Back tempering is one of the most critical problems in laser hardening of extended surfaces. In this type of treatment, several laser tracks are slightly overlapped to obtain a uniform hardened surface. Due to the overlapping, tempered zones are generated on the treated surface with the consequent lack of uniformity in the surface hardness. In this work, a regression model was developed to estimate the loss of hardness due to the tempering effect as a function of the thermal cycle. A specific test, named laser surface treatment test, was designed and executed to reproduce the hardness reduction due to the tempering effect. An analytical thermal model was developed to evaluate the thermal cycle undergone by the material during this test. By the results of the laser surface treatment test combined with the analytical model, a prediction model was estimated. Good agreement was found between predicted and measured hardness decrease, and the identified model could be integrated in a numerical code to evaluate the optimal process parameters.

Modelling of back tempering in laser hardening

GIORLEO, Luca;
2011-01-01

Abstract

Back tempering is one of the most critical problems in laser hardening of extended surfaces. In this type of treatment, several laser tracks are slightly overlapped to obtain a uniform hardened surface. Due to the overlapping, tempered zones are generated on the treated surface with the consequent lack of uniformity in the surface hardness. In this work, a regression model was developed to estimate the loss of hardness due to the tempering effect as a function of the thermal cycle. A specific test, named laser surface treatment test, was designed and executed to reproduce the hardness reduction due to the tempering effect. An analytical thermal model was developed to evaluate the thermal cycle undergone by the material during this test. By the results of the laser surface treatment test combined with the analytical model, a prediction model was estimated. Good agreement was found between predicted and measured hardness decrease, and the identified model could be integrated in a numerical code to evaluate the optimal process parameters.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/47928
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