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Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization

Published

Author(s)

Jordan Weaver, Adam L. Pintar, Carlos R. Beauchamp, Howard Joress, Kil-Won Moon, Thien Q. Phan

Abstract

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.
Citation
Materials & Design
Volume
209

Keywords

Inconel 625, indentation, election backscatter diffraction, stress-relief, additive manufacturing, qualification

Citation

Weaver, J. , Pintar, A. , Beauchamp, C. , Joress, H. , Moon, K. and Phan, T. (2021), Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization, Materials & Design, [online], https://doi.org/10.1016/j.matdes.2021.109969, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929236 (Accessed May 19, 2022)
Created August 3, 2021, Updated October 14, 2021