Characterization of Armor Plate Proof Velocity Via Bayesian Inference

Abstract

Ballistic testing is necessary in all cases where armor is used to protect human life. Despite The great increase in computational capabilities over the past few decades, the dynamic impact and penetration mechanics involved in ballistic events are challenging to accurately model, whether it is for kinetic energy (KE) threats (e.g. small arms) [1, 2, 3, 4] or chemical energy (CE) threats (e.g. shaped charge jets) [5, 6]. As a result, although numerical tools may assist in the development of armor solutions, eventually the armor design must be tested against the actual threat of interest or a representative surrogate.

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Document Details

Document Type
Technical Report
Publication Date
Nov 28, 2022
Accession Number
AD1188302

Entities

People

  • James M. Gorman

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Ammunition Testing
  • Armor Plate
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Sets
  • Design Criteria
  • Engineering
  • Failure Mode And Effect Analysis
  • Machine Learning
  • Mechanics
  • Normal Distribution
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Inference
  • Test Methods

Readers

  • Distributed Systems and Data Platform Development
  • Metallurgy
  • Systems Analysis and Design

Technology Areas

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