Defects Quantification Of Additively Manufactured AISI 316L Stainless Steel Parts Via Non-Destructive Analyses: Experiments And Semi-FEM-Analytical-Based Modeling


Laser additive manufacturing techniques, such as laser powder bed fusion (LPBF), enable the fabrication of intricate metal components but are prone to defects such as lack of fusion (LOF) and keyhole (KEH) porosity. This study presents an integrated experimental simulation approach to quantify these defects in LPBF-processed AISI 316L stainless steel parts. Micro-X-ray computed tomography effectively visualized LOF and KEH pores based on sphericity. A coupled finite element-analytical model predicted the thermal history and melt pool dimensions during printing. An analytical model then used this information to estimate LOF and KEH porosity percentages for different process parameters. As the laser scanning speed decreases from 800 to 1400 mm/s, the number of pores, pore size range, and pore volume decrease from 10,000 to 600, 0.25 to 0.18 mm, and 0.007 to 0.0005 mm3, respectively. With the increase in hatch distance from 0.10 to 0.12 mm, the number of pores decreases, pore size range remains 0–0.25 mm, and pore volume decreases from 0.007 to 0.005 mm3. Furthermore, with the increment in laser power from 190 to 240 W, the intensity of pores decreases, pore size range remains nearly constant, and pore volume decreases from 0.007 to 0.005 mm3. Upon comparison, the developed semi-FEM-analytical simulation model presented reliable results. Furthermore, a processing window has been proposed for AISI 316L stainless steel. The proposed integrated experimental and simulation approach provides an understanding of the defect mechanism and process-structure–property correlations in the LPBF process.


Mechanical and Aerospace Engineering


National Science Foundation, Grant CMMI-1625736

Keywords and Phrases

AISI 316L stainless steel; Defects; Laser powder bed fusion; Micro-X-ray computed tomography; Porosity; Semi-FEM-analytical simulation model

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Article - Journal

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© 2024 Elsevier, All rights reserved.

Publication Date

01 Jul 2024