The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches for optimizing their composition and properties. This study aimed at the application of machine learning for the prediction and optimization of the functional properties of composites based on a thermoplastic matrix with various fillers (two types of fibrous, four types of dispersed, and two types of nano-dispersed fillers). The experimental methods involved material production through powder metallurgy, further microstructural analysis, and mechanical and tribological testing. The microstructural analysis revealed distinct structural modifications and interfacial interactions influencing their functional properties. The key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate mechanical strength. Carbon fibers at 20 wt. % significantly improved wear resistance (by 17–25 times) while reducing tensile strength and elongation. Basalt fibers at 10 wt. % provided an effective balance between reinforcement and wear resistance (by 11–16 times). Kaolin at 2 wt. % greatly enhanced wear resistance (by 45–57 times) with moderate strength reduction. Coke at 20 wt. % maximized wear resistance (by 9−15 times) while maintaining acceptable mechanical properties. Graphite at 10 wt. % ensured a balance between strength and wear, as higher concentrations drastically decreased mechanical properties. Sodium chloride at 5 wt. % offered moderate wear resistance improvement (by 3–4 times) with minimal impact on strength. Titanium dioxide at 3 wt. % enhanced wear resistance (by 11–12.5 times) while slightly reducing tensile strength. Ultra-dispersed PTFE at 1 wt. % optimized both strength and wear properties. The work analyzed in detail the effect of PTFE content and filler content on composite properties based on machine learning-driven prediction. Regression models demonstrated high R-squared values (0.74 for density, 0.67 for tensile strength, 0.80 for relative elongation, and 0.79 for wear intensity), explaining up to 80% of the variability in composite properties. Despite its efficiency, the limitations include potential multicollinearity, a lack of consideration of external factors, and the need for further validation under real-world conditions. Thus, the machine learning approach reduces the need for extensive experimental testing, minimizing material waste and production costs, contributing to SDG 9. This study highlights the potential use of machine learning in polymer composite design, offering a data-driven framework for the rational choice of fillers, thereby contributing to sustainable industrial practices.

The intensifying of the manufacturing process and increasing the efficiency of production planning of precise and non-rigid parts, mainly crankshafts, are the first-priority task in modern manufacturing. The use of various methods for controlling the cutting force under cylindrical infeed grinding and studying its impact on crankpin machining quality and accuracy can improve machining efficiency. The paper deals with developing a comprehensive scientific and methodological approach for determining the experimental dependence parameters’ quantitative values for cutting-force calculation in cylindrical infeed grinding. The main stages of creating a method for conducting a virtual experiment to determine the cutting force depending on the array of defining parameters obtained from experimental studies are outlined. It will make it possible to get recommendations for the formation of a valid route for crankpin machining. The research’s scientific novelty lies in the developed scientific and methodological approach for determining the cutting force, based on the integrated application of an artificial neural network (ANN) and multi-parametric quasi-linear regression analysis. In particular, on production conditions, the proposed method allows the rapid and accurate assessment of the technological parameters’ influence on the power characteristics for the cutting process. A numerical experiment was conducted to study the cutting force and evaluate its value’s primary indicators based on the proposed method. The study’s practical value lies in studying how to improve the grinding performance of the main bearing and connecting rod journals by intensifying cutting modes and optimizing the structure of machining cycles.

In intelligent manufacturing, the phase content and physical and mechanical properties of construction materials can vary due to different suppliers of blanks manufacturers. Therefore, evaluating the composition and properties for implementing a decision-making approach in material selection using up-to-date software is a topical problem in smart manufacturing. Therefore, the article aims to develop a comprehensive automated material selection approach. The proposed method is based on the comprehensive use of normalization and probability approaches and the linear regression procedure formulated in a matrix form. As a result of the study, analytical dependencies for automated material selection were developed. Based on the hypotheses about the impact of the phase composition on physical and mechanical properties, the proposed approach was proven qualitatively and quantitively for carbon steels from AISI 1010 to AISI 1060. The achieved results allowed evaluating the phase composition and physical properties for an arbitrary material from a particular group by its mechanical properties. Overall, an automated material selection approach based on decision-making criteria is helpful for mechanical engineering, smart manufacturing, and industrial engineering purposes.

Issues of technological processes’ intensification and increase of technological equipment efficiency are of priority value in the modern engineering. Application of various methods of hydrodynamic drag on the working fluids in rotary-wheel machines allows increasing of processing efficiency to receive finely-dispersed homogeneous mixtures with necessary characteristics. In this article the issues, connected with the liquid flows' formation in the working zone of rotary-cavitating activator are studied and the main principles of energy impact while the usage of rotor with radial channels, supplied with the elements for receiving cavitating effects, are defined. Based on the offered methodology, experimental research was conducted and calculations of the main indexes, defining efficiency of energy impact on the working medium, were held. The substantiated estimation of efficiency of hydrodynamic machines work according to the dissipative power that allows to choose the proportion of hydrodynamic settings, which provide the highest intensity of cavitating processing of processed liquids, was presented. The practical value of the research is in the studying of ways of increasing of equipment efficiency through the possibility of technological process intensification.