BibTeX
@ARTICLE{
Birk2018Rgo,
title = "Robust generation of constrained {B}-spline curves based on automatic differentiation
and fairness optimization",
journal = "Computer Aided Geometric Design",
volume = "59",
pages = "49--67",
year = "2018",
issn = "0167-8396",
doi = "10.1016/j.cagd.2017.11.005",
url = "http://www.sciencedirect.com/science/article/pii/S0167839617301474",
author = "Lothar Birk and T. Luke McCulloch",
keywords = "Form parameter design, Fairness optimization, Automatic differentiation, B-spline,
Python",
abstract = "This paper details the use of automatic differentiation in form parameter driven
curve design by constrained optimization. Computer aided design, computer aided engineering
(CAD/CAE), and particularly computer aided ship hull design (CASHD) are typically implemented as
interactive processes in which the user obtains desired shapes by manipulation of control vertices.
A fair amount of trial and error is needed to achieve the desired properties. In the variational
form parameter approach taken here, the system computes vertices so that the resulting curve meets
the specifications and is optimized with respect to a fairness criteria. Implementation of curve
design as an optimization problem requires extensive derivative calculations. The paper illustrates
how the programming burden can be eased through the use of automatic differentiation techniques. A
variational curve design framework has been implemented in Python, and applications to CASHD curve
design are shown. The new method is robust and allows great flexibility in the selection of
constraints. Offsets, tangents, and curvature may be imposed anywhere along the curve. Form
parameters may also be used to define straight segments within a curve, require the curve to enclose
specified forms, or specify relationships between curve properties.",
ad_area = "Computer Aided Geometric Design"
}
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