The development of innovative technologies and the employment of diverse material compositions have contributed to the enhancement of wear prediction methods. However, the accurate forecasting of service life and the identification of critical influencing factors remain challenging due to the complex interactions governing wear behaviour. Throughout history, various methodological approaches have been developed to model wear, primarily categorised into analytical calculations and experimental investigations. Analytical methods, including Archard’s equation and its variations, provide a theoretical basis for wear estimation. However, these models frequently depend on empirical coefficients derived from extensive experimentation, which restricts their predictive accuracy. Moreover, classical wear models do not fully account for material fatigue effects and 3D surface texture parameters, which are critical for solving complex engineering problems. Recent advancements have sought to address these limitations by integrating probabilistic surface modelling, fatigue-based degradation theories, and numerical simulations to enhance wear predictions. Experimental investigations remain essential for validating analytical models, as they provide empirical data necessary for parameter calibration. However, these experiments require specialised equipment and are often time-consuming and costly. The integration of modern measurement tools and numerical simulations, such as finite element analysis (FEA) and machine learning-based models, presents a promising direction for improving wear predictions. This review highlights the strengths and limitations of existing wear models and emphasises the need for further refinement of analytical approaches to incorporate fatigue wear mechanisms, real surface roughness effects, and environmental influences for more accurate and reliable wear assessments.

One of the principal objectives of modern production process is the improvement of quality level; this means also guaranteeing the required service life of different products and increase in their wear resistance. To perform this task, prediction of service life of fitted components is of crucial value, since with the development of production technologies and measuring devices it is possible to determine with ever increasing precision the data to be used also in analytical calculations. Having studied the prediction theories of wear process that have been developed in the course of time and can be classified into definite groups one can state that each of them has shortcomings that might strongly impair the results thus making unnecessary theoretical calculations. The proposed model for wear calculation is based on the application of theories from several branches of science to the description of 3D surface micro-topography, assessing the material’s physical and mechanical characteristics, substantiating the regularities in creation of the material particles separated during the wear process and taking into consideration definite service conditions of fittings.

Promocijas darbā izstrādāts jauns nodiluma aprēķina un analīzes matemātiskais modelis, kas ietver virsmas tekstūras (3D) parametrus, deformācijas veidu un sprieguma parametrus nogurumnodiluma gadījumā. Sintezēts jauns berzes detaļu virsmu kontakta modelis un izstrādāta jauna berzes pāra kalpošanas laika aprēķinu metodika. Veikta slīdes berzes pāra nodiluma matemātiskā modeļa aprēķinu rezultātu aprobācija. Analizēta eksperimentāli iegūto rezultātu nodiluma vērtību korelācija ar analītiskajiem aprēķiniem. Sintezētais berzes virsmu nodiluma aprēķina modelis nodrošina iespēju analītiski noteikt nodiluma vērtības, kā arī analizēt un optimizēt nodilumu ietekmējošos parametrus mehānisma projektēšanas procesā.

A new mathematical model for wear calculation and analysis has been developed to determine the wear of sliding friction pairs, including surface texture (3D) parameters, deformation mode and stress parameters in the case of fatigue wear.