Abstract: A system for optimizing DBS parameters for a subject includes automatically performing actions via a processor. The actions include obtaining functional MRI data of a brain of the subject acquired utilizing an MRI system during DBS of the brain utilizing a first set of DBS parameters. The actions include generating functional MRI response maps from the functional MRI data. The actions include extracting, utilizing an unsupervised autoencoder-based neural network, features from the functional MRI response maps. The actions include determining, utilizing a deep learning-based DBS parameter classification model, whether the first set of DBS parameters are optimal DBS parameters for the subject based on the features. The actions include, when the first set of DBS parameters are not the optimal DBS parameters, predicting, utilizing a deep learning-based DBS parameter prediction model, a second set of DBS parameters that are the optimal DBS parameters for the subject based on the features.

Abstract: A first nest structure and a second nest structure are brought into alignment. When the alignment of the first nest structure and second nest structure is obtained, the operation of at least one vacuum pump is controlled such that a bearing element is released from the first nest structure and secured in the second nest structure. Movement of the bearing element from the first nest structure to the second nest structure occurs without slippage resulting in preservation of a common frame of reference used when the bearing element is in the first nest structure and the second nest structure.

Abstract: A positioning system is provided for insertions and placements with increased accuracy and precision for the placement and insertion of components into elements. The system may utilize one or more sensors to provide individual images or data for each individual insertion of components into elements. The system may use known information to compare the individual images or data to provide increased accuracy and precision for insertion of components into elements.

Abstract: A system includes a first sensor having a fixed location relative to a workspace, a second sensor, at least one robotic manipulator coupled to a manipulation tool, and a control system in communication with the at least one robotic manipulator. The control system is configured to determine a location of a workpiece in the workspace based on first sensor data from the first sensor and a three-dimensional (3D) model corresponding to the workpiece. The control system is configured to map a set of 2D coordinates from a second 2D image from the second sensor to a set of 3D coordinates based on the location, and to generate one or more control signals for the at least one robotic manipulator based on the set of 3D coordinates.

Abstract: Systems and methods that include and/or leverage a cluster of machine-learned models to recontour components of gas turbine engines are provided. In one exemplary aspect, the systems and methods leverage a cluster of machine-learned models to predict repair machining offsets for certain sections of the component that can be used to adjust or set a material removal tool path.

Abstract: An inspection system includes one or more imaging devices and one or more processors. The imaging devices generate a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece. At least some of the images in the first and second sets are acquired using different light settings. The processors analyze the first set of images to generate a first prediction image associated with the first position, and analyze the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. The processors merge the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions.

Abstract: An inspection system includes an imaging device, visible light source, ultraviolet light source, and at least one processor. The imaging device generates a first image set of a work piece while the ultraviolet light source illuminates the work piece with ultraviolet light to cause fluorescent dye thereon to emit light, and generates a second image set of the work piece while the visible light source illuminates the work piece with visible light. The first and second image sets are generated at the same positions of the imaging device relative to the work piece. The processor maps the second image set to a computer design model of the work piece based on features depicted in the second image set and the positions of the imaging device. The processor determines a defect location on the work piece based on an analysis of the first image set and the computer design model.

Abstract: An inspection system includes one or more processors that obtain a first image of a work piece that has a fluorescent dye thereon in an ultraviolet (UV) light setting and a second image of the work piece in a visible light setting. The first and second images are generated by one or more imaging devices in the same position relative to the work piece. The one or more processors identify a candidate region of the first image based on a light characteristic of one or more pixels, and determine a corresponding candidate region of the second image that is at an analogous location as the candidate region of the first image. The one or more processors analyze both candidate regions to detect a potential defect on a surface of the work piece and a location of the potential defect relative to the surface of the work piece.

Abstract: An inspection system includes one or more imaging devices and one or more processors. The imaging devices generate a first set of images of a work piece at a first position relative to the work piece and a second set of images of the work piece at a second position relative to the work piece. At least some of the images in the first and second sets are acquired using different light settings. The processors analyze the first set of images to generate a first prediction image associated with the first position, and analyze the second set of images to generate a second prediction image associated with the second position. The first and second prediction images include respective candidate regions. The processors merge the first and second prediction images to detect at least one predicted defect in the work piece depicted in at least one of the candidate regions.

Abstract: An inspection system includes an imaging device, visible light source, ultraviolet light source, and at least one processor. The imaging device generates a first image set of a work piece while the ultraviolet light source illuminates the work piece with ultraviolet light to cause fluorescent dye thereon to emit light, and generates a second image set of the work piece while the visible light source illuminates the work piece with visible light. The first and second image sets are generated at the same positions of the imaging device relative to the work piece. The processor maps the second image set to a computer design model of the work piece based on features depicted in the second image set and the positions of the imaging device. The processor determines a defect location on the work piece based on an analysis of the first image set and the computer design model.

Abstract: An inspection system includes one or more processors that obtain a first image of a work piece that has a fluorescent dye thereon in an ultraviolet (UV) light setting and a second image of the work piece in a visible light setting. The first and second images are generated by one or more imaging devices in the same position relative to the work piece. The one or more processors identify a candidate region of the first image based on a light characteristic of one or more pixels, and determine a corresponding candidate region of the second image that is at an analogous location as the candidate region of the first image. The one or more processors analyze both candidate regions to detect a potential defect on a surface of the work piece and a location of the potential defect relative to the surface of the work piece.

Abstract: A system includes a first sensor having a fixed location relative to a workspace, a second sensor, at least one robotic manipulator coupled to a manipulation tool, and a control system in communication with the at least one robotic manipulator. The control system is configured to determine a location of a workpiece in the workspace based on first sensor data from the first sensor and a three-dimensional (3D) model corresponding to the workpiece. The control system is configured to map a set of 2D coordinates from a second 2D image from the second sensor to a set of 3D coordinates based on the location, and to generate one or more control signals for the at least one robotic manipulator based on the set of 3D coordinates.

Abstract: Systems and methods that include and/or leverage a cluster of machine-learned models to recontour components of gas turbine engines are provided. In one exemplary aspect, the systems and methods leverage a cluster of machine-learned models to predict repair machining offsets for certain sections of the component that can be used to adjust or set a material removal tool path.

Abstract: A system includes one or more processors configured to create a projection matrix based on a three-dimensional (3D) model of a part and sensor data associated with a workpiece in a workspace of a robotic manipulator. The projection matrix provides a mapping between sensor coordinates associated with the sensor data and 3D coordinates associated with the 3D model. The one or more processors are configured to identify a set of sensor coordinates from the sensor data corresponding to a feature indication associated with the workpiece, and to determine from the set of sensor coordinates a set of 3D coordinates using the projection matrix.