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Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.
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Mössbauer analysis of compositional tuning of magnetic exchange interactions in high entropy alloys
We measured the change in the average hyperfine field strength of several high entropy alloys in relation to small compositional deviations from the equiatomic alloy, FeCoNiCuMn. Mössbauer spectra of four psuedo-binary systems, in which Mn content is increased and another element was decreased in equal measure, reveal several discrete peaks in the hyperfine field distribution that show evidence of the discrete exchange interactions between magnetic elements in the alloy. A simple linear regression modelling the perturbation of the average hyperfine field when the composition is altered calculates the contribution of each atom to the overall average. The average hyperfine field is linear with Tc, so these values allow us to estimate Tc for alloys with more complex compositional variation within the window of linearity (<24% Mn based on other alloys). The results were confirmed experimentally by calculating Tc of two new alloys, Fe19Co20Ni19Cu19Mn23 and Fe19Co20Ni19Cu20Mn22.
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- Award ID(s):
- 1709247
- PAR ID:
- 10597456
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- AIP Advances
- Volume:
- 9
- Issue:
- 3
- ISSN:
- 2158-3226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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