Optimizing Aircraft Assembly: A Machine Learning Approach for Screw Fastener Outcome Prediction

Screw fastening is one of the most important manufacturing processes in the aerospace industry. For example, millions of fasteners are utilized in the Boeing 777 to connect around 3 million different structural components. However, screw fasteners mounted by articulated arm robots in the assembly of aircraft parts are subject to positional uncertainties in both dynamic and static applications. The resulting production errors not only have an impact on cost and time but also lead to safety risks. Despite this strong theoretical and practical relevance, only few research approaches focus on the recognition of defective fasteners in aircraft manufacturing, and the detection of incorrectly assembled aeronautical threaded fasteners remains open. To address these challenges, we identified important time series channels and developed a hard voting ensemble of Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) to predict the outcomes of screw fasteners mounted by articulated arm robots in the assembly of aircraft parts. Our model achieved a balanced accuracy of 81.08% on a time series dataset containing both kinematic and dynamic data obtained from a robotic assembly process of aeronautical screwed fasteners. While both CatBoost and XGBoost reached high performance metrics individually, the ensemble approach outperformed each model by utilizing their complementary strengths. Overall, these findings underscore the need for robust machine learning methods to enhance safety standards and reliability in critical assembly processes through advanced techniques.