## 1 Introduction  
Due to the technical and economic significance of cutting technology, numerous studies have been carried out, all of which were in order to model the process itself to increase machining quality and productivity, and thus reduce processing costs [1]. According to [PERSON] [2], in the United States, the correct choice of tools is made in less than 50% of cases, while the nominal cutting speed is used in 58% of cases, and 38% of the tool is used up to its maximum durability. The main objectives of process modelling treatment are to increase the productivity, efficiency, total quality of products or individual aspects (machined surface, tool life, etc.) and reducing the consumption of materials, energy, processing time, and processing costs per unit of product [3]. It is difficult to theoretically use analytical models reliably to determine the processing parameters such as tool wear, optimum geometric shape, deformation phenomena in the tool, limit deformation,tribological processes, and tool loads, because in each machining process, there are several influential factors with interactions between them. It is well known that during hard machining, because of the great influence on the final characteristics of machining parts, cutting temperature is of significance. Cutting temperature is also very important from the economic side and optimization of the machining process [4]. In the manufacturing industry, there is an increased demand for high quality products, high productivity, and overall economy by hard machining, especially in meeting global cost competitiveness. Therefore, it is of great importance to make an initial model that will be able to determine and evaluate the cutting temperature as one of the main output characteristics in hard machining. In the machining processes, the significance of cutting temperature has been well accepted in the metal processing industry from two points of view. The first reflects on the impact on tool wear and on the processing efficiency limit. The second relates to the important effect on the integrity of the workpiece surface, i.e., the heat affected zone, in terms of hardness and surface roughness [5; 6]. In [PERSON] and [PERSON] [7], it is shown that the prediction of the cutting temperature plays an essential role in the machining industry for the correct planning and control of processing parameters and optimizing cutting conditions. For specific and demanding machining operations, the ability to obtain new information is a very important advantage [8; 9]. One of the most common indicators of tool wear is cutting temperature [10]. These findings have been confirmed by many researchers, among them [PERSON] and [PERSON] [11]. They monitored the cutting temperature using an infrared camera and evaluated the influence of heat expansion to the cutting tool. Based on this, the authors explained that increasing the temperature of the tool accelerates the process of tool wear. This statement is of great importance because the costs of cutting tools and their replacement must be reduced to a minimum [12; 13]. From an economic point of view, the tool wear has a main role in machining processes. Also, [PERSON] et al. [14] are convinced that the identification of optimal processing parameters is essential for economical processing. Many researchers who participated in the modelling of cutting temperature used different techniques. One of the possibilities is the application of an artificial intelligence tool, which proved to be very successful in the modelling of machining processes [15; 16]. Intelligent modelling techniques have good properties for modelling complex manufacturing processes [17; 18]. In recent years, a large range of artificial intelligence (AI)-based techniques have been developed that model the correlation between the input (process data such as cutting speed, feed, and depth of cut) and the output (tool life, cutting temperature, surface quality, etc.) parameters of the turning process. [PERSON] et al.