Apparatus and method for in-manufacturing evaluation of structural and material properties of fasteners using machine learning

A method for automatically detecting anomalies in the structure or material forming a fastener utilized in the assembly of a medical device, the method comprising the steps of:

• a. providing an apparatus comprising:
• i. a driving tool capable of recording an angle torque trace for a fastener driven by the tool to form a recorded trace;
• and
• ii. a processor operably connected to the driving tool and including an machine learning engine stored on the processor;
• b. creating a stored angle torque trace for comparison with the recorded trace components with the machine learning engine;
• c. driving a fastener to secure components of a medical device with the driving tool;
• d. recording data forming the angle-torque trace while driving the fastener to form the recorded trace;
• and
• e. comparing the stored angle-torque trace c with the recorded trace to detect anomalies,
• wherein the step of creating the stored trace comprises:
• i. providing a number of sample angle-torque traces to the machine learning engine;
• ii. conducting an unsupervised learning protocol with the machine learning engine on the sample angle-torque traces;
• and
• iii. defining attributes for the stored trace based on anomalies determined by the machine learning engine within the sample traces.