Additive Manufacturing Benchmarks 2022 (AM-Bench-2022)

Bethesda, Maryland| August 15 – 18, 2022

Additive Manufacturing Benchmarks (AM-Bench) provide a continuing series of controlled benchmark measurements, in conjunction with a conference series, with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data. All AM-Bench data are permanently archived for public use. The AM-Bench conference series provides a venue for sharing the results of these tests and convening modelers and experimentalists to discuss successes and challenges.

AM-Bench 2022 conference is the second event in the conference series, which is planned on a three-year cycle.

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Predicting Material Behavior with Improved Solidification Models for the AM Process Window

Authors: Adam Hope, Kaisheng Wu, Jan Julin, Johan Jeppsson, Paul Mason
Date: 15 August 2022
Time: 1:30 PM – 2:00 PM
Session: Materials I: Phase Evolution
Location: Hyatt Regency Bethesda – Regency Ballroom III & IV

Abstract
Predicting localized material properties in additive manufacturing relies on linking chemistry and processing conditions to microstructure in ICME frameworks. In earlier work, using CALPHAD thermodynamics and the Scheil Gulliver equation to generate composition and temperature dependent data for latent heat and heat capacity has been shown to improve the accuracy of finite element simulations to predict the size, shape, and temperature of the laser melt pool. However, rapid solidification, typical of AM processes, can lead to solute trapping, where solute may be incorporated into the solid phase at a concentration significantly different from that predicted by equilibrium thermodynamics. To account for this effect, solute trapping models by Aziz and Kaplan have been incorporated into the CALPHAD/Scheil methodology. A case study is presented to show how this can lead to better microstructure prediction in Alloy 718 and the effect of solute trapping on thermophysical properties to improve finite element modeling.

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