## 2 Investigated Material Structures: Theoretical Background and Related Works  
Austenitic stainless steels (ASSs), especially alloys of grades AISI 304, 316L, and 316 Tl, are often used in biomedical applications. ASSs have several essential qualities that make them an excellent option for orthopaedic applications, especially for the manufacture of implants, artificial joints, and other medical devices. They are highly corrosion-resistant and paramagnetic, which is vital for using magnetic resonance imaging (MRI) techniques. Steels used for biomedical applications contain 17-20% Cr, 12-15% Ni, and 2-3% Mo, which allows them to maintain their austenitic structure across a wide range of temperatures. ASSs cannot be hardened through heat treatment and can only be mechanically strengthened through cold deformation techniques. They exhibit reduced resistance to stress corrosion cracking (SCC), pitting, and intergranular corrosion. However, they offer superior resistance to corrosion from bodily fluids and are well tolerated by the human body. ASSs provide the necessary strength and flexibility for orthopaedic implants and are easily shaped and polished for intricate implant shapes and smooth surfaces. This makes ASSs a top choice for hip, knee, and joint replacement implants due to their durability and ability to handle repetitive motion and load [8; 9; 10; 11]. However, the main problems associated with the functional failure of such implants are their mechanical wear and the development of fatigue fractures. The wear process involves cyclic friction, which causes fragment formation. Fragments induce acute reactions of biological systems to their presence, producing highly corrosive chemicals. These tend to exacerbate the problems associated with bi-material fatigue. Cracks in the material are caused by poor material quality and environmental influences. The reaction of the host tissue to wear debris is a real problem. Fatigue fractures are commonly recorded in artificial hip replacements when failures do not represent a life-threatening condition. However, when implants such as mechanical artificial heart valves are used, such failures can lead to fatal consequences. Avoiding failure altogether is not possible for practical reasons. However, efforts to prevent in vivo failure are important, and the hardness testing of materials can be used for this purpose. Hardness is defined as a material property that quantifies the capacity of a material to resist local plastic deformation. It is the most utilized performance index in metal material mechanics, and it can directly reflect the material's physical properties, such as elasticity, strength, and toughness. Consequently, precise hardness determination is crucial in materials science and engineering. Conventional hardness assessment techniques, such as scratch and indentation testing, offer a straightforward means of obtaining qualitative results. However, these methods typically destroy the sample's surface, which presents a challenge in meeting the high-precision requirements of fields such as aerospace and biomedicine. Therefore, an effective and non-destructive method is needed to measure the hardness of materials to meet the testing needs of various industrial fields [12; 13; 14; 15]. The authors of [16] proposed a non-invasive method for predicting carbon steel hardness by combining micromagnetic testing and artificial neural networks (ANNs). Micromagnetic signals from steel microstructures were used to infer hardness, offering an alternative to conventional indentation methods. A two-step model was employed, with K-nearest neighbours (KNNs) classifying hardness distribution patterns and BP neural networks (BP-NNs) predicting hardness values. The use of this two-step approach improved prediction accuracy, especially for non-uniform datasets, demonstrating the potential of integrating KNNs and BP-NNs for precise, non-destructive hardness testing in industrial applications.