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research-article

Deep learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control

[+] Author and Article Information
Farhad Imani

Leonhard, Building, 310 S Barnard St University Park, PA 16802 fxi1@psu.edu

Ruimin Chen

310 Leonahrd Building State College, PA 16802 RXC91@psu.edu

Evan P. Diewald

310 Leonhard Building University Park, PA 16801 epd5112@psu.edu

E.W. Reutzel

Penn State University P.O. Box 30 State College, PA 16804-0030 ewr101@psu.edu

Hui Yang

310 Leonhard Building State College, PA 16802 huy25@psu.edu

1Corresponding author.

Manuscript received March 31, 2019; final manuscript received July 21, 2019; published online xx xx, xxxx. Assoc. Editor: Qiang Huang.

ASME doi:10.1115/1.4044420 History: Received March 31, 2019; Accepted July 22, 2019

Abstract

Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in-situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Very little has been done on deep learning of variant geometry for image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel machine learning approach that is focused on variant geometry in each layer of the AM build, namely region of interests, for the characterization and detection of layerwise flaws. Experimental results show that the proposed deep learning methodology is highly effective to detect flaws in each layer with an accuracy of 92.50 ± 1.03%. This provides a significant opportunity to reduce inter-layer variation in AM prior to completion of a build.

Copyright © 2019 by ASME
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