Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction

Benoit Amand, Maxime Cordy, Patrick Heymans, Mathieu Acher, Paul Temple, Jean Marc Jézéquel

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
PublisherACM Press
ISBN (Electronic)9781450366489
DOIs
Publication statusPublished - 6 Feb 2019
Event13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 - Leuven, Belgium
Duration: 6 Feb 2019 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
CountryBelgium
CityLeuven
Period6/02/19 → …

Keywords

  • 3D printing
  • Configuration
  • Machine learning
  • Sampling

Fingerprint Dive into the research topics of 'Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction'. Together they form a unique fingerprint.

Cite this