公开(公告)号：US20240342831A1

公开(公告)日：2024-10-17

申请号：US18752404

申请日：2024-06-24

Applicant: General Electric Company

Inventor： Naresh S. Iyer ,  Subhrajit Roychowdhury ,  Christopher D. Immer ,  Xiaohu Ping ,  Rogier S. Blom ,  Jing Yu

IPC: B23K26/342 ,  B23K26/03 ,  B23K26/06 ,  B23K26/073 ,  B23K26/082 ,  B23K31/00 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y50/02

CPC classification number: B23K26/342 ,  B23K26/032 ,  B23K26/0626 ,  B23K26/073 ,  B23K26/082 ,  B23K31/003 ,  B23K31/006 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y50/02

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

公开(公告)号：US20210283717A1

公开(公告)日：2021-09-16

申请号：US16818650

申请日：2020-03-13

Applicant: General Electric Company

Inventor： Naresh S. Iyer ,  Subhrajit Roychowdhury ,  Christopher D. Immer ,  Xiaohu Ping ,  Rogier S. Blom ,  Jing Yu

IPC: B23K26/342 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y50/02 ,  B23K26/03 ,  B23K26/06 ,  B23K26/073 ,  B23K26/082 ,  B23K31/00

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

公开(公告)号：US12017301B2

公开(公告)日：2024-06-25

申请号：US16818650

申请日：2020-03-13

Applicant: General Electric Company

Inventor： Naresh S. Iyer ,  Subhrajit Roychowdhury ,  Christopher D. Immer ,  Xiaohu Ping ,  Rogier S. Blom ,  Jing Yu

IPC: B23K26/342 ,  B23K26/03 ,  B23K26/06 ,  B23K26/073 ,  B23K26/082 ,  B23K31/00 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y50/02

CPC classification number: B23K26/342 ,  B23K26/032 ,  B23K26/0626 ,  B23K26/073 ,  B23K26/082 ,  B23K31/003 ,  B23K31/006 ,  B33Y10/00 ,  B33Y30/00 ,  B33Y50/02

Abstract: An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.