Nanotronics Imaging, Inc.

Aswin Raghav Nirmaleswaran

Matthew C Putman

Vadim Pinskiy

Damas Limoge

Andrew Sundstrom

James Williams, Iii

Eun-Sol Kim

Sadegh Nouri Gooshki

Mario Chris

Fabian Hough

John B Putman

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.

Systems, methods, and media for manufacturing processes

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.

Imitation learning in a manufacturing environment

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.

Systems, methods, and media for artificial intelligence process control in additive manufacturing

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.

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.

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Brooklyn, NY, United States of America

Aswin Raghav Nirmaleswaran