Title: Accelerating In-Process Automatic Inspection Engineering: Removing Inhibitors to Rapid Machine Learning-Based Application Development
Authors: Scott Blake, Amir Atai
Abstract: Although in-process automatic inspection has made inroads in the fabrication of low-volume flight-critical composite components (<1,000 per year), novel enabling technologies are needed to meet the needs of new high-volume structures. For such applications, an automatic inspection technology must be able to verify (and/or flag for nonconformance) a unique set of features and potential anomalies arising from the particulars of each structure’s design and production process. Therefore, each component requires its own inspection engineering employing: (1) The means and methodology to capture a database of calibrated images that display all the features and anomalies that the automatic inspection system must detect and/or measure; and (2) Application of artificial intelligence/machine learning (AI/ML) to develop the algorithms that analyze the captured images. Critically important to widespread use, this inspection system cannot rely on complex AI/ML coding and optimization for each application, which would be time- and cost-prohibitive. The remedy is a low-code AI/ML workflow engine that will democratize inspection application development. This paper will outline the requirements for automatic inspection of high-volume composite components and describe the enabling technologies of (1) image database generation using a machine vision system and (2) democratized AI/ML-based algorithm development.
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Conference: SAMPE NEXUS 2021
Publication Date: 2021/06/29
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