Using Machine Learning for Aerostructure Surface Damage Digital Reconstruction

Yijia Wu, Hon Ping Tang, Anthony Mannion, Robert Voyle, Ying Xin

Aerostructure surface damage inspection is carried out over the whole life-cycle using legacy processes and recording during maintenance. The inspection techniques record the detailed history of the damage and repair. However it remains elusive to predict the location of future damage on the aerostructure surface. In this paper, we work up a novel simulation technique based on the results of machine learning analysis for prediction of the reference location of future aerostructure surface damage. First, we use the support vector machine (SVM) and k-nearest neighbor (KNN) to analyze the damage on three B777-200 aircraft and find that the classification accuracy can range from 75.1% to 86%. Then, we use the prediction result of a feedforward neural network (FNN) to simulate the damage structure and the mapping relationship to explore its reconstructive possibility. We show that the aerostructure surface damage can be reconstructed by machine learning. Moreover, the aerostructure surface damage heat map obtained by reconstruction can be prepared for further image recognition analysis in the future.