DIGITAL TWIN FOR 5-AXIS MILLING OF THIN-WALLED STRUCTURES

Abstract

This study presents a digital twin of 5-axis milling processes for thin-walled parts to predict and prevent the occurrence of static deflection issues, surface location error due to forced vibration, and regenerative chatter. Driven by real-time machine signals or machining programs, the twin continuously simulates the milling process given an initial workpiece and any 5-axis toolpath. Thanks to the implementation of an automatic mesher and a finite element model generator, the stiffness and dynamics of the part are determined based on its current geometry. This enables the digital twin to predict and visualize in 3D the static deflection, forced vibration, and process stability, allowing optimization of the process and avoidance of these issues. Finally, the twin is validated on a 5-axis machining case of a steel blade, in which a surface location error caused by a resonance is diagnosed and eliminated.