Controlling the Load Distribution in High-Strength Materials Army Science Planning and Strategy Meeting (ASPSM)

Abstract

Future Army capabilities require understanding and controlling the physics associated with material fracture and failure from applied loads, a mechanical behavior relevant to many problems within Army systems, ranging from bolted joints to armor, and one that impacts Soldier lethality and survivability. To address this, the US Army Combat Capabilities Development Command Army Research Laboratory held an Army Science Planning and Strategy Meeting (ASPSM) on Controlling the Load Distribution in High-Strength Materials in December 2020. Participants presented and discussed material concepts for redistributing localized loading and delay or preventing failure due to impact in granular media, lattice structures, and emerging composite architectures. Leveraging the researchs inherently interdisciplinary nature (material science, experimental and computational mechanics, machine learning, and computer science), participants identified a need for developing numerical techniques to model the constitutive response of these materials, including mechanical behavior under large deformations, fracture, and failure. Machine learning algorithms can offer some advantages in speed and accuracy by integrating into the data-driven design strategy, but their application needs to be validated by experimental data or physics-based models. A research strategy focused on these principles will produce systems and equipment for the future Soldier with an enhanced capability for mitigating stresses generated from impact or other sources.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2021
Accession Number
AD1123267

Entities

People

  • Andrew Tonge
  • Christopher Hoppel
  • David Stepp
  • Lionel Vargas-gonzalez
  • Mark Tschopp
  • Michael Bakas
  • Richard Becker

Organizations

  • United States Army

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Additive Manufacturing
  • Algorithms
  • Artificial Intelligence
  • Bolted Joints
  • Composite Materials
  • Computational Mechanics
  • Computational Science
  • Computer Science
  • Computers
  • Department Of Defense
  • Engineered Materials
  • Experimental Data
  • Governments
  • Information Operations
  • Learning
  • Load Distribution
  • Machine Learning
  • Machines
  • Manufacturing
  • Materials
  • Materials Laboratories
  • Materials Science
  • Mathematical Models
  • Mechanical Engineering
  • Mechanics
  • Metamaterials
  • Military Research
  • Neural Networks
  • Physics
  • Wave Propagation

Readers

  • Military Science and Technology Research and Modernization.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy