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Engineering simulations for analysis of structural and fluid systems requireinformation of contacts between various 3-D surfaces of the geometry toaccurately model the physics between them. In machine learning applications,3-D surfaces are most suitably represented with point clouds or meshes andlearning representations of interacting geometries form point-basedrepresentations is challenging. The objective of this work is to introduce amachine learning algorithm, ActivationNet, that can learn from point clouds ormeshes of interacting 3-D surfaces and predict the quality of contact betweenthese surfaces. The ActivationNet generates activation states from point-basedrepresentation of surfaces using a multi-dimensional binning approach. Theactivation states are further used to contact quality between surfaces usingdeep neural networks. The performance of our model is demonstrated usingseveral experiments, including tests on interacting surfaces extracted fromengineering geometries. In all the experiments presented in this paper, thecontact quality predictions of ActivationNet agree well with the expectations.

ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs