The final product geometries in AM are often deformed or distorted. The deviations of three-dimensional (3D) shapes from their intended designs can be represented as 2D surfaces in a \(\mathbb {R}^3\) space, which constitutes a complicated set of data for representing, learning, and predicting geometric quality. Patterns of deviation surfaces vary with shape geometries, sizes/volumes, materials, and AM processes.