Abstract: Methodologies and manufacturing processes to manufacture components by electron beam melting additive manufacturing, particularly components of molybdenum or a molybdenum-based alloy and particularly of complex nuclear component geometries. Input parameters are provided for controlling electron beam melting additive manufacturing equipment, such as electron beam melting machines. The input parameters relate to various process steps, including build set-up, initial thermal treatment, initial layering of powder, pre-consolidation thermal treatment, consolidation, post-consolidation thermal treatment, indexing of layers, and post-build thermal treatment. The methodologies and manufacturing processes allow manufacture of components of molybdenum having a purity of ?99.0% and a density of ?99.75%. Metallographic cross-sections of the manufactured molybdenum components were porosity-free and crack-free.

Abstract: A method inspects weld quality in-situ. The method obtains a plurality of sequenced images of an in-progress welding process and generates a multi-dimensional data input based on the plurality of sequenced images and/or one or more weld process control parameters. The parameters may include: (i) shield gas flow rate, temperature, and pressure; (ii) voltage, amperage, wire feed rate and temperature (if applicable); (iii) part preheat/inter-pass temperature; and (iv) part and weld torch relative velocity). The method generates defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input. The analytics information includes predictive insights on quality features of the in-progress welding process. The method then generates a 3-D visualization of one or more as-welded regions, based on the analytics information, and the plurality of sequenced images.

Abstract: A method is provided for particle morphology classification. The method includes obtaining input imagery of a particle sample that includes powder particles that are sintered together. The method also includes generating an input dataset for clustering based on the input imagery, including (i) detecting and segmenting powder particles, (ii) extracting and standardizing powder particle images, and (iii) calculating morphology metrics of the powder particles. The method also includes identifying categories, based on geometry or morphology-based similarities between different particles, using K-means clustering on Hu invariant moments of the powder particle images. Some implementations include receiving labels for the categories from a user, and subsequently using the categories to analyze or quantify future batches of particulate based on those labels.

Abstract: A method is provided for unsupervised 3D modeling and geometric measurement of additively manufactured parts. The method includes obtaining near-infrared (NIR) images for a welding process for a welded part. The welded part includes welded metal and agglomerated powder particles. The method also includes generating a multi-dimensional dataset based on the NIR images, including cropping the multi-dimensional dataset to a region of interest. The method also includes detecting melted regions in image layers of the multi-dimensional dataset to obtain an output volume. The method also includes detecting agglomerated powder in the output volume to obtain melt masks. Each melt mask indicates weld pixels for a respective image layer. The method also includes applying a multi-layer predictive model to account for multi-layer weld penetration, based on the melt masks, to obtain an output data mask that represents a 3D model and geometric measurements for the welded part.

Abstract: A method is provided for enhancing image resolution for sequences of 2-D images of additively manufactured products. For each of a plurality of additive manufacturing processes, the process obtains a respective plurality of sequenced low-resolution 2-D images of a respective product during the respective additive manufacturing process and obtains a respective high-resolution 3-D image of the respective product after completion of the respective additive manufacturing process. The process selects tiling maps that subdivide the low-resolution 2-D images and the high-resolution 3-D images into low-resolution tiles and high-resolution tiles, respectively. The process also builds an image enhancement generator iteratively in a generative adversarial network using training inputs that includes ordered pairs of low-resolution and high-resolution tiles.

Abstract: A method is provided for tracking multiple beams in weld pools. The method includes obtaining an input video feed that includes a plurality of frames having a plurality of beams for weld pools. The method also includes, for each frame of the plurality of frames of the input video feed, creating a gradient mask that identifies one or more pixels in the respective frame for a weld pool. The method also includes detecting and filtering contiguous regions based on the gradient mask to obtain a pixel mask for each contour of a weld pool. The method also includes identifying one or more beam locations within the weld pool based on the pixel mask for each contour.

Abstract: Methods to in-situ monitor production of additive manufacturing products collects images from the deposition process on a layer-by-layer basis, including a void image of the pattern left in a slurry layer after deposition of a layer and a displacement image formed by immersing the just-deposited layer in a renewed slurry layer. Image properties of the void image and displacement image are corrected and then compared to a binary expected image from a computer generated model to identify defects in the just-deposited layer on a layer-by-layer basis. Additional methods use the output from the comparison to form a 3D model corresponding to at least a portion of the additive manufacturing product. Components to control the additive manufacturing operation based on digital model data and to in-situ monitor successive layers for manufacturing defects can be embodied in a computer system or computer-aided machine, such as a computer controlled additive manufacturing machine.

Abstract: Methods to in-situ monitor production of additive manufacturing products collects images from the deposition process on a layer-by-layer basis, including a void image of the pattern left in a slurry layer after deposition of a layer and a displacement image formed by immersing the just-deposited layer in a renewed slurry layer. Image properties of the void image and displacement image are corrected and then compared to a binary expected image from a computer generated model to identify defects in the just-deposited layer on a layer-by-layer basis. Additional methods use the output from the comparison to form a 3D model corresponding to at least a portion of the additive manufacturing product. Components to control the additive manufacturing operation based on digital model data and to in-situ monitor successive layers for manufacturing defects can be embodied in a computer system or computer-aided machine, such as a computer controlled additive manufacturing machine.

Abstract: Methods to in-situ monitor production of additive manufacturing products collects images from the deposition process on a layer-by-layer basis, including a void image of the pattern left in a slurry layer after deposition of a layer and a displacement image formed by immersing the just-deposited layer in a renewed slurry layer. Image properties of the void image and displacement image are corrected and then compared to a binary expected image from a computer generated model to identify defects in the just-deposited layer on a layer-by-layer basis. Additional methods use the output from the comparison to form a 3D model corresponding to at least a portion of the additive manufacturing product. Components to control the additive manufacturing operation based on digital model data and to in-situ monitor successive layers for manufacturing defects can be embodied in a computer system or computer-aided machine, such as a computer controlled additive manufacturing machine.

Abstract: Methodologies and manufacturing processes to manufacture components by electron beam melting additive manufacturing, particularly components of molybdenum or a molybdenum-based alloy and particularly of complex nuclear component geometries. Input parameters are provided for controlling electron beam melting additive manufacturing equipment, such as electron beam melting machines. The input parameters relate to various process steps, including build set-up, initial thermal treatment, initial layering of powder, pre-consolidation thermal treatment, consolidation, post-consolidation thermal treatment, indexing of layers, and post-build thermal treatment. The methodologies and manufacturing processes allow manufacture of components of molybdenum having a purity of ?99.0% and a density of ?99.75%. Metallographic cross-sections of the manufactured molybdenum components were porosity-free and crack-free.

Abstract: A method is provided for enhancing image resolution for sequences of 2-D images of additively manufactured products. For each of a plurality of additive manufacturing processes, the process obtains a respective plurality of sequenced low-resolution 2-D images of a respective product during the respective additive manufacturing process and obtains a respective high-resolution 3-D image of the respective product after completion of the respective additive manufacturing process. The process selects tiling maps that subdivide the low-resolution 2-D images and the high-resolution 3-D images into low-resolution tiles and high-resolution tiles, respectively. The process also builds an image enhancement generator iteratively in a generative adversarial network using training inputs that includes ordered pairs of low-resolution and high-resolution tiles.

Abstract: A method inspects weld quality in-situ. The method obtains a plurality of sequenced images of an in-progress welding process and generates a multi-dimensional data input based on the plurality of sequenced images and/or one or more weld process control parameters. The parameters may include: (i) shield gas flow rate, temperature, and pressure; (ii) voltage, amperage, wire feed rate and temperature (if applicable); (iii) part preheat/inter-pass temperature; and (iv) part and weld torch relative velocity). The method generates defect probability and analytics information by applying one or more computer vision techniques on the multi-dimensional data input. The analytics information includes predictive insights on quality features of the in-progress welding process. The method then generates a 3-D visualization of one or more as-welded regions, based on the analytics information, and the plurality of sequenced images.

Abstract: Methods to in-situ monitor production of additive manufacturing products collects images from the deposition process on a layer-by-layer basis, including a void image of the pattern left in a slurry layer after deposition of a layer and a displacement image formed by immersing the just-deposited layer in a renewed slurry layer. Image properties of the void image and displacement image are corrected and then compared to a binary expected image from a computer generated model to identify defects in the just-deposited layer on a layer-by-layer basis. Additional methods use the output from the comparison to form a 3D model corresponding to at least a portion of the additive manufacturing product. Components to control the additive manufacturing operation based on digital model data and to in-situ monitor successive layers for manufacturing defects can be embodied in a computer system or computer-aided machine, such as a computer controlled additive manufacturing machine.