Abstract: System and method for context prediction via application-specific process-to-product causal networks for generating manufacturing insights. A process graph is obtained that comprises a set of data nodes, including operation specific process data nodes and product data nodes. A probabilistic graph model (PGM) is learned for the industrial process based on the process graph and historic process and product data collected for the industrial process in respect of the data nodes The PGM comprises a computed structure and a set of relationship parameters.

Abstract: System and method that includes: capturing an image of a manufactured part using an image sensing device, wherein an actual pose of the manufactured part relative to the image sensing device in the image can vary from an expected pose of the manufactured part relative to the image sensing device; estimating an optimal pose that represents a transformation of the expected pose to the actual pose based on (i) the image, (ii) initial pose data that indicates the expected pose, and (iii) a 3D model that models a model part that corresponds to the manufactured part as a set of polygonal facets; mapping texture data from the image to facets of the 3D model based on the optimal pose to generate a textured 3D model corresponding to the optimal pose; and rendering a virtual image for a further pose of the manufactured part based on the textured 3D model.

Abstract: System and method that includes mapping temperature values from a two dimensional (2D) thermal image of a component to a three dimensional (3D) drawing model of the component to generate a 3D thermal model of the component; mapping temperature values from the 3D thermal model to a 2D virtual thermal image corresponding to a virtual thermal camera perspective; and predicting an attribute for the component by applying a prediction function to the 2D virtual thermal image.

Abstract: System and method that includes mapping temperature values from a two dimensional (2D) thermal image of a component to a three dimensional (3D) drawing model of the component to generate a 3D thermal model of the component; mapping temperature values from the 3D thermal model to a 2D virtual thermal image corresponding to a virtual thermal camera perspective; and predicting an attribute for the component by applying a prediction function to the 2D virtual thermal image.