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 ana...

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Bibliographic Details
Main Authors Beacham, JR., Jimmie A, Jia, Tao
Format Patent
LanguageEnglish
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
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Summary: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.
Bibliography:Application Number: US202017070618