Wear data classification with machine learning for well tools

A system, comprising:

• a training engine configured to train a neural network to categorize common failure modes sustained by used well tools during operation, wherein training includes:
• inputting an input dataset including a plurality of known failure mode images;
• and
• comparing the input dataset to an output generated using the neural network;
• one or more scanners configured to scan a used well tool;
• a computer system in communication with the one or more scanners and configured to, based on the scanning, generate a scanned file of the used well tool;
• and
• an inference engine in communication with the training engine and the computer system, the inference engine configured to generate one or more failure modes sustained by the used well tool by applying the neural network to the scanned file.

A system, comprising:

• one or more scanners arrangeable to scan a used well tool;
• and
• a computer system in communication with the one or more scanners and including a non-transitory, computer readable medium programmed with computer executable instructions that, when executed by a processor of the computer system, performs the steps of:
• generating a scanned file of the used well tool based on a signal received from the one or more scanners, the scanned file being aligned with a reference surface based on a normalized coordinate format;
• generating a normalized coordinate format at the computer system, based in part on dimensional information associated with the used well tool;
• aligning the scanned file with a reference surface of the used well tool sing the normalized coordinate format, wherein the reference surface is defined using a set of points within the normalized coordinate format, and is stored on the computer system;
• and
• calculating deviation between the scanned file and the reference surface using and thereby determining material removed from the individual wear parts of the used well tool.