Context and Motivation


  • Composite materials crucial for sustainable future
    • Aircraft 50% or more out of FRP
    • Wind energy sector with larger turbines
  • Extreme loads and stress cycles during
  • Damage tolerance needed to extend the lifespan


  • Lack of fundamental knowledge regarding damage growth process under cyclic loading
  • Complex interaction of fibres, fibre orientation, and matrix materials
  • Every new design requires multiple tests at various scales
  • Imperfect solutions: high-safety factors, ‘no growth’ criterion


Composites products are

  • Expensive
  • Labourious
  • Inefficient to manufacture


A better understanding of the fatigue characterization at a meso-scale, so that we can scale up the gained insights to larger structures

D-STANDART intention


To develop fast and efficient methods to characterise the fatigue properties of composite materials, and thereby model the durability of large-scale composite structures with arbitrary lay-ups under realistic conditions (loads, environment, manufacturing/effect of defect)


  • Through minimal and accelerated testing of generic specimens
  • Transferring the results of the experiments to large-scale structures using artificial intelligence and machine learning


Enabling reduced time-to-market, material waste, and increased lifespan of composite products in the aerospace and wind energy industries