Abstract: In an example embodiment, a controller and computer system are provided for a lighting apparatus. More particularly, users are able to use the computer system to define a sequence of one or more capture configurations. Each capture configuration includes identification(s) of one or more light sources to illuminate during an image capture, one or more channels to use to capture the image, and an exposure time. Each sequence can further define custom delays between capture configurations within the sequence. Upon receipt of a trigger from either hardware or software, the controller then fires the stored sequence, which takes images based on the captured configurations (and delays). Artificial intelligence techniques can then be applied to the captured images to identify suspected defects in components captured in the images.

Abstract: An additively manufactured product comprises a lattice including repeating unit cells, the repeating unit cells including a hybrid surface lattice unit cell, the hybrid surface lattice unit cell having a configuration represented by an interpolation of a first and second surface lattice unit cell, the hybrid surface lattice unit cell having a characteristic tensile and/or mechanical property (e.g., stiffness, energy absorption, energy return, resilience, toughness) along a predefined axis therein not achieved by either said first or second surface lattice unit cell when formed from the same material as said hybrid surface lattice unit cell.

Abstract: Three-dimensional (3D) lattice objects with multiple zones of different lattice unit cells may be formed by additive manufacturing. Sets of primary unit cells are provided such that cach primary unit cell is configured to smoothly transition to other primary unit cells through a series of intermediate unit cells. A universal unit cell may define the primary and intermediate unit cells.

Abstract: In an example embodiment, two different neural networks are used in conjunction with each other to identify defects in items appearing in images. More particularly, a defect neural network is trained on images labeled with an indication of a defect type (or no defect) appears in an item in the image (with optionally an identification of the area in which the defect appears). Separately, a location neural network is trained on images that have a specific geometric shape (e.g., point, line, circle) labeled in the image. The labels identify not just the pixels that are part of the shape, but also provide a value that indicates a degree of the identification of the location. Surrounding pixels to the identified shapes are then given values according to a distribution that is based on distance from the shape, with the peak values being those pixels that are exactly on the identified shape.

Abstract: In an example embodiment, a controller and computer system are provided for a lighting apparatus. More particularly, users are able to use the computer system to define a sequence of one or more capture configurations. Each capture configuration includes identification(s) of one or more light sources to illuminate during an image capture, one or more channels to use to capture the image, and an exposure time. Each sequence can further define custom delays between capture configurations within the sequence. Upon receipt of a trigger from either hardware or software, the controller then fires the stored sequence, which takes images based on the captured configurations (and delays). Artificial intelligence techniques can then be applied to the captured images to identify suspected defects in components captured in the images.