公开(公告)号：US11625483B2

公开(公告)日：2023-04-11

申请号：US16887623

申请日：2020-05-29

Applicant: General Electric Company

Inventor： Johan Reimann ,  Nurali Virani ,  Naresh Iyer ,  Zhaoyuan Yang

IPC: G06F21/56 ,  G06N7/00 ,  G06N3/08 ,  G06K9/62 ,  G06N3/084 ,  G06N20/10

Abstract: A system and method including receiving a set of deep neural networks (DNN) including DNNs trained with an embedded trojan and DNNs trained without any embedded trojan, each of the trained DNNs being represented by a mathematical formulation learned by the DNNs and expressing a relationship between an input of the DNNs and an output of the DNNs; extracting at least one characteristic feature from the mathematical formulation of each of the trained DNNs; statistically analyzing the at least one characteristic feature to determine whether there is a difference between the DNNs trained with the embedded trojan and the DNNs trained without any embedded trojan; generating, in response to the determination indicating there is a difference, a detector model to execute the statistical analyzing on deep neural networks; and storing a file including the generated detector model in a memory device.

公开(公告)号：US11580430B2

公开(公告)日：2023-02-14

申请号：US16257367

申请日：2019-01-25

Applicant: General Electric Company

Inventor： Lembit Salasoo ,  Vipul K. Gupta ,  Xiaohu Ping ,  Subhrajit Roychowdhury ,  Justin Gambone, Jr. ,  Naresh Iyer ,  Xiaolei Shi ,  Mengli Wang

IPC: G06N7/00 ,  G06N20/00 ,  G06F30/00 ,  B22F10/20 ,  B33Y10/00 ,  B33Y50/02 ,  G06F119/18 ,  B22F10/30

Abstract: Determining a quality score for a part manufactured by an additive manufacturing machine based on build parameters and sensor data without the need for extensive physical testing of the part. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. A first algorithm is applied to the first set of build parameters and the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The quality score is output via the communication interface of the device.

公开(公告)号：US11481664B2

公开(公告)日：2022-10-25

申请号：US16122184

申请日：2018-09-05

Applicant: General Electric Company

Inventor： Subhrajit Roychowdhury ,  Naresh Iyer

IPC: G06N20/00 ,  B33Y40/00 ,  B33Y50/02 ,  G06F30/23 ,  G06F119/18

Abstract: A method of transferring operational parameter sets between different domains of additive manufacturing machines includes creating a first machine domain parameter set in a first machine domain, accessing a model of a second additive manufacturing in a second machine domain, creating a second machine domain parameter set by applying transfer learning techniques including learning differences between the first machine domain and the second machine domain, adjusting the first machine domain parameter set using the differences before incorporation into the second machine domain to obtain the second machine domain parameter set, the second machine domain parameter set representing operational settings for the second additive manufacturing machine, the second additive manufacturing machine producing a product sample, determining if the product sample is within quality assurance metrics, and if the product sample is not within the quality assurance metrics, adjusting the second machine domain parameter set.

公开(公告)号：US10884394B2

公开(公告)日：2021-01-05

申请号：US16127545

申请日：2018-09-11

Applicant: General Electric Company

Inventor： Subhrajit Roychowdhury ,  Thomas Spears ,  Justin Gambone, Jr. ,  Ruijie Shi ,  Naresh Iyer

IPC: G05B19/4099 ,  B33Y50/00

Abstract: A method of calibrating an additive manufacturing machine includes obtaining a model for the additive manufacturing machine, obtaining a baseline sensor data set for a particular additive manufacturing machine, creating a machine-specific nominal fingerprint for the particular additive manufacturing machine with controllable variation for one or more process inputs, producing on the particular additive manufacturing machine a test-page based object, obtaining a current sensor data set of the test-page based object on the particular additive manufacturing machine, estimating a scaling factor or a bias for each of the one or more process inputs from the current data set, and updating a calibration file for the particular additive machine if the estimated scaling error or bias are greater than a respective predetermined tolerance. A system for implementing the method and a non-transitory computer-readable medium are also disclosed.