Developing machine learning solutions for materials science will allow the faster production of better materials used everywhere.
Jay Oswald is an associate professor in the School for Engineering of Matter, Transport and Energy in the Ira A. Fulton School of Engineering at Arizona State University.
Total projects: 10
Discovering the optimal cooling rate for silica glass to absorb energy under high-pressure shock will protect the warfighters of tomorrow.
Understanding a material's ability to deform plastically is fundamental in creating secure structures.
Studying the fracture speed in a glassy polymer as a function of electric resistance and temperature can improve a variety of products.
Mapping the behavior of glassy material fractures allows us to build materials and mechanisms over time that are sustainable, safe and efficient.
Analyzing the Effect of Contact Resistance on the Temperature Field Evolution During Resistance Spot Welding
Investigating the temperature field evolution during resistance spot welding can improve the durability of electric car batteries.
Examining properties like temperature resistance, strength and toughness in plastics will increase the potential for developing stronger materials.
Studying the effect of materials and design on alignment of small satellite thrusters will help improve mission success and accessibility.