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Artificial Intelligence for Damage Detection in Automotive Composite Parts: A Use Case

Journal Article
ISSN: 2641-9645, e-ISSN: 2641-9645
Published April 06, 2021 by SAE International in United States
Artificial Intelligence for Damage Detection in Automotive Composite Parts: A Use Case
Citation: Ciampaglia, A., Mastropietro, A., De Gregorio, A., Vaccarino, F. et al., "Artificial Intelligence for Damage Detection in Automotive Composite Parts: A Use Case," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(6):2936-2945, 2021,
Language: English


The detection and evaluation of damage in composite materials components is one of the main concerns for automotive engineers. It is acknowledged that defects appeared in the manufacturing stage or due to the impact and/or fatigue loads can develop along the vehicle riding. To avoid an unexpected failure of structural components, engineers ask for cheap methodologies assessing the health state of composite parts by means of continuous monitoring. Non Destructive Technique (NDT) for the damage assessment of composite structures are nowadays common and accurate, but an on-line monitoring requires properties as low cost, small size and low power that do not belong to common NDT. The presence of a damage in composite materials, either due to fatigue cycling or low-energy impact, leads to progressive degradation of elastic moduli and strengths. Since there is a well-known relationship between the elastic modulus reduction and the amount of damage, the stiffness degradation can be used for the scope of detecting the position and the amount of damage that has taken place. Relying on these concepts, a novel strain-based damage sensing procedure is here proposed, that can identify damages in composite structures by processing strain measures from a distributed sensors network. To achieve this result a combined Machine Learning pipeline, composed by Principal Component Analysis (PCA) and One Class Support Vector Machine (OC-SVM) is proposed. First, PCA learns a linear transformation on the undamaged measurements to reduce the data dimensionality; secondly, OC-SVM trained to detect anomalies in the projected components. A cross-validation procedure is used to find the optimal pipeline configuration. The methodology is virtually tested on a carbon fiber suspension. The results suggest dropping the first components of the PCA to feed the classifier. In addition, results show the capability of the algorithm to detect anomalies in the component strain response.