Towards Learning-Aided Configuration in 3D Printing

Benoit Amand, Maxime Cordy, Patrick Heymans, Mathieu Acher, Paul Temple, Jean-Marc Jézéquel
2019 Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems - VAMOS '19  
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
more » ... per, 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.
doi:10.1145/3302333.3302338 dblp:conf/vamos/AmandCHATJ19 fatcat:uxh7mpbaozbopp7nrepvmloro4