Patents by Inventor Damas Limoge

Damas Limoge has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11063965
    Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.
    Type: Grant
    Filed: February 4, 2020
    Date of Patent: July 13, 2021
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20210194897
    Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.
    Type: Application
    Filed: February 4, 2020
    Publication date: June 24, 2021
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge, Andrew Sundstrom, James Williams, III
  • Publication number: 20210138735
    Abstract: A manufacturing system is disclosed herein. The manufacturing system may include one or more station, a monitoring platform, and a control module. Each station is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.
    Type: Application
    Filed: November 6, 2020
    Publication date: May 13, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Damas Limoge, Fabian Hough, Sadegh Nouri Gooshki, Aswin Raghav Nirmaleswaran, Vadim Pinskiy
  • Publication number: 20210132593
    Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.
    Type: Application
    Filed: November 6, 2020
    Publication date: May 6, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Andrew Sundstrom, Damas Limoge, Eun-Sol Kim, Vadim Pinskiy, Matthew C. Putman
  • Publication number: 20210069990
    Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a product. The monitoring platform is configured to monitor progression of the product throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the product.
    Type: Application
    Filed: September 9, 2020
    Publication date: March 11, 2021
    Applicant: Nanotronics Imaging, Inc.
    Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
  • Publication number: 20200401119
    Abstract: Aspects of the disclosed technology encompass the use of a deep learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving a plurality of control values from two or more stations, at a deep learning controller, wherein the control values are generated at the two or more stations deployed in a manufacturing process, predicting an expected value for an intermediate or final output of an article of manufacture, based on the control values, and determining if the predicted expected value for the article of manufacture is in-specification. In some aspects, the process can further include steps for generating control inputs if the predicted expected value for the article of manufacture is not in-specification. Systems and computer-readable media are also provided.
    Type: Application
    Filed: July 23, 2019
    Publication date: December 24, 2020
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20200401120
    Abstract: Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.
    Type: Application
    Filed: October 24, 2019
    Publication date: December 24, 2020
    Inventors: Matthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
  • Publication number: 20200247063
    Abstract: Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.
    Type: Application
    Filed: April 20, 2020
    Publication date: August 6, 2020
    Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
  • Publication number: 20200247061
    Abstract: Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.
    Type: Application
    Filed: December 20, 2019
    Publication date: August 6, 2020
    Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
  • Patent number: 10518480
    Abstract: Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.
    Type: Grant
    Filed: April 2, 2018
    Date of Patent: December 31, 2019
    Assignee: Nanotronics Imaging, Inc.
    Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
  • Publication number: 20190299536
    Abstract: Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.
    Type: Application
    Filed: April 2, 2018
    Publication date: October 3, 2019
    Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris