公开(公告)号：US20250147861A1

公开(公告)日：2025-05-08

申请号：US19014557

申请日：2025-01-09

Applicant: General Electric Company

Inventor： Harry Kirk Mathews, JR. ,  Sarah Felix ,  Subhrajit Roychowdhury ,  Saikat Ray Majumder ,  Thomas Spears

IPC: G06F11/34 ,  B29C64/393 ,  B33Y50/02 ,  G06F17/18 ,  G06F30/00

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method includes receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

公开(公告)号：US12222838B2

公开(公告)日：2025-02-11

申请号：US17724704

申请日：2022-04-20

Applicant: General Electric Company

Inventor： Harry Kirk Mathews, Jr. ,  Sarah Felix ,  Subhrajit Roychowdhury ,  Saikat Ray Majumder ,  Thomas Spears

IPC: G06F11/34 ,  B29C64/393 ,  B33Y50/02 ,  G06F17/18 ,  G06F30/00

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

公开(公告)号：US11327870B2

公开(公告)日：2022-05-10

申请号：US16242194

申请日：2019-01-08

Applicant: General Electric Company

Inventor： Harry Kirk Mathews, Jr. ,  Sarah Felix ,  Subhrajit Roychowdhury ,  Saikat Ray Majumder ,  Thomas Spears

IPC: G06F11/34 ,  B29C64/393 ,  G06F17/18 ,  B33Y50/02 ,  G06F30/00

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.