Machine Learning Based Stress Prediction Around the Hole of Attachment Lug Structures for Flaw Tolerance Evaluations

This study investigates the stress concentration and damage tolerance of lug structures, with an application example using the horizontal tail plane lug of a light utility helicopter. Using Finite Element Analysis (FEA), stress distributions around the lug hole were simulated under varying load conditions to understand how different loading angles and magnitudes affect stress concentrations. A machine learning approach was employed to predict stress distributions based on a dataset generated from FEA simulations. Several regression models were tested and Random Forest Regression model yields the best predictive accuracy among the others. The study also incorporates a flaw tolerance analysis using the NASGRO® software to calculate the crack growth characteristics of the horizontal tail plane lug structure under service loading. The results highlight the importance of stress distribution variability and identifying the most critical point of the lug structure under service loading with changing angle and amplitude. This research provides insights into the structural integrity of lug components under dynamic loading, contributing to more reliable flaw tolerance assessments for aerospace applications using machine learning techniques.