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

Area of Science:

• Manufacturing Engineering
• Materials Science
• Computational Modeling

Background:

• Broaching is crucial for surface integrity in aerospace, automotive, and energy sectors, affecting component performance under extreme conditions.
• Severe thermomechanical loads, fatigue, and corrosion necessitate predictive models for optimal tool design and machining.
• Ensuring in-service performance of critical components relies on understanding and controlling broaching outcomes.

Purpose of the Study:

• To develop a broaching digital twin utilizing hybrid modeling for accurate and rapid predictions.
• To predict forces per tooth, local overloads, and surface topography irregularities during the broaching process.
• To validate the digital twin's efficacy using a real-world industrial case study.

Main Methods:

• Implementation of a hybrid modeling approach combining analytical, numerical, and empirical techniques.
• Development of a digital twin for simulating the broaching process.
• Validation against a case study of fir-tree broaching of Inconel 718 turbine discs.

Main Results:

• The digital twin achieved an average error below 10% for force predictions.
• The model successfully identified critical teeth and zones susceptible to failure.
• Surface topography irregularities were predicted with an error of less than 15%; a novel link to residual stress was observed.

Conclusions:

• The hybrid-model digital twin provides a reliable tool for predicting broaching performance.
• The model aids in optimizing tool design and machining parameters to enhance component reliability.
• The study experimentally revealed a new relationship between surface topography and residual stress in broached components.