The numerical simulation of the induction heating process can be computationally expensive, especially if ferromagnetic materials are studied. There are several analytical models that describe the electromagnetic phenomena. However, these are very limited by the geometry of the coil and the workpiece. Thus, the usual method for computing more complex systems is to use the finite element method to solve the set of equations in the multiphysical system, but this easily becomes very time consuming. This paper deals with the problem of solving a coupled electromagnetic - thermal problem with higher computational efficiency. For this purpose, a semi-analytical modeling strategy is proposed, that is based on an initial finite element computation, followed by the use of analytical electromagnetic equations to solve the coupled electromagnetic-thermal problem. The usage of the simplified model is restricted to simple geometrical features such as flat or curved surfaces with great curvature to skin depth ratio. Numerical and experimental validation of the model show an average error between 0.9% and 4.1% in the prediction of the temperature evolution, reaching a greater accuracy than other analyzed commercial softwares. A 3D case of a double-row large size ball bearing is also presented, fully validating the proposed approach in terms of computational time and accuracy for complex industrial cases.

Laser welding has become increasingly popular in recent decades due to its high processing speed and minimal heat-affected zone, which contribute to extended component lifetimes. However, the adoption of this advanced technique is often hindered by a lack of skilled personnel associated with traditional welding and limited awareness of its potential. This study proposes a straightforward methodology for developing a finite element-based thermal model for laser welding, incorporating a two-step experimental calibration process. Using temperature measurements from thermocouples and data from a welding spot test, the numerical model was calibrated to optimize combinations of welding speed and power. The study focuses on S275JR structural steel, involving initial spot weld followed by a linear welding trajectory. The results demonstrate the precision of the proposed calibration approach, as numerical predictions closely align with experimental data from all three thermocouples. The findings validate the effectiveness and accuracy of this methodology, highlighting its ability to adapt spot calibration results to more complex linear welding scenarios. Moreover, the simplicity of the process ensures it is accessible and broadly applicable across different materials and welding configurations. This work offers a reliable and resource-efficient framework for laser welding process optimization in industrial applications.

Induction hardening is a heat treatment process that enhances the mechanical properties of materials, improving their resistance to fatigue, fracture, and wear. Unlike conventional methods, induction hardening selectively heats the surface of the material, forming a high-hardness layer while leaving the core relatively unaffected. In addition, it generates a compressive residual stress layer in the surface, which is beneficial for the component service behaviour. This compressive layer progressively decreases, turning tensile at the boundary of the hardened layer, and gradually decreases in the untreated core. This study first focused on the numerical simulation model to analyze the physical mechanisms involved in the process and select the ideal calculation method. Subsequently, the effects of the material’s carbon content and the quenching severity on hardness and residual stress formation after the induction hardening of a cylindrical 42CrMo4 steel specimen are examined. For this purpose, a coupled thermo-metallurgical-mechanical finite element model in ANSYS^® is employed. The findings underscore the importance of accounting for all factors contributing to stress generation to accurately predict the material’s behaviour. Additionally, the results highlight the significant impact of carbon content on the hardness of the hardened layer, as well as the substantial role of quenching medium severity in shaping the axial residual stress profile within the material.

Induction hardening is a heat treatment that has been increasingly employed in the industry in recent years. It is a complex, highly coupled, and multiphysical process involving electromagnetism, thermal, mechanical, and metallurgical physics. One of the main quality requirements of the process is the hardened case depth generated in the workpiece. The usual method to measure the hardened case and ensure the quality of the parts is to use destructive techniques, which generate material and energy waste and production inefficiencies. Additionally, selecting process parameters such as current, frequency, or scanning speed typically requires several trial-and-error iterations. The goal of this work is to provide a hybrid digital twin (DT) that acts as a nondestructive test technique, predicting the resulting hardened case in real-time and enabling the correction of process parameters during the induction hardening process, ultimately achieving a zero-waste manufacturing scheme. For this purpose, a DT based on an artificial neural network (ANN) model is developed, predicting the hardened case depth in real-time using four monitored input variables: induction frequency, current, and two temperature measurements on the surface of the hardened part. The required data for DT development and training are obtained using a finite element model. Several ANN architectures are evaluated, and the configuration with the best regression results is chosen for implementation in an industrial induction hardening machine. The hardened case predictions obtained from the developed DT demonstrate high accuracy within the analyzed frequency and current range.