## 1 Introduction  
Industry 5.0 reflects a fundamental shift of companies and economies towards a new paradigm for balancing economic development with solving social and environmental problems [1]. It focuses on three interconnected core values: human-centeredness, sustainability, and resilience (Figure 1). Industry 5.0 recognizes the power of industry to achieve societal goals, to become a provider of prosperity by making production respect the boundaries of our planet, and  
Figure 1: Three pillars of Industry 5.0 [2]. to put the well-being of industrial workers at the center of the production process. One of its priorities is environmental protection. Recycling rates are increasing in Europe and the reuse and recycling of discarded products and materials will become a necessity as the shortage of raw materials becomes more pressing. Some materials fit quite easily into the circular economy concept, while others (such as composite materials, fiber-reinforced plastics, metallurgical waste, etc.) present a much more challenging challenge and require further research [2]. The effort is, therefore, in conjunction with intelligent digital technology, artificial intelligence, and big data, to create a more integrated and better-connected ecosystem for companies [3]. Artificial intelligence, as a key element, should significantly contribute to the fulfilment of the goals of Industry 5.0 in all its areas. The presented contribution points out the possibilities of using artificial intelligence-machine learning (ML) methods in the design of a model for evaluating the absorption properties of selected composite materials. A composite material is a material that is made from two or more basic materials that have different chemical or physical properties. By combining them, a new material with new properties will be created. Within the structure of the new material, individual elements remain separate and distinct, distinguishing composites from mixtures and solid solutions. Base materials often have distinctly characteristic properties, and the combined architecture built from the base materials allows composites to have unprecedented properties. Recently, ML is seen as a promising tool for designing and discovering new composite materials [4]. This technique was described in [5] where machine learning was applied to a composite system in predicting mechanical properties, including toughness and strength; in [6] ML was used as a tool to develop polymer composites with higher specific strength and stiffness along with higher resistance to impact, wear, and fatigue. The advantage of using ML in model finding for evaluating the properties of composite materials is that using only a subset of the possible data, ML algorithms can uncover hidden patterns in the data and learn an objective function that best maps the input variables to an output variable (or output variables)--a process called the training process [5]. In order to understand the behavior of the process and the interrelationships between input parameters and outputs (reactions) in any process, it is useful to look for suitable models [1; 7]. The developed models can also act as effective prediction tools in predicting preliminary response values for given sets of input parameters [1]. Depending on the architecture used, they can sometimes be called deep machine learning. These algorithms use computational methods to \"learn\" information directly from the data without a predetermined equation as a model [8; 9; 10]. To find the problem optimal solutions, gain knowledge of the process, or predict the future state are used computer models [9; 10], as do many other machine learning algorithms, neural networks work by building a model that we then train on as much labeled data as possible [11]. Based on this known training data, the model learns to \"predict\" the outcomes of new, unknown cases. A \"self-learning\" process can create prediction models from the history of time-dependent data.