Context and Motivation
Situation
- 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
Challenge
- 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
Consequence
Composites products are
- Expensive
- Labourious
- Inefficient to manufacture
NEED
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
What?
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)
How?
- Through minimal and accelerated testing of generic specimens
- Transferring the results of the experiments to large-scale structures using artificial intelligence and machine learning
Effect?
Enabling reduced time-to-market, material waste, and increased lifespan of composite products in the aerospace and wind energy industries
