Material selection and optimization process for component manufacturing

• Main Authors: Somanath, Nagendra, Noraas, Ryan B, Giering, Michael J
• Format: Patent
• Language: English
• Published: 01.11.2022
• Subjects: AIRCRAFT; AVIATION; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; COSMONAUTICS; COUNTING; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING ORREPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLYADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; HANDLING RECORD CARRIERS; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFTCOMPONENTS, NOT OTHERWISE PROVIDED FOR; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PERFORMING OPERATIONS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS; REGULATING; TRANSPORTING

A method for designing a material for an aircraft component 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 to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and 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.