  In this paper, we introduce a probabilistic statistics solution or artificial
intelligence (AI) approach to identify and quantify permanent (non-zero strain)
continuum/material deformation only based on the scanned material data in the
spatial configuration and the shape of the initial design configuration or the
material configuration. The challenge of this problem is that we only know the
scanned material data in the spatial configuration and the shape of the design
configuration of three-dimensional (3D) printed products, whereas for a
specific scanned material point we do not know its corresponding material
coordinates in the initial or designed referential configuration, provided that
we do not know the detailed information on actual physical deformation process.
Different from physics-based modeling, the method developed here is a
data-driven artificial intelligence method, which solves the problem with
incomplete deformation data or with missing information of actual physical
deformation process. We coined the method is an AI-based material deformation
finding algorithm.
  This method has practical significance and important applications in finding
and designing thermal compensation configuration of a 3D printed product in
additive manufacturing, which is at the heart of the cutting edge 3D printing
technology. In this paper, we demonstrate that the proposed AI
continuum/material deformation finding approach can accurately find permanent
thermal deformation configuration for a complex 3D printed structure component,
and hence to identify the thermal compensation design configuration in order to
minimizing the impact of temperature fluctuations on 3D printed structure
components that are sensitive to changes of temperature.
