Abstract: A method for self-assessing components of a cargo handling system configured for autonomous control by a processor is disclosed. In various embodiments, the method includes receiving by the processor a sensor database from a plurality of sensing agents in operable communication with the processor; testing by the processor the plurality of sensing agents by comparing the sensor database against a sensor assessment; and performing by the processor a first calibration assessment of the plurality of sensing agents to assess whether a compromised operational status of the cargo handling system has occurred due to a failure of one or more of the plurality of sensing agents.

Abstract: A method for designing a material for an aircraft component according to one example includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy. Each of the images in the set of images has varied constituent compositions and at least one patch of corresponding data is embedded into the image. The method also includes determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.

Abstract: A process for automated component inspection includes the steps of calibrating an imaging device mounted on a table; calibrating a coordinate measuring machine mounted on the table, the coordinate measuring machine comprising a fixture coupled to an arm of the coordinate measuring machine; coupling a component to the fixture; acquiring an image of said component with said imaging device; registering a baseline dimensioned image to the component image; applying the baseline dimensioned image to a damage detection algorithm; and determining component damage by the damage detection algorithm.

Abstract: A tool for monitoring a part condition includes a computerized device having a processor and a memory. The computerized device includes at least one of a camera and an image input and a network connection configured to connect the computerized device to a data network. The memory stores instructions for causing the processor to perform the steps of providing an initial micrograph of a part to a trained model, providing a data set representative of operating conditions of the part to the trained model, and outputting an expected state of the part from the trained model based at least in part on the input data set and the initial micrograph.

Abstract: A method for self-assessing components of a cargo handling system configured for autonomous control by a processor is disclosed. In various embodiments, the method includes receiving by the processor a sensor database from a plurality of sensing agents in operable communication with the processor; testing by the processor the plurality of sensing agents by comparing the sensor database against a sensor assessment; and performing by the processor a first calibration assessment of the plurality of sensing agents to assess whether a compromised operational status of the cargo handling system has occurred due to a failure of one or more of the plurality of sensing agents.

Abstract: A communication network configured for connection with a cargo handling system and a third party is disclosed. In various embodiments, the communication network includes a first processor; a first communication link connecting the first processor to the cargo handling system, the cargo handling system including a plurality of sensing agents configured to monitor a status of a cargo load; and a second communication link connecting the first processor to the third party, where the first processor is configured to receive a first load status data from the cargo handling system over the first communication link and to transmit a second load status data to the third party over the second communication link.

Abstract: A method for adjusting a system autonomy level of a cargo handling system configured for autonomous control by a processor is disclosed. In various embodiments, the method includes receiving by the processor a sensor database from a plurality of sensing agents in operable communication with the processor; determining by the processor a confidence level based on the sensor database; and adjusting by the processor the system autonomy level for continued operation of the cargo handling system.

Abstract: A tracking system for communicating real-time information concerning a cargo load on a cargo handling system to a third party is disclosed. In various embodiments, the system includes a processor; a first communication link connecting the processor to the cargo handling system, the cargo handling system including a plurality of sensing agents configured to monitor a status of the cargo load; and a second communication link connecting the processor to the third party, where the processor is configured to receive a first load status data from the cargo handling system over the first communication link and to transmit a second load status data to the third party over the second communication link.

Abstract: A method for robotic inspection of a part, includes the steps of: supporting the part with a robot mechanism; obtaining part-related sensor input with a sensor positioned to inspect the part supported by the robot mechanism; and controlling movement of the robot mechanism relative to the sensor, wherein the controlling is done by a feedback control unit which receives the sensor input, and the feedback control unit is configured to control the robot mechanism based upon the sensor input.

Abstract: A method for designing a material for an aircraft component according to one example includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy. Each of the images in the set of images has varied constituent compositions and at least one patch of corresponding data is embedded into the image. The method also includes determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.

Abstract: A tool for monitoring a part condition includes a computerized device having a processor and a memory. The computerized device includes at least one of a camera and an image input and a network connection configured to connect the computerized device to a data network. The memory stores instructions for causing the processor to perform the steps of providing an initial micrograph of a part to a trained model, providing a data set representative of operating conditions of the part to the trained model, and outputting an expected state of the part from the trained model based at least in part on the input data set and the initial micrograph.

Abstract: A process for automated component inspection includes the steps of calibrating an imaging device mounted on a table; calibrating a coordinate measuring machine mounted on the table, the coordinate measuring machine comprising a fixture coupled to an arm of the coordinate measuring machine; coupling a component to the fixture; acquiring an image of said component with said imaging device; registering a baseline dimensioned image to the component image; applying the baseline dimensioned image to a damage detection algorithm; and determining component damage by the damage detection algorithm.