The long-term goal of this work is to predict and control the microstructure evolution in metal additive manufacturing processes. In pursuit of this goal, we developed and applied an approach which combines physics-based thermal modeling with data-driven machine learning to predict two important microstructure-related characteristics, namely, the meltpool depth and primary dendritic arm spacing in Nickel Alloy 718 parts made using the laser powder bed fusion (LPBF) process. Microstructure characteristics are critical determinants of functional physical properties, e.g., yield strength and fatigue life. Currently, the microstructure of LPBF parts is optimized through a cumbersome build-and-characterize empirical approach. Rapid and accurate models for predicting microstructure evolution are therefore valuable to reduce process development time and achieve consistent properties. However, owing to their computational complexity, existing physics-based models for predicting the microstructure evolution are limited to a few layers, and are challenging to scale to practical parts. This paper addresses the aforementioned research gap via a novel physics and data integrated modeling approach. The approach consists of two steps. First, a rapid, part-level computational thermal model was used to predict the temperature distribution and cooling rate in the entire part before it was printed. Second, the foregoing physics-based thermal history quantifiers were used as inputs to a simple machine learning model (support vector machine) trained to predict the meltpool depth and primary dendritic arm spacing based on empirical materials characterization data. As an example of its efficacy, when tested on a separate set of samples from a different build, the approach predicted the primary dendritic arm spacing with root mean squared error ≈ 110 nm. This work thus presents an avenue for future physics-based optimization and control of microstructure evolution in LPBF.
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Fatigue-resistant high-performance elastocaloric materials made by additive manufacturing
Elastocaloric cooling, a solid-state cooling technology, exploits the latent heat released and absorbed by stress-induced phase transformations. Hysteresis associated with transformation, however, is detrimental to efficient energy conversion and functional durability. We have created thermodynamically efficient, low-hysteresis elastocaloric cooling materials by means of additive manufacturing of nickel-titanium. The use of a localized molten environment and near-eutectic mixing of elemental powders has led to the formation of nanocomposite microstructures composed of a nickel-rich intermetallic compound interspersed among a binary alloy matrix. The microstructure allowed extremely small hysteresis in quasi-linear stress-strain behaviors—enhancing the materials efficiency by a factor of four to seven—and repeatable elastocaloric performance over 1 million cycles. Implementing additive manufacturing to elastocaloric cooling materials enables distinct microstructure control of high-performance metallic refrigerants with long fatigue life.
more » « less- Award ID(s):
- 1904830
- NSF-PAR ID:
- 10125742
- Publisher / Repository:
- American Association for the Advancement of Science (AAAS)
- Date Published:
- Journal Name:
- Science
- Volume:
- 366
- Issue:
- 6469
- ISSN:
- 0036-8075
- Page Range / eLocation ID:
- p. 1116-1121
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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