## 1 Introduction  
Significant advancements have been achieved in materials science through the integration of computational approaches, particularly machine learning (ML) and artificial intelligence (AI). These two technologies changed how researchers in academia approach complex problems by enabling them to process immense volumes of data, design workflows for optimization purposes, and develop correct forecasting methods [1, 2, 3, 4]. One crucial area in materials science is surface modification of metals. Out of all these processes, boriding, which is a thermochemical diffusion treatment and involves the diffusion of boron atoms into the surface of the metal to form hard boride layers, stands as unique due to its ability to significantly improve properties such as wear resistance and hardness [5, 6, 7]. ML can offer precision and predictive abilities to the boriding process. In the boring of steels, the process parameters such as temperature, time, boring media and methods, and the alloying elements can affect the thickness of the boring layer as well as the hardness or wear characteristics of the coating [5; 8; 9; 20]. For example, [PERSON] et al. [5] showed that the layer thickness and the surface hardness of borided AISI 316L stainless steel increase with process temperature and time. [PERSON] et al. [8] pointed out the effect of alloying elements, especially chromium, in AISI 304L steel as decreasing the boron diffusion, which gives rise to declining coating thickness. [PERSON] and [PERSON] [9] demonstrated that the boring of SAE 1020 steel in different media results in different layer thicknesses. An increased corrosion resistance and surface hardness were reported for AISI 1010 steel after the boring process [10]. [PERSON] and [PERSON] [12] studied the boring of cold-rolled high manganese steel and showed that the thickness of the boring layer increases with process time and temperature. At the same time, the wear and hardness characteristics of the base metal are improved. Additionally, enhancement of the boron diffusion within the presence of manganese was reported [12]. [PERSON] and [PERSON] investigated the boring behavior of S265 steel and X6 CrNiTi18-10 steel and showed the differences in the coating layer thickness and morphologies due the different alloying elements [16]. This kind of study showed that it may be necessary to apply several experiments in order to understand the possible effects of different processes and material parameters on the boring behavior. In this context, ML can provide an insight to estimate the effect of those parameters. There are some studies in the literature regarding the application of ML for the boring process [21; 22; 23; 24]. [PERSON] et al. [21] used an artificial neural network (ANN) approach to estimate the boride layer thickness and hardness of AISI W1 steel for the boring process. [PERSON] et al. [22] studied the growth kinetics of the boride layer on AISI 1045 steel with the help of an ANN. The wear behavior of borided low-carbon steel was investigated using ML [23]. [PERSON] et al. [24] recently used ANNs to predict the boride layer thickness for AISI 1020, AISI 1060, and AISI 4140 steels. Moreover, [PERSON] et al. [25] predicted the boride layer thickness formed on AISI 420, 440C, and 304 steels using both kinetic models and ML techniques, including deep neural networks (DNNs) and adaptive neuro-fuzzy inference systems (ANFISs). These studies mainly focused on a single amount or small amounts of material to analyze the effects of process parameters by ML. To the best of our knowledge, there has not been any research about the prediction of boride thickness in terms of formulation parameters, physicochemical properties, and process conditions. Therefore, in this study, for the first time, a comprehensive analysis of the boring process for ferrous materials was performed with the help of ML.