Component assessment method and apparatus

A method to assess a first component, the method comprising:

• accessing the first component;
• prior to inspecting the first component, physically processing the first component;
• inspecting the first component to provide inspection results that include a plurality of potential wear indications;
• inputting the inspection results to a first machine learning model that is trained using a training corpus that includes inspection results for previously inspected components that are at least similar to the first component by being at least a same categorical type and serving a same operational purpose as the first component, and wherein the first machine learning model outputs assessment information that identifies some of the potential wear indications as being relevant and some of the potential wear indications as not being relevant;
• and
• determining whether to return the first component to active service as a function, at least in part, of the assessment information;
• wherein when determining whether to return the first component to active service as a function, at least in part, of the assessment information yields an inconclusive determination:
• inputting at least some of the assessment information as output by the first machine learning model to a second machine learning model that is trained using a training corpus that includes historical results from previous inspections of the first component;
• and
• outputting from the second machine learning model prediction information regarding whether a repeated physical processing of the first component will yield a particular result.

An apparatus to assess a first component, the apparatus comprising:

• a memory having stored therein inspection results from an inspection of the first component, where the inspection results include a plurality of potential wear indications;
• and
• a control circuit operably coupled to the memory and configured to:
• access the inspection results;
• and
• input the inspection results to a first machine learning model that is trained using a training corpus that includes inspection results for previously inspected components that are at least similar to the first component and wherein the first machine learning model outputs assessment information that identifies some of the potential wear indications as being relevant and some of the potential wear indications as not being relevant, and
• wherein the assessment information informs a determination regarding whether to return the first component to active service;
• input at least some of the assessment information as output by the first machine learning model to a second machine learning model that is trained using a training corpus that includes historical results from previous inspections of the first component;
• and
• output from the second machine learning model prediction information regarding whether a repeated physical processing of the first component will yield a particular result.