Multiobjective optimisation of nanosecond fiber laser milling of 2024 T3 aluminium alloy. (September 2020)

• Record Type: Journal Article
• Authors: Leone, C.; Genna, S.; Tagliaferri, F.
• Is Part Of: Journal of manufacturing processes. Volume 57(2020)
• Page Start: 288
• Page End: 301
• Publication Date: 2020-09
• Subjects: Laser machining -- Surface quality -- Statistical analysis -- Process optimisation; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.06.026
• Languages: English
• ISSNs: 1526-6125

Abstract

Highlights: Laser milling test were carried out on AA 2024 T3, adopting a Q-Switched fiber laser. The depth, roughness and material removal rate under different process conditions were measured. ANalysis Of VAriance (ANOVA) was adopted to evaluate the effect of the process parameters. Response Surface Method (RSM) was adopted to model the machining process. Multi-Response Optimisation (MRO) was adopted to optimise the process parameters. 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 mm 2 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.