In this work a preliminary statistical study on laser machining of titanium alloy is presented. Laser machining tests were carried out on Ti6Al4V alloy sheet, 2 mm thick, using a Q-Switched 30 W Yb:YAG fiber laser. The aim of the paper is to characterize the laser engraving process; that is, to detect which process parameters affect the quality of the machining surface in terms of depth of machined volume and roughness. The examined parameters were: the lease beam scan speed, the pulse frequency, the distance between the linear patterns of two consecutive laser scans (step), the number of repetitions of the geometric pattern and the scanning strategy. A two-level fractional factorial design and ANalysis Of VAriance (ANOVA) were applied. Moreover experimental results have shown that , for each adopted pulse frequency, the machined volume linearly depend on the total amount of released energy. A predictive model of the machined volume was proposed and verified. At last the process map in term of Material Removal Rate (MRR) and roughness has been evaluated and discussed too.

Statistical analysis of fiber laser machining of titanium alloy

LEONE, Claudio;
2012

Abstract

In this work a preliminary statistical study on laser machining of titanium alloy is presented. Laser machining tests were carried out on Ti6Al4V alloy sheet, 2 mm thick, using a Q-Switched 30 W Yb:YAG fiber laser. The aim of the paper is to characterize the laser engraving process; that is, to detect which process parameters affect the quality of the machining surface in terms of depth of machined volume and roughness. The examined parameters were: the lease beam scan speed, the pulse frequency, the distance between the linear patterns of two consecutive laser scans (step), the number of repetitions of the geometric pattern and the scanning strategy. A two-level fractional factorial design and ANalysis Of VAriance (ANOVA) were applied. Moreover experimental results have shown that , for each adopted pulse frequency, the machined volume linearly depend on the total amount of released energy. A predictive model of the machined volume was proposed and verified. At last the process map in term of Material Removal Rate (MRR) and roughness has been evaluated and discussed too.
F., Tagliaferri; N., Pagano; Leone, Claudio; B., Palumbo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/329451
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