A review of computational modeling in powder-based additive manufacturing for metallic part qualification

Jingfu Liu, Behrooz Jalalahmadi, Y. B. Guo, Michael P. Sealy, Nathan Bolander

Research output: Contribution to journalReview articlepeer-review

28 Scopus citations


Purpose: Additive manufacturing (AM) is revolutionizing the manufacturing industry due to several advantages and capabilities, including use of rapid prototyping, fabrication of complex geometries, reduction of product development cycles and minimization of material waste. As metal AM becomes increasingly popular for aerospace and defense original equipment manufacturers (OEMs), a major barrier that remains is rapid qualification of components. Several potential defects (such as porosity, residual stress and microstructural inhomogeneity) occur during layer-by-layer processing. Current methods to qualify AM parts heavily rely on experimental testing, which is economically inefficient and technically insufficient to comprehensively evaluate components. Approaches for high fidelity qualification of AM parts are necessary. Design/methodology/approach: This review summarizes the existing powder-based fusion computational models and their feasibility in AM processes through discrete aspects, including process and microstructure modeling. Findings: Current progresses and challenges in high fidelity modeling of AM processes are presented. Originality/value: Potential opportunities are discussed toward high-level assurance of AM component quality through a comprehensive computational tool.

Original languageEnglish (US)
Pages (from-to)1245-1264
Number of pages20
JournalRapid Prototyping Journal
Issue number8
StatePublished - Nov 14 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


  • Process modeling


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