en.Wedoany.com Reported - The Moscow Aviation Institute (MAI) has developed an automated diagnostic method for aerospace composite materials, capable of identifying internal microdefects by integrating volumetric imaging data.

Designed for non-destructive evaluation workflows based on X-ray computed tomography and high-resolution scanning technologies, this method can detect internal structural anomalies such as microcracks, pores, and delamination zones, which are difficult to distinguish using traditional inspection methods. Compared to conventional manual analysis, the system significantly reduces inspection cycle times. Large imaging datasets that previously required operator-led reviews lasting several days can now be processed within minutes.

In terms of performance characteristics, the MAI method differs from traditional inspection workflows in three key parameters. The system's sensitivity extends to sub-micrometer defect scales, addressing a critical limitation of operator-dependent inspection methods, where detection capability is constrained by visual resolution and fatigue.

This method is positioned for quality assurance and structural health monitoring applications in composite fuselage components. In these applications, minor manufacturing defects can serve as precursors to fatigue propagation during operational lifespan. With the widespread use of composites in modern fuselage structures, there is a growing demand for more consistent, automated, and repeatable inspection frameworks.

MAI describes this development as part of a broader shift toward digitally integrated non-destructive evaluation, combining automated pattern recognition with fused imaging datasets to reduce operator variability and improve classification consistency.

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