## 1 Introduction  
Remarkable progress has been made in the development of methods and tools for modeling and simulating the production, processing, and structural properties of steels and metal alloys. Computational modeling, a cost-effective approach to optimize certain factors, such as chemical composition and process conditions, is widely used in scientific and industrial research and helps to achieve the desired properties in metal materials [1, 2, 3, 4]. A more cost-effective approach for evaluating the hardness of continuously cooled steel from the austenitizing temperature is the Jominy end-quench test. Integrating the results of this test into models used for heat treatment simulations requires the computation of cooling rates at specific locations on the cooled object, associating them with respective distances from the quenched end of the sample [5]. The techniques for computing Jominy hardenability curves are described in detail in various studies, including [6, 7, 8, 9, 10, 11]. Modern steelmaking techniques allow precise regulation of the chemical composition and hardenability, with some manufacturers advocating strict limitations on hardenability. The widely used Jominy end-quench hardenability test is an important tool in the production, specification, procurement, and application of heat-treatable structural steels, which are critical to modern transportation, construction, and agricultural machinery. The delineation of hardenability bands and the associated metallurgical methods, which link hardenability to heat treatment response, microstructure evolution, and eventual mechanical properties, form the basis for the economically viable selection of steels and the design of components [12]. The hardenability of steel depends on its composition, subject to specific limits defined for each steel grade. Despite compliance with the compositional specifications, a typical variation in hardenability can occur for each steel grade. In some instances, tighter compositional control is essential for the applications for which the steel is used. Therefore, several steels are offered in H-grade variants with tighter compositional control. The extent of control is precisely defined by the maximum and minimum hardenability limits [13]. In the field of materials engineering, there is a growing interest in using artificial and computational intelligence [4; 14; 15]. The increasing accessibility of material databases and advances in machine learning open up new possibilities for the prediction of material properties and the development of next-generation materials [16; 17; 18; 19]. One of the best-known methods of computational intelligence is the use of artificial neural networks (ANN) [20; 21; 22]. Artificial neural networks (ANNs) are invaluable for practical applications as they overcome the hurdles associated with formulating mathematical models. Their ability to establish connections between studied variables without the need for explicit mathematical descriptions is a distinctive feature that enables ANNs to learn solutions to problems based on identified patterns from the given experimentally collected data [1; 23; 24; 25]. Artificial neural networks (ANNs), especially multilayer perceptron networks (MLPs), are widely used in modeling steel and metal alloy problems due to their efficiency in handling classification and regression tasks. ANNs are characterized by their ability to learn from labeled dataset and are, therefore, well suited for supervised learning applications [26; 27; 28]. The critical requirement for the development of an effective neural model is the creation of a representative dataset. This requires quantifying various requirements, such as data availability, labelling accuracy, and clustering adequacy. For example, the dataset must cover a comprehensive range of variables, and their statistical distribution must be thoroughly evaluated.