2025-04-09 09:00:00
Current methods used to characterize high strain rate particle impacts relevant to cold spray processes are limited in resolving deformation characteristics and observing viscoelastic behavior of particles during deposition. This leaves critical gaps in the understanding of particle-substrate interactions and adhesion mechanisms. The primary…
Instrumented indentation impact testing offers a fast and easy way to measure the mechanical properties of materials at high strain rates while using a minimal amount of material. In this presentation, the challenges and some solutions of measuring indentation load-depth curves from impact tests…
Segmented elastomers such as polyurethane-urea (PUU) have attracted considerable research interest in the last decade due to their excellent high strain rate properties. Given the sheer number and structural diversity of potential molecules that can constitute the hard and soft segments in PUUs, a…
Mechanical metamaterials with rotating squares have attracted considerable attention due to their adjustable shape control, strength, and energy absorption characteristics. Prior research suggests that the load-bearing and energy absorption capacities of these structures can be tailored by varying the stiffness and/or thickness of the…
We developed an image-based convolutional neural network (CNN) designed for quantitative, time-resolved measurement of fragmentation behavior in opaque brittle materials using ultra-high-speed optical imaging. Building on prior work with the U-net model, we trained binary, 3-class, and 5-class models via supervised learning, using data…
The high computational cost of finite element analysis (FEA) often limits its application to heterogeneous and complex materials, where intricate behaviors and interactions lead to time-extensive and computationally intensive simulations. As a result, data-driven approaches based on deep learning (DL) have emerged as promising,…
Recent advances in scientific programming, particularly with regards to topology optimization and machine learning, have necessitated computational methods that generate gradients for optimization. One method to do so, is to utilize a tool called automatic differentiation, a mechanism to algorithmically calculate derivatives of functions…
Engineering material microstructure is the key to developing advanced materials with enhanced mechanical properties required for advanced applications. At the same time, conventional mechanics modeling finds it challenging in the context of high-throughput material design, wherein it needs to predict the microstructure effects on…
Designing multiscale structures entails several challenging tasks, including generating random material microstructures, developing microstructure-resolved physics models, and performing multiscale design and optimization. Conventionally, multiscale finite element (FE) methods are implemented to perform microstructure-resolved physics simulations. However, these slow and computationally expensive numerical methods often…
Neural operators have recently emerged as a powerful machine-learning framework for learning infinite-dimensional mappings in physics-based systems described using partial differential equations. However, their application to realistic high-dimensional problems often encounters computational challenges. Existing approaches either down-sample the input-output spaces, sacrificing accuracy by overlooking…
