The products miniaturization tendency of the last years led to an acceleration of micromilling process development. Considering the high-quality requirements, a deep knowledge of this operation, concerning ploughing-shearing transition, tool run-out, and tool edge radius effects, is mandatory, especially when machining difficult-to-cut materials. For this reason, this paper introduces a novel 2D micromachining Finite Element Method simulation strategy for micromilling forces evaluation, when cutting IN625. The major output of this technique consists in the computation of an optimized flow stress law, suitable for the simulation of high-speed machining. Particle Swarm Optimization method was employed for optimizing the flow stress parameters by comparing the cutting force predicted by an analytical model previously calibrated on experimental data, providing good agreement. This strategy permits the micromilling process predictive analysis, avoiding costly optimization experimental tests.

(2023). A Novel 2D Micromilling FEM simulation strategy to optimize the flow stress law of IN625 . Retrieved from https://hdl.handle.net/10446/249249

A Novel 2D Micromilling FEM simulation strategy to optimize the flow stress law of IN625

Cappellini, Cristian;Attanasio, Aldo
2023-01-01

Abstract

The products miniaturization tendency of the last years led to an acceleration of micromilling process development. Considering the high-quality requirements, a deep knowledge of this operation, concerning ploughing-shearing transition, tool run-out, and tool edge radius effects, is mandatory, especially when machining difficult-to-cut materials. For this reason, this paper introduces a novel 2D micromachining Finite Element Method simulation strategy for micromilling forces evaluation, when cutting IN625. The major output of this technique consists in the computation of an optimized flow stress law, suitable for the simulation of high-speed machining. Particle Swarm Optimization method was employed for optimizing the flow stress parameters by comparing the cutting force predicted by an analytical model previously calibrated on experimental data, providing good agreement. This strategy permits the micromilling process predictive analysis, avoiding costly optimization experimental tests.
2023
Abeni, Andrea; Cappellini, Cristian; Attanasio, Aldo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/249249
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