# Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy
## 1 Introduction  
Wood and wood-based materials have irreplaceable interior and exterior uses in many applications due to their mechanical and aesthetic properties, which can be reduced in outdoor environments [1, 2]. By using surface inactivation, oil extraction substances can be expulsed onto the surface of the wood, preventing the access of hydroxyl groups to the wood cell wall. Some extracts from wood (namely, fatty acids, terpenes, phenols, and so on) may be displaced to the outside layer of the wood, becoming a resinous layer. Inactivating the surface layer for many wood applications shows improvements in terms of lower roughness and reduced losses in plane machining, resulting in higher-quality wood surfaces. In order to achieve better physical and mechanical properties or a colour change, the use of a treatment process is important, and heat treatment is one of the most common methods for material treatment [3]. Depending on the medium and material used in the treatment process, there are several types of heat treatment [4]. Thermally modified wood can be found almost everywhere, and currently, the most common methods are, for example, ThermoWood in Finland, Plato Wood in the Netherlands, oil heat treatment (OHT) in Germany, and Les Bois Perdure and the rectification process (Retiwood) in France [5, 6]. There are also lesser-known treatment processes that use superheated steam, such as Wood Treatment Technology (WTT) in Denmark and Firmolin technology in the Netherlands, or a partial vacuum, like Temovuoto in Italy [7]. The overall process of heat treatment can be defined as a treatment in which wood is processed at high temperatures in ranges between 100 and 260 \({}^{\circ}\)C and in an environment without the use of a chemical substance but rather in a type of medium (steam, nitrogen, oil, etc.) [6, 8]. In the furniture industry, woodworking processes such as milling are widespread; milling is achieved using tools--cutters with a rotating motion--the trajectory of whichcould be described as the movement of cutting teeth travelling along a cycloid curve. Depending on requirements and quality, the milling process has to achieve a certain accuracy and roughness of the surface [9]. With the selection of certain milling parameters, one can achieve the increased surface quality of woodwork products [10; 11; 12] and a better optimisation of energy consumption [13; 14]. The results of empirical research show that heat-treated wood can be used for milling because the treatment does not affect the grain size of chips formed in sawing and milling [15]. Even beyond the manufacturing sphere, the use of models created by an artificial intelligence machine learning algorithm is becoming more common. Based on data acquired via experiments, it is possible to create a prediction model that predicts the factors that influence the technical parameters of manufacturing [16]. Machine learning consists of the computation of huge amounts of acquired data for the creation of a prediction model; three basic types of machine learning are used for this purpose: supervised learning, unsupervised learning, and feedback learning [17]. To predict process parameters, tools are used to analyse the measured data and create the subsequent model. These tools include machine learning as a subfield in artificial intelligence, regression models, artificial neural networks, deep learning [18; 19], decision trees, and support vector machines [20]. Common algorithms used in unsupervised learning include hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models [20]. Currently, there exist several types of approaches to machine learning for the purpose of the prediction of important parameters in machining processes [21], such as cutting force, surface roughness [22], and cutting performance [23]. However, very few works are dedicated to the creation of models and the analysis of signals in the processing of wood-based materials.