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

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Résumé

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.

langue originaleAnglais
titreProceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
EditeurACM Press
ISBN (Electronique)9781450366489
Les DOIs
étatPublié - 6 févr. 2019
Evénement13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 - Leuven, Belgique
Durée: 6 févr. 2019 → …

Série de publications

NomACM International Conference Proceeding Series

Une conférence

Une conférence13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019
PaysBelgique
La villeLeuven
période6/02/19 → …

    Empreinte digitale

Contient cette citation

Amand, B., Cordy, M., Heymans, P., Acher, M., Temple, P., & Jézéquel, J. M. (2019). Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction. Dans Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019 (ACM International Conference Proceeding Series). ACM Press. https://doi.org/10.1145/3302333.3302338