@article{TASMEKTEPLIGIL2022101478,
title = {SplineLearner: Generative learning system of design constraints for models represented using B-spline surfaces},
journal = {Advanced Engineering Informatics},
volume = {51},
pages = {101478},
year = {2022},
issn = {1474-0346},
doi = {https://doi.org/10.1016/j.aei.2021.101478},
url = {https://www.sciencedirect.com/science/article/pii/S1474034621002287},
author = {A. Alper Tasmektepligil and Erkan Gunpinar},
keywords = {B-spline surface, Design constraints, Generative design, Computer-aided design, Constraint-based modeling, Machine learning},
abstract = {Product design involves a computer-aided design (CAD) model with its design (dimensional) parameters. A generative design (GD) system can then be utilized to generate new designs by modifying these parameters. There is a need for a GD system to determine the visual validity of a design that is obtained after parametric modification. In this context, this paper introduces an approach to learn visual (i.e., design) constraints of a CAD model (represented using B-spline surfaces) by means of user feedbacks. A deformation technique (utilizing modification and limit curves) for B-spline surfaces is first introduced, which involves a few design (deformation) parameters. Via a generative learning process, the proposed system, SplineLearner, generates random designs, which are shown to user(s) for visual validity classifications. In a machine learning step, a mathematical model is computed that can perform prediction for a design to be valid or not. The mathematical model is also integrated into SplineLearner (after some user interactions) to prevent imbalances between the numbers of valid and invalid designs. As a proof of concept, B-spline surface models of a car body parts (hood, roof, side and trunk) are utilized, and two user studies are conducted to demonstrate the efficacy of the proposed method.}
}