Apparatus And Method For In-Manufacturing Evaluation Of Structural And Material Properties Of Fasteners Using Machine Learning

An apparatus and method for detecting structural and material defects in a fastener driven during a manufacturing process includes a driving tool capable of recording an angle-torque trace during the driving of the fastener and a machine learning engine operably connected to the driving tool for analyzing the recorded angle-torque trace. The machine learning engine can be provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process or protocol without the need for defined anomalous and non-anomalous samples being provided to the machine learning engine. Using the self-defined stored trace and acceptable tolerances, the machine learning engine can analyze attributes of subsequently recorded angle-torque traces to ascertain whether the attributes of the recorded angle-torque traces indicate anomalies within the fastener identified by the recorded trace.