Engineering Commons^™

Deep Learning-Guided Prediction Of Material’S Microstructures And Applications To Advanced Manufacturing, Jianan Tang

All Dissertations

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss.

In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) …

Effect Of Crystal Quality On Twinning Stress In Ni–Mn–Ga Magnetic Shape Memory Alloys, Andrew Armstrong, Peter Müllner

Materials Science and Engineering Faculty Publications and Presentations

Low twinning stress (TS) is a prerequisite for magnetic shape memory functionality in ferromagnetic martensites. We compare Ni₅₀Mn₂₈Ga₂₂ (nominal at.%) single crystals from four different producers to reveal the effect of crystal quality on the TS. Near the reverse martensite transformation, the TS is generally low, about 1 MPa, regardless of mosaicity of up to 1.7° and chemical composition deviations of up to 2 at.% of Mn. Pure type I and type II twin boundaries occur in crystals with smooth chemical composition gradients. The corresponding temperature dependences of TS follow universal linear trends with the …

Microstructure Evolution And Mechanical Property Response Of Tc11 Titanium Alloy Under Electroshock Treatment, Chang Liu, Lechun Xie, Dongsheng Qian, Lin Hua, Liqiang Wang, Lai-Chang Zhang

Research outputs 2014 to 2021

© 2020 The Authors This work investigated the effects of electroshock treatment (EST) on the microstructure variation and mechanical properties of TC11 alloy. The average hardness of the specimens decreased from 358 HV to 328 HV after EST of 0.04 s, then increased to 396 HV after EST of 0.06 s. After EST, the yield strength of specimen declined from 959 MPa to 797 MPa after EST of 0.04 s, and then increased to 1265 MPa after EST of 0.06 s, but the fracture strain decreased continuously. The variation in mechanical properties was closely related to the phase transition from …