Predicting Failure in Additively Manufactured Parts-"The Effects of Defects"

Christopher M. Peitsch, Steven M. Storck, Ian D. McCue, Timothy J. Montalbano, Salahudin M. Nimer, Douglas B. Trigg, Nathan G. Drenkow, Joseph Sopcisak, Ryan H. Carter, Morgana M. Trexler

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)418-421
Number of pages4
JournalJohns Hopkins APL Technical Digest (Applied Physics Laboratory)
Issue number4
StatePublished - 2021

ASJC Scopus subject areas

  • General Engineering
  • General Physics and Astronomy


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