A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates

Abstract

Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of debonding reduces the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new supervised learning algorithm to identify debonded regions in aluminum honeycomb panels. The algorithm uses a linear approximation method handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of neural networks. The honeycomb panels are modeled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modeled using linear springs, the rigidities of which are reduced in debonded sectors. The algorithm is validated using experimental data of an aluminum honeycomb panel under different damage scenarios.

Modal Strain Energy-Based Debonding Assessment of Sandwich Panels Using a Linear Approximation with Maximum Entropy

“…The training, setting-up and testing databases were built using a numerical model of the honeycomb panel, which was validated in [22]. The honeycomb panel is modelled with finite elements using a simplified three-layer shell model.…”

“…The principal advantage is that new data can be easily incorporated into the training database with no need to re-train the algorithm as in the case of ANNs. The LME algorithm was first developed for damage assessment [21,22], and Sanchez et al [23] and Meruane et al [24] employed it in impact identification.…”

“…Alternatively, the data sets can be generated using a numerical model of the structure. It has been demonstrated by diverse studies that it is possible to produce correct damage predictions in an experimental structure using supervised learning algorithms trained with data obtained from a numerical model [20][21][22]49]. Here, a finite element model of the panel was used to generate the data to train the damage assessment algorithm.…”

Impact Location and Quantification on an Aluminum Sandwich Panel Using Principal Component Analysis and Linear Approximation with Maximum Entropy

“…The main advantage is that new data can be easily incorporated into the training database with no need for re-training the algorithm as in the case of ANN and SVM. The LME algorithm was originally developed for damage assessment [19,20], and Sanchez et al [21] implemented it in impact identification demonstrating that, in this case, LME provides a better performance than ANNs and SVMs.…”

Linear and Nonlinear Dynamic Analyses of Sandwich Panels with Face Sheet‐to‐Core Debonding

“…For the sake of debonding detection, the linear dynamic FEA has been carried out in [49] to highlight the relative changes of dynamic response between a healthy sandwich plate and a debonded one. More advanced techniques using modal dataset to detect debonding in sandwich structures can be found in some recent works, for example, [50][51][52].…”