A machine-learning model to predict tensile properties of Ti6Al4V parts prepared by laser powder bed fusion with hot isostatic pressing
Author:
Publisher
Elsevier BV
Subject
Materials Chemistry,Mechanics of Materials,General Materials Science
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1. Machine learning-powered analysis of hot isostatic pressing for Ti-6Al-4 V powder;Applied Physics A;2024-08-07
2. Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion;Materials Today Communications;2023-12
3. Understanding the hot isostatic pressing effectiveness of laser powder bed fusion Ti-6Al-4V by in-situ X-ray imaging and diffraction experiments;Scientific Reports;2023-10-27
4. Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process modelling;Progress in Materials Science;2023-09
5. Impact of Post-Process Heat Treatments Performed on Ti6Al4V Titanium Alloy Specimens Obtained Using LPBF Technology;Technologies;2023-07-15
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