A New Computationally Efficient Method in Laser Hardening Modeling

Laser hardening is a laser assisted process devoted to the surface hardening of the mechanical components. This process is highly suitable for medium carbon steels with carbon content comprised between 0.2 – 0.6% or for low alloy steels which are usually surface hardened during their manufacturing process. Laser hardening technology is gaining a great industrial interest in the last years in fact, the possibility of integrating the heating source directly on the production line, together with the absence of the quenching medium, meets the production needs of modern industries. Laser hardening optimization could be complex especially when tempering due to multiple passes effects must be considered. Many research studies have been proposed in the last years aimed at predicting the optimal laser process parameters such as beam power density, beam velocity and scanning strategies. Many Authors agree with the assumption that the whole austenite resulting from the heating is transformed into martensite during the quenching. This is a valid approximation for single pass but could be a rough hypothesis in multiple-passes when the cooling rate could be not so high. Moreover hysteresis phenomena, due to the severe heat cycle occurring in laser hardening, should be taken into account for pearlite to austenite and martensite to austenite transformations during heating and for martensite tempering during multiple passes. In this paper the crucial problems to be faced regarding laser surface hardening modeling are discussed with respect to current literature. In particular, partial austenitization of the pearlite is suggested as a solution of the hardness prediction of the profile depth. Then three transformation parameters are proposed in order to take into account the hysteresis phenomena in martensite and pearlite transformations into austenite and in martensite tempering. Finally several experimental examples are proposed in order to validate the mentioned assumptions.