Abstract: Methods of inspecting a product made by additive manufacturing (AM) of multiple layers, computer program products and inspection modules for AM systems are provided. An augmented file is derived from a design file including layer data used to produce the product by AM. For each design layer, the augmented file includes the layer data for the design layer and weighted layer data for design layers beneath the design layer. A machine learning (ML) algorithm (trained on previous images and augmented files) is applied with respect to the derived augmented file onto received optical inspection images of the product during the AM process to detect production errors.

Abstract: Methods, computer program products and nozzle monitoring modules are provided, to monitor nozzles in 2D and/or 3D inkjet printing systems. Nozzle monitoring comprises registering an image of a printed product with respect to a corresponding raster file and evaluating nozzle performance and managing nozzles by applying a neural network (NN) trained on a plurality of registered images of the printed product and corresponding raster files. The disclosed NN approach may apply a deep learning model, and has been shown to improve performance over manual analysis of images of printed products (which is the current method of monitoring nozzles). Disclosed methods and modules may be implemented in 2D inkjet printing system and/or in 3D additive manufacturing (AM) inkjet printing system, monitoring nozzles that deposit layers of the 3D product.

Abstract: Methods of inspecting a product made by additive manufacturing (AM) of multiple layers, computer program products and inspection modules for AM systems are provided. An augmented file is derived from a design file including layer data used to produce the product by AM. For each design layer, the augmented file includes the layer data for the design layer and weighted layer data for design layers beneath the design layer. A machine learning (ML) algorithm (trained on previous images and augmented files) is applied with respect to the derived augmented file onto received optical inspection images of the product during the AM process to detect production errors.