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公开(公告)号：US20220172330A1
公开(公告)日：2022-06-02
申请号：US17535766
申请日：2021-11-26
发明人： Simon MASON , Ryan Scott KITCHEN , Travis MCFALLS
摘要： 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. The process stores the image enhancement generator for subsequent use to enhance sequences of low-resolution 2-D images captured for products during additive manufacturing.
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公开(公告)号：US20230373155A1
公开(公告)日：2023-11-23
申请号：US18228077
申请日：2023-07-31
IPC分类号： B29C64/129 , B29C64/268 , B29C64/393 , B33Y10/00 , G06T7/00 , G06T7/62 , B33Y50/02 , B22F12/90 , B22F10/85
CPC分类号： B29C64/129 , B29C64/268 , B29C64/393 , B33Y10/00 , G06T7/001 , G06T7/62 , G06T7/0008 , B33Y50/02 , B22F12/90 , B22F10/85 , G06T2207/30164 , B22F10/28
摘要： 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.
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公开(公告)号：US20210318673A1
公开(公告)日：2021-10-14
申请号：US17221885
申请日：2021-04-05
摘要： 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. The 3-D visualization displays the quality features for virtual inspection and/or for determining weld quality.
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公开(公告)号：US20210170676A1
公开(公告)日：2021-06-10
申请号：US16951543
申请日：2020-11-18
IPC分类号： B29C64/129 , B29C64/268 , B29C64/393 , B33Y10/00 , B33Y50/02 , G06T7/00 , G06T7/62
摘要： 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.
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