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Meeting MS&T22: Materials Science & Technology
Symposium Inference-based Approaches for Material Discovery and Property Optimisation
Presentation Title Alloy-agnostic Criteria for Solidification Cracking Susceptibility Evaluation
Author(s) Rafael Giorjao, Eric Brizes, Antonio Ramirez
On-Site Speaker (Planned) Rafael Giorjao
Abstract Scope Predicting the occurrence of solidification cracking during the solidification of metallic alloys by numerical simulation is a crucial move for avoiding such defects. Several models are widely available, however, the application of such are impacted due to the specific and not accessible parameters required. A simple, composition-based approach to rank solidification cracking susceptibility is presented. The procedure links computational thermodynamic and fluid dynamics to provide an evaluation tool for solidification cracking. The method is related to the liquid filling phenomena in dendritic arms during solidification, which plays a critical role in solidification cracking phenomena. The dendritic profiles were constructed using the fraction of solid calculated by commercial thermodynamic software packages. The method capability to rank the solidification cracking propensity of similar alloys based on composition provides an important new operative tool to aid alloy development in welding and additive manufacturing related areas.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A General Solid Solution Strengthening Model in Multicomponent Alloys
Alloy-agnostic Criteria for Solidification Cracking Susceptibility Evaluation
Comparing High-dose Simulated Irradiation in Tungsten to Experiments
Exploring the Evolution of Irradiation-induced Defects Through Their Energetic Signatures
High Throughput CALPHAD-based Thermodynamic and Kinetic Evaluation of Stainless-steel Solidification
Multi-technique Characterisation of Ion-irradiation Effects on High-pressure-Torsion (HPT) Processed EUROFER-97
Probing the Local Charge Density and Phonon Dynamics by Electron Microscopy
Uncover Hidden Materials Properties with the Lens of Machine Learning
Using Local Thermal Transport Property to Characterize Microstructure of Materials from Additive and Advanced Manufacturing Technologies

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