Abstract: A method for fabricating a ceramic mold includes additively manufacturing the ceramic mold with a liquid ceramic photopolymer by using an additive manufacturing system. The ceramic mold includes a core structure and a shell structure each formed from the liquid ceramic photopolymer. A cavity is defined between the core structure and the shell structure. The cavity is adapted to define the shape of a cast component upon casting and removal of the ceramic mold. The shell structure defines a plurality of shell apertures. The method further includes inserting each pin of a plurality of pins through a first shell aperture of the plurality of shell apertures, across a portion of the cavity, and into one of a second shell aperture of the plurality of shell aperture or contact with the core structure.

Abstract: A method includes additively manufacturing a first portion of the ceramic mold. The main body of the core structure defines a plurality of main body apertures. The method further includes additively manufacturing a second portion of the ceramic mold that includes a tip cap of the core structure. The tip cap of the core structure defines a plurality of tip cap apertures. The method further includes firing the first portion and the second portion of the ceramic mold. The method further includes, after firing the first portion and the second portion of the ceramic mold, connecting the first portion and the second portion by inserting each pin of a plurality of pins into a respective main body aperture of the plurality of main body apertures and a respective tip cap aperture of the plurality of tip cap apertures.

Abstract: This disclosure describes a system, method, and non-transitory computer-readable medium for image search-based object detection of utility assets in image databases. The method includes receiving an input image of a utility asset and a query bounding box representing an image-based object query. Bounding boxes of objects represented in the input image are generated based on the input image and the query bounding box, in which anchoring boxes corresponding to object classifications are identified from the bounding boxes. A textual label is determined for a selected subset of anchoring boxes. An image embedding representing the region is encoded, and image tokens are generated based on the encoded image embedding. Output images of other utility assets relevant to the image-based object query are identified from images in an image database, based on at least one of (i) the encoded image embedding, (ii) the image tokens, or (iii) the textual label. The output images are provided for output.

Abstract: This disclosure describes a system, method, and computer storage medium for joint asset and defect detection. The approach includes receiving input data including an input image of a utility asset, the input image including one or more objects. Deep neural networks are configured to generate embeddings for classification labels of the one or more objects, each embedding corresponding to a classification label and including a mapping between the classification label and a subset of feature vectors. Defect classifiers are configured to determine a likelihood of an object from the one or more objects in the input image containing a type of defect. Each defect classifier is trained to determine a type of defect based on the embeddings for the one or more classification labels. The approach includes generating an output image that includes bounding boxes for the objects and an annotation corresponding a respective object from the objects.

Abstract: This disclosure describes a system, method, and non-transitory computer-readable medium for image search-based object detection of utility assets in image databases. The method includes receiving an input image of a utility asset and a query bounding box representing an image-based object query. Bounding boxes of objects represented in the input image are generated based on the input image and the query bounding box, in which anchoring boxes corresponding to object classifications are identified from the bounding boxes. A textual label is determined for a selected subset of anchoring boxes. An image embedding representing the region is encoded, and image tokens are generated based on the encoded image embedding. Output images of other utility assets relevant to the image-based object query are identified from images in an image database, based on at least one of (i) the encoded image embedding, (ii) the image tokens, or (iii) the textual label. The output images are provided for output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for mapping an electrical grid are disclosed. A method includes sampling multiple locations within a geographic region, executing a detection process for each location, the detection process including applying the set of images for the location as input to a machine learning (ML) model that is trained to identify electrical grid assets depicted within images taken from a combination of different perspectives and obtaining an output from the machine learning model that indicates whether a same electrical grid asset is identified in each of the images of the location. In response to an ML output that indicates a positive identification of the same electrical grid asset being depicted in a particular set of images of a particular location, the method further includes: selecting a number of sublocations within the region, and executing the detection process for each sublocation.