Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Abstract: Systems, apparatus, computer-readable medium, and associated methods to monitor, analyze, and adjust at least one of 1) additive machine health and configuration or 2) build health and configuration are disclosed. An example apparatus includes an analytics processor, separate from and in a trusted relationship with an additive manufacturing machine building a part, to process, based on a trigger, data from monitoring of the additive manufacturing machine and the build of the part, the analytics processor including a hybrid model fusing additive process physics and data science to process the data to identify an abnormality in at least one of the build or the additive manufacturing machine and to adjust a configuration of the additive manufacturing machine during the build to address the abnormality.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Abstract: An additive manufacturing system may include an additive manufacturing machine and a control system. The additive manufacturing machine may include one or more irradiation devices respectively including a beam source configured to emit an energy beam, an optical assembly that has one or more optical elements configured to focus the energy beam emitted by the beam source, a beam source sensor configured to determine a beam source sensor value from a source measurement beam representative of the energy beam prior to the energy beam passing through one or more optical elements of the optical assembly, and an optics sensor configured to determine an optics sensor value from an optics measurement beam representative of the energy beam downstream from the one or more optical elements of the optical assembly.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

Abstract: A system includes a plurality of movable members and an electromagnetic wave probe disposed within at least one movable member among the plurality of movable members. The actuation system is coupled to the plurality of movable members. The actuation system is configured to actuate and removably position the plurality of movable members to form a wave guide around at least a portion of an outer peripheral surface of the pipe assembly and position the electromagnetic wave probe proximate to the portion of the outer peripheral surface of the pipe assembly.

Abstract: A system includes a plurality of movable members and an electromagnetic wave probe disposed within at least one movable member among the plurality of movable members. The actuation system is coupled to the plurality of movable members. The actuation system is configured to actuate and removably position the plurality of movable members to form a wave guide around at least a portion of an outer peripheral surface of the pipe assembly and position the electromagnetic wave probe proximate to the portion of the outer peripheral surface of the pipe assembly.

Abstract: A cutting tool abnormality sensing apparatus is disclosed. The apparatus includes an image pickup device for taking individual images of teeth of the cutting tool. The apparatus further includes a triggering mechanism for triggering the image pickup device each time a tooth passes along a field of view of the image pickup device. One or more light sources are provided to illuminate the cutting tool. Further, the apparatus includes an image processor to process one or more criteria in the individual images for quantifying an extent of abnormality in the cutting tool.

Abstract: A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.

Abstract: A method for processing a radiographic image of a scanned object is provided. The method comprises acquiring radiographic image data corresponding to a scanned object and identifying one or more regions of interest in the radiographic image data corresponding to the scanned object. The method further comprises performing an image-contrast comparison of the radiographic image data corresponding to the scanned object and one or more reference radiographic images, to identify one or more defects in the radiographic image data corresponding to the scanned object.

Abstract: A method for processing a radiographic image of a scanned object is provided. The method comprises acquiring radiographic image data corresponding to a scanned object and identifying one or more regions of interest in the radiographic image data corresponding to the scanned object. The method further comprises performing an image-contrast comparison of the radiographic image data corresponding to the scanned object and one or more reference radiographic images, to identify one or more defects in the radiographic image data corresponding to the scanned object.

Abstract: A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.