Laser hardening is a surface treatment process characterized by a high level of performance. The resulting physical, chemical, and mechanical properties of the surface layers can be accurately designed by modifying the process parameters i.e., scanning speed, frequency and laser power. Thus, the development of the laser hardening technology requires considerable preliminary work, including the determination of the range of components that may be hardened, the selection of proper treatment conditions and the identification of optimized strategies to employ such a technology for real industrial components. The present research aimed to provide a deep understanding of the laser hardening process. The effect of process parameters i.e., the laser power, the scanning speed, the number of scans and the overlapping, has been assessed by means of a campaign of experimental tests. Thus, an attempt to predict the effect of process parameters of treated components was carried out by developing an expert system using a neural network.

Laser hardening prediction by using a neural network model

LAMBIASE, FRANCESCO;DI ILIO, Antoniomaria;PAOLETTI, ALFONSO
2012

Abstract

Laser hardening is a surface treatment process characterized by a high level of performance. The resulting physical, chemical, and mechanical properties of the surface layers can be accurately designed by modifying the process parameters i.e., scanning speed, frequency and laser power. Thus, the development of the laser hardening technology requires considerable preliminary work, including the determination of the range of components that may be hardened, the selection of proper treatment conditions and the identification of optimized strategies to employ such a technology for real industrial components. The present research aimed to provide a deep understanding of the laser hardening process. The effect of process parameters i.e., the laser power, the scanning speed, the number of scans and the overlapping, has been assessed by means of a campaign of experimental tests. Thus, an attempt to predict the effect of process parameters of treated components was carried out by developing an expert system using a neural network.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/43921
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