计算机辅助设计
计算机科学
硬化(计算)
材料科学
涡轮叶片
人工神经网络
工件(错误)
过程(计算)
人工智能
机械工程
工程制图
涡轮机
工程类
复合材料
操作系统
图层(电子)
作者
Amirkoushyar Ziabari,Singanallur Venkatakrishnan,Michael M. Kirka,Paul Brackman,Ryan R. Dehoff,Philip R. Bingham,Vincent Paquit
标识
DOI:10.1115/imece2020-23766
摘要
Abstract Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.
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