Aging heat treatment design for Haynes 282 made by wire-feed additive manufacturing using high-throughput experiments and interpretable machine learning.

ABSTRACT

Wire-feed additive manufacturing (WFAM) produces superalloys with complex thermal cycles and unique microstructures, often requiring optimized heat treatments. To address this challenge, we present a hybrid approach that combines high-throughput experiments, precipitation simulation, and machine learning to design effective aging conditions for the WFAM Haynes 282 superalloy. Our results demonstrate that the γ' radius is the critical microstructural feature for strengthening Haynes 282 during post-heat treatment compared with the matrix composition and γ' volume fraction. New aging conditions at 770°C for 50 hours and 730°C for 200 hours were discovered based on the machine learning model and were applied to enhance yield strength, bringing it on par with the wrought counterpart. This approach has significant implications for future AM alloy production, enabling more efficient and effective heat treatment design to achieve desired properties.

Our research tackles suboptimal properties in additively manufactured alloys with conventional heat treatment, using high-throughput experiments, CALPHAD (CALculation of PHAse Diagrams), and interpretable machine learning to effectively optimize heat treatments for WFAM (Wire-Feed Additive Manufacturing) Haynes 282 superalloy.