Abstract: Systems and methods of analyzing aligning a golden template with a scanned image of specimen under manufacture are disclosed herein. A computing system generates a golden template for a patterned specimen design by forming a single coherent image from a first plurality of scanned images of a plurality of specimens manufactured according to the patterned specimen design. The computing system identifies a plurality of regions of interest in the patterned specimen design that is present across a second plurality of scanned images of the plurality of specimens manufactured according to the patterned specimen design. The computing system receives a new scanned image of a new specimen manufactured in accordance with the patterned specimen design. The computing system aligns the golden template with the new scanned image of the new specimen by performing a pixel-by-pixel analysis in the plurality of regions of interest only.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes a monitoring platform and an analytics platform. The monitoring platform is configured to capture data of an operator during assembly of an article of manufacture. The monitoring platform includes one or more cameras and one or more microphones. The analytics platform is in communication with the monitoring platform. The analytics platform is configured to analyze the data captured by the monitoring platform.

Abstract: A computing system generates a training data set for training a machine learning model to detect line defects on a surface of a specimen. The computing system trains the machine learning model to detect line defects based on the training data set. The machine learning model is trained to detect line defects by detecting the individual defects represented by the plurality of annotations extending between the starting point and the end point. The computing system determines that the machine learning model has exceeded a threshold level of accuracy. Based on the determining, the computing system deploys the machine learning model to detect line defects on future specimens.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes a monitoring platform and an analytics platform. The monitoring platform is configured to capture data of an operator during assembly of an article of manufacture. The monitoring platform includes one or more cameras and one or more microphones. The analytics platform is in communication with the monitoring platform. The analytics platform is configured to analyze the data captured by the monitoring platform.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes a monitoring platform and an analytics platform. The monitoring platform is configured to capture data of an operator during assembly of an article of manufacture. The monitoring platform includes one or more cameras and one or more microphones. The analytics platform is in communication with the monitoring platform. The analytics platform is configured to analyze the data captured by the monitoring platform.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

Abstract: A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.