TY - JOUR
T1 - Multi-principal element alloy discovery using directed energy deposition and machine learning
AU - Nelaturu, Phalgun
AU - Hattrick-Simpers, Jason R.
AU - Moorehead, Michael
AU - Jambur, Vrishank
AU - Szlufarska, Izabela
AU - Couet, Adrien
AU - Thoma, Dan J.
N1 - Funding Information:
This work was supported by the Advanced Research Projects Agency-Energy (ARPA-E) program (ARPA-E DE-AAR0001050). The authors would like to thank Michael Niezgoda, graduate student in DJT's group at UW-Madison, for writing the g-code used to fabricate the DED samples in an automated fashion. The authors are also grateful to Prof. John H. Perepezko for access to his laboratory and the use of the high-temperature vacuum furnace. IS, VJ, and DJT gratefully acknowledge support from the US National Science Foundation through Designing Materials to Revolutionize and Engineer our Future (DMREF) award number 1728933.
Funding Information:
This work was supported by the Advanced Research Projects Agency-Energy ( ARPA-E ) program ( ARPA-E DE-AAR0001050 ). The authors would like to thank Michael Niezgoda, graduate student in DJT's group at UW-Madison, for writing the g-code used to fabricate the DED samples in an automated fashion. The authors are also grateful to Prof. John H. Perepezko for access to his laboratory and the use of the high-temperature vacuum furnace. IS, VJ, and DJT gratefully acknowledge support from the US National Science Foundation through Designing Materials to Revolutionize and Engineer our Future ( DMREF ) award number 1728933 .
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/1
Y1 - 2024/1
N2 - Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within ±5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys.
AB - Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within ±5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys.
KW - Alloy development
KW - Directed energy deposition
KW - High entropy alloys (HEAs)
KW - High-throughput
KW - Machine learning
KW - Multi-principal element alloys (MPEAs)
UR - http://www.scopus.com/inward/record.url?scp=85178140214&partnerID=8YFLogxK
U2 - 10.1016/j.msea.2023.145945
DO - 10.1016/j.msea.2023.145945
M3 - Article
AN - SCOPUS:85178140214
SN - 0921-5093
VL - 891
JO - Materials Science and Engineering: A
JF - Materials Science and Engineering: A
M1 - 145945
ER -