Abstract: The present invention relates to a device (1) for sorting powder particles into ranges of particles according to one or more of a density, size and/or shape of the particles, wherein the device (1) comprises a particle sorting chamber (2) with at least one sloping side wall (3), which side wall (3) slopes from a lower part (4) of the sorting chamber (2) towards an upper part (5) thereof, wherein the lower part (4) of the sorting chamber (2) is larger dimensioned than the upper part (5), wherein at the upper part (5) of the particle sorting chamber (2) an inlet (6) is provided for supplying a flow of the powder particles to be sorted to the sorting chamber (2), wherein a particle outlet (7) is provided in the upper part (5) of the sorting chamber (2) for conducting sorted particles from the sorting chamber (2) through a duct (8) to at least one particle sedimentation classifier (9), wherein in the lower part (4) of the sorting chamber (2) means (11) are provided for generating an upward rotating gas flow in th

Abstract: A method and system for performing characterization of one or more materials. One or more materials are scanned by means of a sensory system including an X-ray sensor configured to perform multi-energy imaging for obtaining at least a lower-energy X-ray image and a higher-energy X-ray image. Images obtained by means of the sensory system are segmented, and for each segmented object, data indicative of an area density and data indicative of an atom number by analysis of the lower-energy X-ray image and the higher-energy X-ray image, and data indicative of the area density and atom number is determined by means of a calibrated model. Data indicative of a mass is calculated based on the data indicative of the area density and the data indicative of the area of each of the segmented objects.

Abstract: A method and system for training a machine learning model configured to perform characterization of components in a material stream with a plurality of unknown components. A training reward associated with each unknown component within the plurality of unknown components in the material stream is determined, based on which at least one unknown component is physically isolated from the material stream by means of a separator unit, wherein the separator unit is configured to move the selected unknown component to a separate accessible compartment. The isolated at least one unknown component is analyzed for determining the ground truth label thereof, wherein the determined ground truth is used for training an incremental version of the machine learning model.

Abstract: The present invention relates to a device (1) for sorting powder particles into ranges of particles according to one or more of a density, size and/or shape of the particles, wherein the device (1) comprises a particle sorting chamber (2) with at least one sloping side wall (3), which side wall (3) slopes from a lower part (4) of the sorting chamber (2) towards an upper part (5) thereof, wherein the lower part (4) of the sorting chamber (2) is larger dimensioned than the upper part (5), wherein at the upper part (5) of the particle sorting chamber (2) an inlet (6) is provided for supplying a flow of the powder particles to be sorted to the sorting chamber (2), wherein a particle outlet (7) is provided in the upper part (5) of the sorting chamber (2) for conducting sorted particles from the sorting chamber (2) through a duct (8) to at least one particle sedimentation classifier (9), wherein in the lower part (4) of the sorting chamber (2) means (11) are provided for generating an upward rotating gas flow in th

Abstract: A method and system for performing characterization of one or more materials. One or more materials are scanned by means of a sensory system including an X-ray sensor configured to perform multi-energy imaging for obtaining at least a lower-energy X-ray image and a higher-energy X-ray image. Images obtained by means of the sensory system are segmented, and for each segmented object, data indicative of an area density and data indicative of an atom number by analysis of the lower-energy X-ray image and the higher-energy X-ray image, and data indicative of the area density and atom number is determined by means of a calibrated model. Data indicative of a mass is calculated based on the data indicative of the area density and the data indicative of the area of each of the segmented objects.