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DigiTwin: 3D Imaging, Analysis & Digital Twinning

New Frontier Technologies
A high-resolution x-ray CT image with micron-scale features (fibre orientation, matrix, voids, defects) is segmented using machine learning algorithms. A 3D mesh is created accounting for the distribution of every material phase. This model is integrated into a digital twin of the as-manufactred product for finite element analysis, enabling highly realistic performance predictions.

Why and Whom

Lack of detailed understanding of the internal structure of composite parts results in uncertainty of performance and over-engineered designs, especially in high-performance applications. DigiTwin leverages modern computer tomography and machine learning technologies to obtain detailed 3D information of the internal structures to enable high-fidelity quality assessment and performance simulation.

Partners

Australian National University - CTLab | Digital Composites Factory

Key Benefits

The value of DigiTwin is its ability to enhance both quality control and performance in industries where composite materials and structures have a critical role, such as aerospace, space, automotive, and clean energy (e.g. hydrogen). The key benefits are enhanced quality assurance, highly accuracte performance prediction, reduced development cycle times and time-to-market.

Awards

Award Winner - Digital AI & Data
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