Geometric deviation modeling with Statistical Shape Analysis in Design for Additive Manufacturing

被引:6
作者
Zhu, Zuowei [1 ]
Anwer, Nabil [1 ]
Mathieu, Luc [1 ]
机构
[1] Univ Paris Sud, Univ Paris Saclay, ENS Cachan, LURPA, F-94235 Cachan, France
来源
29TH CIRP DESIGN CONFERENCE 2019 | 2019年 / 84卷
关键词
Design for Additive Manufacturing; Geometric deviation modeling; Statistical Shape Analysis; Gaussian Process; TRANSFORMATION; COMPENSATION; PREDICTION;
D O I
10.1016/j.procir.2019.04.251
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Effective modeling of geometric deviations is an important issue in Design for Additive Manufacturing (DfAM), since it enables the evaluation of geometric consistency and the optimization of geometric design. Motivated by the awareness that process-related factors have non-trivial effects on geometric deviations, a new method is proposed in this paper which combines Statistical Shape Analysis with Gaussian Process to enable the modeling of deviations with consideration of process parameters. By learning from a number of simulated samples, the method could achieve effective prediction of deviations for new parts. Its applications in surface deformation evaluation and geometric compensation are also discussed, which will bring substantial benefits to DfAM. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码:496 / 501
页数:6
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