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

• Main Authors: Beacham, JR., Jimmie A, Jia, Tao
• Format: Patent
• Language: English
• Published: 14.04.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HAND TOOLS; MANIPULATORS; PERFORMING OPERATIONS; PHYSICS; PORTABLE POWER-DRIVEN TOOLS; TOOLS OR BENCH DEVICES NOT OTHERWISE PROVIDED FOR, FORFASTENING, CONNECTING, DISENGAGING OR HOLDING; TRANSPORTING

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.