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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Machine Learning
Presentation Title G-69: Gaussian Process Regression Modelling and Texture Control During Hot Deformation of Additively Manufactured Maraging Steels
Author(s) Jubert Pasco, Clodualdo Aranas Jr., Thomas McCarthy
On-Site Speaker (Planned) Jubert Pasco
Abstract Scope This work systematically investigates the grain topology evolution and hot deformation response of additively manufactured 18Ni(300) maraging steels at a temperature range of 850 °C to 1050 °C and strain rate range of 0.01 s−1 to 1 s−1. The flow stress values under experimental deformation conditions were predicted using a Gaussian process regression (GPR) model with maximum likelihood parameter estimation approach and compared with an Artificial neural network (ANN) model with a 3-6-6-1 architecture. Results show that columnar prior austenite grains exhibited localized flow deformation behavior and preferential nucleation of recrystallized laths aligned along <111> // BD. The superior performance of the GPR model compared to the ANN model during cross-validation can be attributed to its effective balance in penalizing both model fit and complexity. A net-shape fabrication technique combined with a high temperature forming route to develop dense structures with controlled local texture is proposed.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, Iron and Steel

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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G-59: A Deep Learning Framework for Predicting Surface Deformation of Alloys Under Uniaxial Tensile Loading at Microscopic Length Scale
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G-70: Integrating CAFE with MOOSE for Microstructure Evolution Analysis in 316L Stainless Steel 3D Printing Process
G-71: Large Language Models for Distilling Knowledge in Additive Manufacturing
G-72: LLM Agents for 3D Printing Error Detection and Correction
G-73: Local Stress Analysis of Ti5553 Lattice Structures Under Mixed Mode Stresses
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G-75: Mathematical Study of Partial Blockage of SEN in Specific Zones on Flow Patterns in the Mold
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G-77: Microstructural Investigation and Numerical Analysis and Observation of Additively Manufactured Anti-Tetra-Chiral 316L Stainless Steel Samples
G-78: Microstructure Prediction in Laser Powder Bed Fusion via Physics-Based Modeling and In-Situ Sensor Data Fusion
G-79: Multi-Objective Study on Optimization of WAAM Parameters for Optimal Material Properties
G-80: Multi-Phase-Field Modeling and High-Performance Simulations for Grain Structures Depending on Scanning Strategy During PBF Additive Manufacturing
G-81:Quantifying the Characteristics of Pore Features Using Gaussian Process Machine Learning in LPBF Process Parameter Space
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Micromechanical Modeling Exploration of Microstructure-Properties of Additively Manufactured Pure Tantalum
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