DAMAGE DETECTION OF PMI FOAM SANDWICH STRUCTURE BASED ON ENHANCED INNER PRODUCT MATRIX

Polymethacrylimide (PMI) foam sandwich composite structures are widely used in the aerospace field due to their unique mechanical properties. How to detect the debonding damage of the panel and the core material quickly and accurately at low cost is of great significance to the safety of the structures. However, the traditional ultrasonic-based non-destructive testing techniques cannot effectively detect the debonding damage of such structures due to the sound-absorbing properties of PMI foam. This paper explores the feasibility and effectiveness of a method based on vibration response testing in the damage detection of PMI foam sandwich structures. According the structural damage characteristic inner product matrix (IPM) established by the correlation analysis of vibration time domain responses, the concept of enhanced inner product matrix (EIPM) is proposed by stacking the IPM calculated under different excitation points. A structural damage detection method based on EIPM and Convolutional Neural Network (CNN) is established, in which the EIPM is used as the input of the CNN and the damage state of the PMI foam sandwich structure is used as the output of the CNN. The simulation example and experimental validation results of debonding damage detection of the PMI foam sandwich cantilever beam show that the average identification accuracy of the proposed method is more than 99% when 3 or more measurement points are utilized. Compared with the IPM-based method, the EIPM method has better convergence speed and stability.