positional information and one for the orientational information, where the orientation is given by the vector that points from a point on the orientation curve to the respective point on the position curve. As the robots need to slow down for sharp turns, i.e., high curvatures in the tool path lead to slow processing, our goal is to generate tool paths with minimized curvatures and a guaranteed error bound. Starting from an initial tool path, which is given in form of polygonal representations of the position and orientation curves, we generate optimized versions of the curves in form of B-spline curves that lie within some error bounds of the input path. Our approach first computes an optimized version of the position curve within a tolerance band o f the input curve. Based on this first step, the orientation curve needs to be updated to again fit the position curve. Then, the orientation curve is optimized using a similar approach as for the position curve, but the error bounds are given in form of tolerance frustums that define the tolerance in lead and tilt. For an efficient optimization procedure, our approach analyzes the input path and splits it into small (partially overlapping) groups before optimizing the position curve. The groups are categorized according to their geometric complexity and handled accordingly using two different optimization procedures. The simpler, but faster algorithm uses a local spline approximation, while the slower, but better algorithm uses a local sleeve approach. These algorithms are adapted to both the position and orientation curve optimization. Subsequently, the groups are combined into a 5-axis tool path in form of two G2-continuous B-spline curves over the same knot vector. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.139.83.248

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Selinger, E. and Linsen, L. (2018). Efficient Curvature-optimized G2-continuous Path Generation with Guaranteed Error Bound for 5-axis Machining. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP; ISBN 978-989-758-287-5; ISSN 2184-4321, SciTePress, pages 59-70. DOI: 10.5220/0006537400590070

@conference{grapp18,
author={Evgenia Selinger. and Lars Linsen.},
title={Efficient Curvature-optimized G2-continuous Path Generation with Guaranteed Error Bound for 5-axis Machining},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP},
year={2018},
pages={59-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006537400590070},
isbn={978-989-758-287-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - GRAPP
TI - Efficient Curvature-optimized G2-continuous Path Generation with Guaranteed Error Bound for 5-axis Machining
SN - 978-989-758-287-5
IS - 2184-4321
AU - Selinger, E.
AU - Linsen, L.
PY - 2018
SP - 59
EP - 70
DO - 10.5220/0006537400590070
PB - SciTePress