Furthermore, metallic components that are designed to participate in tribosystems often require additional surface modification to enhance their fatigue, wear, and corrosion resistance. For example, the enhancement of surface properties is required in the case of tool steel models that are used for the injection or compression of plastics. In the past, several non-conventional techniques based on the use of high energy beams [7; 8; 9] have been proposed for the surface treatment of Fe-alloys, with very promising scientific results. Nevertheless, owing to their relative lower installation and operational costs, thermochemical surface treatments, such as nitriding and nitrocarburizing, still remain the industrial-level surface treatments of choice for steels and cast irons [10]. Compared to other nitrocarburizing techniques [11; 12; 13; 14], Tuffriding, despite the drawback of a necessary post-processing waste treatment step [15], is the most widespread, since it is less expensive, conceptually simpler, and provides highly reproducible results. It involves the treatment of metallic components in molten cyanide salt baths at 580 \({}^{\circ}\)C [16], a temperature lower than the eutectoid point (591 \({}^{\circ}\)C) of the Fe-N phase diagram, thereby allowing the simultaneous diffusion of nitrogen and carbon atoms into the ferrite lattice [6]. This results in the formation of two distinct surface layers of different nitrogen concentration. The layer on the top of the treated surface is known as the compound or white layer, and it primarily consists of \(\varepsilon\)-carbonitrilefe2-3(C,N). The layer underneath is known as the diffusion zone and it is mainly composed of a \(\alpha\)-(Fe,N) solid solution [17; 18]. These two successive layers constitute the nitrocarburized case that can extend to a depth of several--tens up to hundreds of-- micrometers. Liquid nitrocarburizing is applied as the final chemical-heat treatment for the improvement of tribological performance of various machine parts and friction elements, which are subjected to sliding or rolling friction. Research efforts aim to optimize the material selection and surface treatment parameters, considering the specific application demands. In this context, the performance of nitrocarburized steels has been assessed under sliding [5; 19; 20], rolling [12], erosion [21], and/or corrosion [22] conditions. Despite the fact that extended experimental research on these materials can be found in the international literature, a reliable quantitative tool, that is able to predict the tool steels performance in plane-contact dry sliding tribopairs, is still missing. The strongly nonlinear dependence of the friction and wear coefficients on parameters, such as the applied pressure, the rotational speed, and the bulk hardness (HRC), render the development of an analytical formula for the prediction of tribological performance, using deterministic methods, a rather difficult issue. Meanwhile, artificial neural networks (ANNs) have emerged over the last decades as an attractive meta-modelling technique that is applicable to a vast number of scientific fields, including material science. An important characteristic of ANNs is that they can be used to build soft-sensors, i.e., models with the ability to estimate critical quantities without having to measure them [23]. In particular, such surrogate models can be constructed through a training process with only a few available data that can be used to predict pre-selected model parameters, [PERSON] the need for time- and cost-consuming experiments. Thus far, the literature includes studies in which soft computing techniques, such as ANNs, were implemented for predicting the mechanical properties and behavior of materials. It should be stated that in the scientific field of engineering, the use of soft computing techniques has proven to be the salient meta-computing approach that contributes in a reliable and robust manner in cases where deterministic methods have failed [24; 25; 26; 27; 28; 29].