YIP Structural interlocking through functional grading
Abstract
This research aims to validate the hypothesis that functional grading is a potent tool for fabricating seamlessly integrated interlocking structures. By redistributing localized strains, our goal is to engineer plastic deformation, influence crack initiation and propagation dynamics, and impact fracture characteristics.We will produce functionally graded materials by depositing multiple materials. We will focus on steel alloys as they are extensively used in various naval applications due to their exceptional mechanical properties and corrosion resistance. We will carefully select alloys that demonstrate phase compatibility to prevent the formation ofdeleterious phases. Additionally, we will explore the phase evolution in functionally graded materials synthesized through location-specific reactive printing, which enables the formation of strengthening phases. Since functional grading is the cornerstone of this project, our overarching objective is to introduce a range of heterogeneities in parts to achieve varying levels of strain localization.We will begin our formal investigation by computationally examining phase formations. Subsequently, we will corroborate our computational findings with experimental data. While computational investigations conducted through first-principles modeling offer deterministic results, physical experiments inherently entail uncertainties. Our objective is to intelligently integrate diverse datasets using advanced heterogeneous data fusion methods to precisely characterize and comprehend the phase evolution in complex alloys like steel. Following this, we will emulate the complex structures found in naturally evolved biological systems to create novel interlocks. This will be achieved through the integration of spline-based method with first-principles modeling. The incorporation of graph neural network techniques will be instrumental in generating a sufficient volume of synthetic data, enabling us to accurately uncover the underlying mechanisms responsible for strain localization resulting from location-specific structural interlocks. By leveraging graph neural network models, we can harness the knowledge gained from previous data patterns. This enables us to navigate through a broader spectrum of potential design configurations with greater ease and efficiency, requiring less computational resources.Expanding our exploration to this wide range of possibilities will facilitate identification of the evolution of interlocking structures through the utilization of Bayesian algorithms. These algorithms tend to exhibit superior performance when operating with a diverse and robust initial population. In the final phase of our project, we will validate our computational findings by performing custom experiments that will give us not only bulk properties like elastic and plastic deformation and strain energy absorption beforefracture but also local strains. While nature-inspired engineering, particularly utilizing interlocks, has emerged as an effective and innovative design strategy, it has not yet been explored in conjunction with functional grading. Our research will provide the fundamental knowledge necessary for the creation of intelligent structures capable of dispersing localized strains, reducing stress concentrations, and contributing to a more uniform distribution of mechanical loads. Our findings will offer promising avenues in understanding biological interdigitated features and designing innovative interlocked materials for a range of naval applications suchas structural components for ships and submarines, lightweight armor systems for combat vehicles, and advanced turbine blades for propulsion systems. Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 10, 2025
- Source ID
- N000142512250
Entities
People
- Amrita Basak
Organizations
- Office of Naval Research
- Pennsylvania State University
- United States Navy