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Towards Learning-Aided Configuration in 3D Printing
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
doi:10.1145/3302333.3302338
dblp:conf/vamos/AmandCHATJ19
fatcat:uxh7mpbaozbopp7nrepvmloro4