Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction

Abstract : Configurators rely on logical constraints over parameters to aid users and determine the validity of a configuration. However, for some domains, capturing such configuration knowledge is hard, if not infeasible. This is the case in the 3D printing industry, where parametric 3D object models contain the list of parameters and their value domains, but no explicit constraints. This calls for a complementary approach that learns what configurations are valid based on previous experiences. In this paper, we report on preliminary experiments showing the capability of state-of-the-art classification algorithms to assist the configuration process. While machine learning holds its promises when it comes to evaluation scores, an in-depth analysis reveals the opportunity to combine the classifiers with constraint solvers.
Liste complète des métadonnées

https://hal.inria.fr/hal-01990767
Contributor : Mathieu Acher <>
Submitted on : Wednesday, January 23, 2019 - 2:17:13 PM
Last modification on : Tuesday, April 9, 2019 - 11:23:11 AM

File

Towards_Learning_Aided_Configu...
Files produced by the author(s)

Identifiers

Citation

Benoit Amand, Maxime Cordy, Patrick Heymans, Mathieu Acher, Paul Temple, et al.. Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction. VAMOS 2019 - 13th International Workshop on Variability Modelling of Software-Intensive Systems, Feb 2019, Leuven, Belgium. pp.1-9, ⟨10.1145/3302333.3302338⟩. ⟨hal-01990767⟩

Share

Metrics

Record views

127

Files downloads

90