Papers by William Trehern
Scripta Materialia, 2019
This paper presents martensitic transformation characteristics of selected multi-component (Ni,Pd... more This paper presents martensitic transformation characteristics of selected multi-component (Ni,Pd) 50 (Ti, Hf,Zr) 50 alloys, with an emphasis on superelasticity and thermal actuation behavior. We report, for the first time, austenite finish temperatures beyond 700°C in NiTi-based high temperature shape memory alloys without the presence of platinum and gold. The increase in transformation temperatures, and transformation stress and recovered strains at elevated temperatures are attributed to the high configurational entropy of the studied alloys. Based on the current findings, we introduce multi-component ultra-high temperature shape memory alloys, which are expected to pioneer a completely new field of study and applications for shape memory alloys.
Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable prediction... more Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of interest. We focus on materials science applications, where interpretable predictive modeling may provide principled understanding of materials systems and guide new materials discovery. It is often computationally prohibitive to exhaust all the potential relationships to construct and search the whole feature space to identify interpretable and predictive features. We develop and evaluate new AFE strategies by exploring a feature generation tree (FGT) with deep Qnetwork (DQN) for scalable and efficient exploration policies. The developed DQN-based AFE strategies are benchmarked with the existing AFE methods on several materials
Additive manufacturing, 2021
Additive manufacturing (AM) has gained considerable academic and industrial interest due to its a... more Additive manufacturing (AM) has gained considerable academic and industrial interest due to its ability to produce parts with complex geometries with the potential for local microstructural control. However, due to the large number of material and process variables associated with AM, optimization of alloying compositions and process parameters to achieve desired properties is an arduous task. There is a fundamental gap in understanding how changes in process variables, and alloy composition and thermodynamics affect additively manufactured parts. The present systematic study sheds light on the effects of alloying composition and corresponding phase diagram features on the printability and solidification microstructures of four binary nickel-based alloys, namely, Ni-20at.% Cu, Ni-5at.% Al, Ni-5at.% Zr, and Ni-8.8 at.% Zr. These compositions are selected to represent binary isomorphous, weak solute partitioning, strong solute partitioning, and eutectic alloying conditions, respective...
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Papers by William Trehern