Patents by Inventor Aswin Raghav Nirmaleswaran
Aswin Raghav Nirmaleswaran 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).
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Publication number: 20230391016Abstract: 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: ApplicationFiled: August 21, 2023Publication date: December 7, 2023Applicant: Nanotronics Imaging, Inc.Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
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Patent number: 11731368Abstract: 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: GrantFiled: August 6, 2021Date of Patent: August 22, 2023Assignee: Nanotronics Imaging, Inc.Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
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Publication number: 20220269254Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.Type: ApplicationFiled: February 25, 2022Publication date: August 25, 2022Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Andrew Sundstrom, Damas Limoge, Vadim Pinskiy, Aswin Raghav Nirmaleswaran, Eun-Sol Kim
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Publication number: 20220024140Abstract: 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: ApplicationFiled: August 6, 2021Publication date: January 27, 2022Applicant: Nanotronics Imaging, Inc.Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
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Publication number: 20210394456Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a computing system. The computing system receives an image of the product at a step of the multi-step manufacturing process. The computing system determines a current state of the product based on the image of the product. The computing system determines, via a deep learning model, that the product is not within specification based on the current state of the product and the image of the product. Based on the determining, the computing system adjusts a control logic for at least a following station. The adjusting includes generating, by the deep learning model, a corrective action to be performed by the following station.Type: ApplicationFiled: September 8, 2021Publication date: December 23, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
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Publication number: 20210387421Abstract: 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: ApplicationFiled: August 23, 2021Publication date: December 16, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
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Patent number: 11117328Abstract: 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: GrantFiled: September 9, 2020Date of Patent: September 14, 2021Assignee: Nanotronics Imaging, Inc.Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
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Publication number: 20210263495Abstract: 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: ApplicationFiled: February 19, 2021Publication date: August 26, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Sadegh Nouri Gooshki, Aswin Raghav Nirmaleswaran, Fabian Hough
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Patent number: 11097490Abstract: 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: GrantFiled: December 20, 2019Date of Patent: August 24, 2021Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
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Patent number: 11084225Abstract: 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: GrantFiled: April 20, 2020Date of Patent: August 10, 2021Assignee: NANOTRONICS IMAGING, INC.Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
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Publication number: 20210192779Abstract: 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: ApplicationFiled: March 9, 2021Publication date: June 24, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, Andrew Sundstrom, Aswin Raghav Nirmaleswaran, Eun-Sol Kim
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Publication number: 20210138735Abstract: 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: ApplicationFiled: November 6, 2020Publication date: May 13, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Damas Limoge, Fabian Hough, Sadegh Nouri Gooshki, Aswin Raghav Nirmaleswaran, Vadim Pinskiy
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Publication number: 20210069990Abstract: 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: ApplicationFiled: September 9, 2020Publication date: March 11, 2021Applicant: Nanotronics Imaging, Inc.Inventors: Fabian Hough, John B. Putman, Matthew C. Putman, Vadim Pinskiy, Damas Limoge, Aswin Raghav Nirmaleswaran, Sadegh Nouri Gooshki
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Publication number: 20200247061Abstract: 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: ApplicationFiled: December 20, 2019Publication date: August 6, 2020Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
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Publication number: 20200247063Abstract: 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: ApplicationFiled: April 20, 2020Publication date: August 6, 2020Inventors: Vadim Pinskiy, Matthew C. Putman, Damas Limoge, Aswin Raghav Nirmaleswaran
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Patent number: 10518480Abstract: 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: GrantFiled: April 2, 2018Date of Patent: December 31, 2019Assignee: Nanotronics Imaging, Inc.Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris
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Publication number: 20190299536Abstract: 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: ApplicationFiled: April 2, 2018Publication date: October 3, 2019Inventors: Matthew C. Putman, Vadim Pinskiy, James Williams, III, Damas Limoge, Aswin Raghav Nirmaleswaran, Mario Chris