Experimental optimization of laser additive manufacturing process of single-crystal nickel-base superalloys by a statistical experiment design method

Yao Jian Liang, Jia Li*, An Li, Xu Cheng, Shu Wang, Hua Ming Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

Experimental process optimization is essential to obtain reliable processing conditions prior to performing actual laser additive manufacturing (LAM) of single-crystal (SX) nickel-base superalloys. The influence of processing parameters on deposited productivity and epitaxial SX growth in powder-feeding LAM process was systematically investigated by the orthogonal experiment (OE) method, a statistical experiment design method. This method can rapidly and economically estimate the effect of each processing parameter by a small number of experiments. Resulting relationship between the processing variables and each of deposited productivity and microstructure formation contributes to the selection of detailed processing conditions to balance the factors crucial to successful SX LAM, which means that appropriate adjustment of the processing parameters during actual SX LAM is easy to be performed. On the basis of the analyses of the OE results, a combination of relatively high power, low scanning velocity and moderate powder feeding rate is beneficial to both deposited productivity and epitaxial SX growth during powder-feeding LAM, and allows the preparation of good multilayer SX deposits with fine dendrites.

Original languageEnglish
Pages (from-to)174-181
Number of pages8
JournalJournal of Alloys and Compounds
Volume697
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Epitaxial growth
  • Laser processing
  • Metals and alloys
  • Microstructure
  • Orthogonal experiment method

Fingerprint

Dive into the research topics of 'Experimental optimization of laser additive manufacturing process of single-crystal nickel-base superalloys by a statistical experiment design method'. Together they form a unique fingerprint.

Cite this