|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing: Nano/Micro-mechanics and Length-scale Phenomena
||Multi-scale Dynamic Strain Measurement and Machine Learning Optimization to Uncover Solidification Dynamics of Ti-5553 L-PBF Melt Pools
||Caleb Andrews, Maria Strantza, Nicholas P Calta, Tae Wook Heo, Saad A Khairallah, Rongpei Shi, Manyalibo J Matthews, Mitra L. Taheri
|On-Site Speaker (Planned)
Laser powder bed additive manufacturing (L-PBF AM) is a technology which enables digital near-net fabrication of complex parts. However, the rapid solidification inherent to the L-PBF process generates residual strains across scales. Understanding how strain spans the micro-millimeter scale as a function of laser processing parameters can elucidate how to form strain free parts by optimizing the L-PBF process, or, create strain anisotropy at the microscale to tune mechanical properties. We demonstrate a high-resolution electron back scattering diffraction (HR-EBSD) framework to measure the absolute strain of Ti-5553 meltpools across their entire length scale with sub-micron resolution. Monte Carlo diffraction simulation is utilized to create a zero-strain point of reference, from which collected Kikuchi patterns can be correlated against, while a deep learning denoising autoencoder is employed to reduce noise from experimental data. This demonstrates a method to obtain the absolute strain tensor at the sub-grain scale without utilizing beamline techniques.
||Additive Manufacturing, Machine Learning, Mechanical Properties