Laser surface hardening becomes one of the most effective techniques used to enhance wear and fatigue resistance of mechanical parts. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To adequately exploit the benefits presented by the laser heating method, it is necessary to develop a comprehensive strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive approach used to build a simplified model for predicting the hardness profile. A finite element method based prediction model for AISI 4340 steel is investigated. A circular shape with a Gaussian distribution is used for modeling the laser heat source. COMSOL MULTIPHYSICS software is used to solve the heat transfer equations, estimate the temperature distribution in the part and consequently predict the hardness profile. A commercial 3 kW Nd:Yag laser system is combined to a structured experimental design and confirmed statistical analysis tools for conducting the experimental calibration and validation of the model. The results reveal that the model can effectively lead to a consistent and accurate prediction of the hardness profile characteristics under variable hardening parameters and conditions. The results show great concordance between predicted and measured values for the dimensions of hardened and melted zones.

Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.