Machine Learning for Thermal Transport Analysis of Aluminum Alloys with Precipitate Morphology

Abstract

A large number of microstructural parameters and a wide range of transport physics impose challenges on thermal transport analysis of alloy. Herein, modern data science techniques are employed to overcome the challenges, pursuing effective calculation of thermal transport properties. This emerging approach is tested for precipitate-hardened aluminum (Al) alloy with consideration of precipitate morphology. The finite element method (FEM) is employed to create a database of effective thermal conductivity of hypothetical Al alloys with varying precipitate morphological and thermal transport features. Using the FEM-generated data sets, the correlation analysis is conducted to qualitatively evaluate the importance of various precipitate features. The correlation analysis identifies the surface area, average diameter, and volume fraction of precipitates as the most descriptive features for determining the thermal conductivity of alloys. Afterward machine learning (ML) models are trained to accurately predict the effective thermal conductivity. Comparing the ML predictions with effective thermal conductivity and microstructural information from experiments, precipitate thermal transport properties can be calculated, such as interfacial conductance between Al matrix and precipitate, without atomistic simulations. This research demonstrates the feasibility of data-driven approaches for effective thermal transport calculation and the promise of the FEM-generated data analysis for more comprehensive evaluation of metallic alloys.