Broaching Digital Twin to Predict Forces, Local Overloads, and Surface Topography Irregularities

Abstract

Broaching is a key manufacturing process that directly influences the surface integrity of critical components, impacting their functional performance in sectors such as aeronautics, automotive, and energy. Such components are subjected to severe conditions, including high thermomechanical loads, fatigue, and corrosion. For this reason, the development of predictive models is essential for determining the optimal tool design and machining conditions to ensure proper in-service performance. This study, therefore, presents a broaching digital twin based on hybrid modelling, which combines analytical, numerical, and empirical approaches to provide rapid and accurate predictions of the forces per tooth, local overloads, and surface topography irregularities. The digital twin was validated with a critical industrial case study involving fir-tree broaching of turbine discs made of forged and age-hardened Inconel 718. The accuracy of the digital twin was demonstrated by the results: the average error in force predictions was below 10%, and the model effectively identified the most critical teeth and zones prone to failure. It also predicted surface topography irregularities with an error of less than 15%. Interestingly, the relationship between surface topography irregularities and surface residual stress variations across the machined surface was observed experimentally for the first time.