Through-Hole-Fastener Install Fatigue Stress Factors

Abstract

Fasteners, while critical components for mechanical performance, introduce galvanic couples at high stress locations in naval aircraft. The latter can increase fatigue susceptibility and compromise coating systems thereby serving as access points for environmental chemistries that exacerbate fatigue in combination with galvanic corrosion. Preventative measures or #best practices# during install of through-hole-fasteners are numerous and varied. These practical install conditions influence galvanic corrosion damage to aircraft frames and components as evidenced by frequent association with cracks and other forms of structural degradation. The various coating options and fastener install variables introduce a large parameter space that is not understood mechanistically or quantifiedas fatigue life stressors. Thus, there is a need to better quantify the effect of electrochemical stress factors on fatigue of through-hole-fastener install conditions. This work aims to both quantify and understand mechanisms associated with fastener installation and environmental conditions, particularly in the context of galvanic atmospheric corrosion and humidity cycling, under real worldfastener install conditions. These results will be utilized to develop a probabilistic predictive model for environmental and installation effects upon fastener fatigue. Ultimately, this model will be integrated into a larger Bayesian Network based model (OSU N00014-23-S-B001 WPT# 23-000003448) for digital twin predictive capabilities of corrosion fatigue in relevant naval environments. Developing this capability would provide an important tool in the design and maintenance of aircraft.

Document Details

Document Type
DoD Grant Award
Publication Date
May 15, 2024
Source ID
N000142412333

Entities

People

  • Brendy Rincon Troconis

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Texas at San Antonio

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Materials Science and Engineering.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space