In the present study, a 30 W Q-switched fiber laser was adopted for milling 2024 aluminium alloy sheet 2 mm in thickness. Square pockets, 10 × 10 mm2 in plane dimension, were machined at the maximum nominal average power (30 W), under different laser processing parameters: scan speed, hatching distance, pulse energy and repetitions. After machining, the achieved depth (Depth) and roughness (Ra) were measured by way of a 3D surface profiling system. In addition, the material removal rate (MRR) was calculated as the ratio between the removed volume/process time. Analysis of Variance was adopted to assess the effect of the process parameters on the Depth, Ra and MRR. Response Surface Methodology (RSM) was adopted to model the process behaviours; the roughness and MRR were found to be strictly related to the machined depth. In the end, Multi-Response Optimisation (MRO) methodology was adopted to individuate the optimal process conditions allowing the process conditions able to produce the desired depths with the minimum roughness at the maximum MRR.

Multiobjective optimisation of nanosecond fiber laser milling of 2024 T3 aluminium alloy

Leone C.
;
2020

Abstract

In the present study, a 30 W Q-switched fiber laser was adopted for milling 2024 aluminium alloy sheet 2 mm in thickness. Square pockets, 10 × 10 mm2 in plane dimension, were machined at the maximum nominal average power (30 W), under different laser processing parameters: scan speed, hatching distance, pulse energy and repetitions. After machining, the achieved depth (Depth) and roughness (Ra) were measured by way of a 3D surface profiling system. In addition, the material removal rate (MRR) was calculated as the ratio between the removed volume/process time. Analysis of Variance was adopted to assess the effect of the process parameters on the Depth, Ra and MRR. Response Surface Methodology (RSM) was adopted to model the process behaviours; the roughness and MRR were found to be strictly related to the machined depth. In the end, Multi-Response Optimisation (MRO) methodology was adopted to individuate the optimal process conditions allowing the process conditions able to produce the desired depths with the minimum roughness at the maximum MRR.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/432242
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