# The Design of a System for the Induction Hardening of Steels Using Simulation Parameters
###### Abstract
Hardened steel is characterized by high hardness, great brittleness and significant internal stress. The steel parts must be loosened after hardening to reduce these stresses. Induction hardening is the most commonly used surface hardening method due to its high productivity and the high quality of the hardened layer. Heating with a high-frequency current is based on the so-called \"skin effect\" phenomenon. The surface layers of the steel part heat up rapidly to high temperatures, while the inner layers remain cold or heat up only slightly due to the thermal conductivity of the steel. An alternative high-quality method is laser surface hardening, but this involves higher costs and has limited applications compared to the induction method [20]. Another relatively modern method is electron beam hardening. Similar to the previous method, the interaction of the beam and material generates heat in the surface layer of the sample. Although it has advantages over the laser method, its comparatively low energy efficiency (even over 70%) [21], high investment costs and the creation of significant artefacts in X-ray computed tomography images (in the control or process of the material analysis) [22] are substantial disadvantages compared to the induction process. The maximum hardness achieved during induction hardening is 2-3 HRC units higher than during classical heating in a furnace. Other mechanical properties of steel (tensile strength, yield strength, elongation, contraction and impact toughness) also show higher values after induction hardening than after classical hardening [19]. The additional advantages of induction hardening compared to classic hardening are as follows:  
* Lower energy consumption;
* Higher productivity;
* The greater possibility of automating the process;
* Higher-quality tempered steel;
* Less material deformation. Some of the most important elements of industrial plants where the induction hardening of steel is applied include electric motor shafts, gears, crankshafts, turbine blades, rolling mill rolls, gauges and tools operating under impact loads. Although induction hardening is a mature technology, it constantly evolves and finds new contemporary applications. A good example is its use in hybrid or duplex technologies, where mechanical and surface thermal processes or two different heat treatment processes are combined, which can considerably increase the processing capacity, as shown in a recently published article [23]. Other implementations include additive manufacturing [24], supporting sustainability in the manufacturing of some mechanical components [25] and further optimization of the high-frequency process, considering the metal phase transformation simulations [26]. Finally, it should be emphasized that induction hardening belongs to the complex multiphysics parameterized problems [27], and thus, it is highly complicated for calculation and simulation. Different approaches have been used to simulate the process: using an algorithm scheme [28], the finite element method [29], various numerical modelling with multiparameter material properties [30], data-driven analysis with applied multi-linear regression [31], or the nonlinear regression technique [27] and neural networks [32]. In terms of energy efficiency, a high-power resonant full-bridge inverter (FBRI) can easily reach a maximal efficiency of 95% at high frequencies [33], but when applied for induction hardening, a similar FBRI at full load in the kW range could achieve a much lower efficiency of 85% [34]. A higher efficiency for induction hardening could be achieved using a particular test setup, with an approach relying on efficiency maximization, leading to an efficiency of up to 91% [35]. An efficiency above 95% is reserved for low-temperature applications, where even 97% efficiency is achievable [36]. However, in induction hardening, only special systems like dual-frequency induction heating (efficiency of the inverter up to 96%) [37] can reach the goal.