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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title A Comprehensive Hot Cracking Model Development by Coupling CALPHAD and Machine Learning
Author(s) Andrew Jordan, Qiaofu Zhang
On-Site Speaker (Planned) Andrew Jordan
Abstract Scope Hot cracking is an irreparable failure in Powder Bed Fusion Additive Manufacturing (PBF-AM) alloys that hinders the reliability and performance of the printed component. Existing hot cracking models such as CSC, HCS, and Kou’s model predict hot cracking qualitatively for specific alloys and are based on arbitrary solidification time/fraction of solid ranges that agree with the Λ-curve identified by Clyne and Davies et al. This research aims to develop a comprehensive Hot Cracking Susceptibility Index (HCSI) model to accurately predict hot cracking occurrences, length, and density. This model implements physics-assisted Integrated Computational Materials Engineering (ICME) using CALPHAD (CALculation of PHAse Diagram) based Scheil-Gulliver solidification simulations in tandem with machine learning (ML) algorithms. This work developed an interpretable ML model that predicts hot cracking for various PBF-AM alloys. Experimental validation of the HCSI model was conducted by the printing and characterization of two designed alloys of differing compositions.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, ICME, Machine Learning

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Crystal Plasticity–Informed Surrogate Modeling for Predictive Laser Powder Bed Fusion Simulation of Ti-6Al-4V
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