Imagine trying to guess how long a complex carbon-fiber part will last before it cracks without actually breaking thousands of them. Instead of slow and expensive physical tests, this project creates a smart digital shortcut using AI. It's like having a high-speed simulator that predicts wear and tear, helping companies build lighter and greener products faster.
Current methods for predicting the lifespan of composite materials are too slow and inaccurate. This leads to over-engineering, higher costs, and slower time-to-market for lightweight structures.
AI surrogate models for fatigue prediction and cradle-to-cradle sustainability assessment tools for composite design.
If you are a manufacturer dealing with slow and inaccurate fatigue testing for wing structures — this project developed AI surrogate models that accelerate the prediction of defect growth. This allows for faster certification and reduced waste during the design phase.
If you are a producer dealing with the high cost of over-engineering blades to ensure durability — this project developed fast simulation methodologies to quantify material parameters. This enables the creation of lighter, more durable blades with a lower environmental footprint.
If you are an SME dealing with the difficulty of assessing the total environmental impact of different material lay-ups — this project developed cradle-to-cradle life-cycle and techno-economic assessment models. This helps you choose the most sustainable design alternative based on data.
By replacing slow, expensive high-fidelity models with AI surrogate models, the project aims to reduce the time and resources needed to find the optimal level of durability during development.
Yes, the project specifically develops AI models designed to be applied within an industrial design environment to accelerate the commercialisation of advanced components.
Based on available project data, the specific licensing terms are not mentioned, but the project involves 8 industry partners and 2 SMEs to support results uptake.
Yes, it includes dedicated life-cycle assessments and cradle-to-cradle characterisations to help developers account for sustainability in their decision-making.
Based on available project data, the project runs from 2023 to 2025, focusing on making models fast and accurate for immediate use in the design process.
The consortium is heavily industry-weighted with a 62% industry ratio, comprising 8 industrial partners and 2 SMEs. This strong commercial presence, combined with 3 universities and 2 research organizations across 7 countries, indicates a high focus on practical application and market uptake rather than pure academic research.
Contact the Royal Netherlands Aerospace Centre (KNLR)
Contact us to connect with the D-STANDART consortium for AI-driven composite modeling.
