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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.
Cited literature [44 references]
https://hal.inria.fr/hal-01990767
Contributor : Mathieu Acher <>
Submitted on : Wednesday, January 23, 2019 - 2:17:13 PM
Last modification on : Tuesday, February 25, 2020 - 8:08:10 AM
Document(s) archivé(s) le : Wednesday, April 24, 2019 - 1:53:31 PM
Contributor : Mathieu Acher <>
Submitted on : Wednesday, January 23, 2019 - 2:17:13 PM
Last modification on : Tuesday, February 25, 2020 - 8:08:10 AM
Document(s) archivé(s) le : Wednesday, April 24, 2019 - 1:53:31 PM
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