ICER Student Highlights: Zhuowen Zhao

Portrait of Zhuowen Zhao

Understanding Plastic Deformation in Polycrystalline Metallic Materials using Solid Mechanics Simulation and Machine Learning 
 

Zhuowen Zhao is a graduate student pursuing a PhD in the Department of Chemical Engineering and Materials Science (CHEMS) at Michigan State University. His research under the supervision of Dr. Philip Eisenlohr focuses on understanding and predicting the heterogeneous plastic deformation in polycrystalline metallic materials such as tin, titanium, and titanium alloys. Plastic deformation is the process by which materials (particularly metals) permanently change in shape under an applied stress. Since most technologically relevant materials are polycrystalline and contain many (millions) of so-called grains, the actual deformation is not homogeneous but—sometimes strongly—differs among and even within different grains. Zhao’s research centers around understanding microstructure–property relations, specifically dislocations (line defects) that carry the flow of the plastic deformation within the internal microstructure of these materials.

Within this topic, Zhao is working on two major projects: one tries to identify slip resistances (parameters that describe the difficulty to move dislocations) in hexagonal titanium1 and titanium alloys through a combination of digital modeling and real-world experimentation (nano-indentation); while the other seeks to predict slip transfer across grain boundaries with the aid of artificial neural networks2.

These innovative projects diverge from traditional materials science methods that focus on experimental characterizations of material structures and their properties, especially in the field of metallurgy3. Zhao states that “With the continuing advancement of computational power in the past few decades, materials simulation is playing a bigger and bigger role in materials science research today.” He also talks about being grateful for the opportunity to work with these cutting edge methods and to explore new ways of solving engineering problems.

Hence, Zhao uses HPCC services a great deal in his work. These services allow him to process the massive number of calculations required in his simulations within a reasonable timeframe. However, even with the help of high performance computing, Zhao still struggles with the large amount of time it takes to find the best parameters for his models. Without HPCC though, the amount of time would not render this process difficult. It would render it impossible. 

Thankfully, Zhao and the other researchers in his group are on their way to reaching their goals. The alloys they are studying have applications in the automotive industry, electronics, medical devices, and more. Developing lighter alloys will impact the world in a number of ways. Zhao specifically mentions how “auto parts made of lighter alloys with enhanced properties can make the next-generation vehicles faster, safer and more energy efficient.” All in all, the research done by Zhao and others will facilitate changes in manufacturing and engineering, and increase sustainability and efficiency in a variety of industries. 

Zhao was inspired to study materials science and engineering after realizing that he wanted his work to move beyond the abstract and zero in on real world applications. He also wanted to pursue a field heavily based on the practical applications of mathematical principles and to use materials simulations and machine learning as a means of exploring how new technologies can impact products and infrastructures that we engage with every day. On the whole, Zhao’s hard work, passion, and ambition will undeniably facilitate a more efficient and sustainable future. 

 

 

 

 

 

1Hexagonal Titanium: Titanium where the atoms of its crystal structure are closely packed together and the unit cell consists of three layers of atoms. The top and bottom layers contain six atoms at the corners of a hexagon and one atom at the center of each hexagon.
2Artificial Neural Network: A computational network that simulates the learning process of the brain to solve hierarchical problems. 
3Metallurgy: The study of the microstructures, properties, and processings of metals. 

*Zhao’s recent work on classifying grain boundary slip transfer with artificial neural networks is published in Scripta Materialia 185(2020), 71-75.  https://doi.org/10.1016/j.scriptamat.2020.04.029