Quality Assessment of Metal Additive Manufactured Parts by a Multiscale Convolutional Fuzzy Neural Network Using Ultrasound Images as Input Data
Author:
Affiliation:
1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
2. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10301565.pdf?arnumber=10301565
Reference38 articles.
1. Nondestructive testing for metal parts fabricated using powder-based additive manufacturing;koester;Mater Eval,2018
2. DefectDet: A deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images
3. Inspection of additive-manufactured layered components
4. Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis
5. Ultrasonic nondestructive evaluation of laser powder bed fusion 316L stainless steel
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