Predicting Failure in Additively Manufactured Parts-The Effects of Defects

被引：0

作者：

Peitsch, Christopher M. ^{[1
]}

Storck, Steven M. ^{[1
]}

McCue, Ian D. ^{[1
]}

Montalbano, Timothy J. ^{[1
]}

Nimer, Salahudin M. ^{[1
]}

Trigg, Douglas B. ^{[1
]}

Drenkow, Nathan G. ^{[1
]}

Sopcisak, Joseph ^{[1
]}

Carter, Ryan H. ^{[1
]}

Trexler, Morgana M. ^{[1
]}

机构：

[1] Johns Hopkins University, Applied Physics Laboratory, Laurel,MD, United States

来源：

Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | 2021年 / 35卷 / 04期

关键词：

Additives - Data handling - Failure (mechanical);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

While the use of metal additive manufacturing (AM) has grown immensely over the past decade, there still exists a gap in understanding of process defects in AM, which often inhibit its use in critical applications such as flight hardware. The Johns Hopkins University Applied Physics Laboratory (APL) is developing novel techniques to replicate authentic surrogate defects in AM parts and characterize their effect on mechanical response. Advanced data processing methods, such as machine learning, are being leveraged to develop predictive failure models, which will help enhance our understanding of the effects of defects. © 2021 John Hopkins University. All rights reserved.

引用

页码：418 / 421

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