PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022

PROJECT TITLE: INnovative damage MOnitoring Of Self-HEAling Composites by acoustic emissions in civil and aerospace Applications – IN MOOSHEAC

CUP CODE:  D53D23003600006

Budget:  € 137.865

P.I.  BARILE Claudia

Other Research Units Polytechnic University of Turin

Brief project description
The project aimed to study and test the repairability, the recovery of mechanical performance, and the structural stability of self-healing (SH) composites. The experimental campaign included both destructive and non-destructive tests on specimens and subcomponents made of purpose-designed SH composite materials, differentiated by resin content, fibre architecture, and stacking configurations. The destructive tests were intended to comprehensively assess the recovery of mechanical properties by the SH composite system following surface, subsurface, and interlaminar failures. 

The non-destructive tests, on the other hand, were carried out for two main reasons: to monitor in situ the progression of damage in the SH composite system using acoustic emission (AE), digital image correlation (DIC), and terahertz time-domain spectroscopy (THz-TDS); and to analyze the repair process and the recovery of mechanical integrity by conducting active-mode AE tests on both laboratory-scale specimens and large-scale components. 

Purpose
The design objective was to identify a structured approach based on the combination of multiple experimental techniques for the mechanical analysis of composite materials, capable of accurately and precisely describing both the mechanical behaviour of the material and the initiation, propagation, and repair of defects and failures during service. 

This methodology also made it possible to identify an optimal SH composite configuration capable of extending the service life of structural materials, with clear sustainability benefits, given the environmental impact of plastic matrices..

Expected results
The destructive testing campaign, based on recognised standards, aimed to quantify the level of repair and recovery of the mechanical properties of these composites through the activation of the self-healing process.

To support this investigation, analytical approaches for processing parametric data and AE waveform data based on Machine Learning (ML) were used, with the aim of automating the process of describing the mechanical behaviour and the extent and type of material failure..

Achieved results 
An innovative damage monitoring system was developed using an AE technique based on waveform-related parameters and supported by machine learning. 

In the context of aerospace applications, the AE-based damage monitoring system was also tested on specimens exposed to different levels of ultraviolet (UV) radiation. The results of these experimental investigations showed that these SH composites can potentially be used in structural applications thanks to their good self-healing capability, thereby increasing their service life.