About this Abstract |
Meeting |
MS&T21: Materials Science & Technology
|
Symposium
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Additive Manufacturing: Processing, Microstructure and Material Properties of Titanium-based Materials
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Presentation Title |
A Machine Learning Model to Predict Tensile Properties of Ti6Al4V Parts Prepared by Selective Laser Melting with Hot Isostatic Pressing |
Author(s) |
Zhaotong Yang, Mei Yang, Richard D. Sisson, Yanhua Li, Jianyu Liang |
On-Site Speaker (Planned) |
Zhaotong Yang |
Abstract Scope |
A machine learning model is established to predict the influence of Hot isostatic pressing (HIP) parameters on the tensile properties of Ti-6Al-4V parts prepared by selective laser melting (SLM). The database was established by collecting published reports on HIP treatment of SLM Ti6Al4V from 2010 to 2019. Using the established model, it is possible to prescribe HIP parameters and predict properties after HIP for SLM Ti-6Al-4V parts with high confidence. It is found that the YS and UTS are sensitive to the HIP parameters including temperature and holding time. The initial YS and UTS of as-printed parts are weaker influencing factors for YS and UTS of final parts. The model suggests that a HIP process with holding temperature lower than 970°C and time around 2 hours is desirable for SLMed Ti-6Al-4V. However, the prediction of strain to failure shows lower accuracy because of the variation of defects among different samples. |