Feature Concatenation based Multilayered Sparse Tensor for Debond Detection Optical Thermography

Junaid Ahmed,Abdul Baseer,Ahmed Ali Shah,Gulsher Baloch,Guiyun Tian

doi:10.14569/ijacsa.2022.0130134

Abstract

Composites being the key ingredients of the manufacturing in the aerospace, aircraft, civil and related industries, it is quite important to check its quality and health during its manufacture or in service. The most commonly found problem in the CFRPs is debonding. As debonds are subsurface defects, the general methods are not quite effective and require destructive tests. The Optical Pulse Thermography (OPT) is a quite promising technology that is being used for detecting the debonds. However, the thermographic time sequences from the OPT system have a lot of noise and normally the defects information is not clear. For solving this problem, an improved tensor nuclear norm (I-TNN) decomposition is proposed in the concatenated feature space with multilayer tensor decomposition. The proposed algorithm utilizes the frontal slice of the tensor to define the TNN and the core singular matrix is further decomposed to utilize the information in the third mode of the tensor. The concatenation helps embed the low-rank and sparse data jointly for weak defect extraction. To show the efficacy and robustness of the algorithm experiments are conducted and comparisons are presented with other algorithms.

Highlights

For the task of extracting weak defect information in the thermal sequences of carbon fiber reinforced polymer (CFRP) debonds using the optical pulse thermography (OPT) based technology, post-image processing techniques are generally used
To extract the defect information we propose the following optimization problem [11], [12]; minL,C‖Ln‖∗ + ∂‖Cn‖1 s. t Xn = Ln + Cn where ‖. ‖∗ represents the tensor nuclear norm, ∂ is the regularizing parameter and ‖. ‖1 is the l1 norm
The algorithms used in competition with the proposed algorithm are pulse phase thermography (PPT) [4], thermal signal reconstruction (TSR) [3], sparse principal component thermography (SPCT) [6], ensemble variational Bayes tensor factorization (EVBTF) [9], and sparse mixture of Gaussian (S-MoG) [10]

Summary

Introduction

For the task of extracting weak defect information in the thermal sequences of carbon fiber reinforced polymer (CFRP) debonds using the optical pulse thermography (OPT) based technology, post-image processing techniques are generally used. The algorithm provides reasonable results for detecting the debond defects in the CFRPs. In [2], another decomposition-based algorithm is proposed called the independent component analysis(ICA). The algorithm is similar to the PCA and provides reasonable results for detecting the debonds in CFRPs. In [3], the authors propose a polynomial-based decomposition algorithm called the thermal signal reconstruction (TSR). In [6], [7], sparse principal component thermography (SPCT) is proposed for debonding detection in composites This algorithm is an extension of the PCA algorithm. The specimen under test was the flat rectangular shape CFRPs with debond defects at multiple depths and with multiple diameters Another multilayer decomposition approach is proposed in [10] called a sparse mixture of Gaussian (S-MoG) for debond detection in composites. This algorithm is tested for the irregular shape CFRP V-shaped having debond defects at the elbow location

Methods

Results

Conclusion