This paper presents an empirical study on the impact of maintenance function on more sustainable manufacturing processes. The work methodology comprises four stages. At first, ten factors of maintenance activities from a sustainable manufacturing point of view were identified. Then, in the second stage, the matrix of crossed impact multiplications applied to a classification (MICMAC) was carried out to categorize these factors based on their influence and dependence values. In the third stage, the criteria for evaluation of maintenance factors were defined, then the fuzzy analytic hierarchy process (F-AHP) was applied to determine their relative weights. In the last stage, the results of MICMAC and F-AHP analyses were used as inputs for the fuzzy technique for order preference by similarity to ideal solution (F-TOPIS) to generate aggregate scores and selection of the most important maintenance factors that have an impact on sustainable manufacturing processes. A numerical example is provided to demonstrate the applicability of the approach. It was observed that factors “Implementation of preventive and prognostic service strategies”, “The usage of M&O data collection and processing systems”, and “Modernization of machines and devices” are the major factors that support the realization of sustainable manufacturing process challenges.

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 increase in the performance and effectiveness of maintenance processes is a continuous aim of production enterprises. The elimination of unexpected failures, which generate excessive costs and production losses, is emphasized. The elements that influence the efficiency of maintenance are not only the choice of an appropriate conservation strategy but also the use of appropriate methods and tools to support the decision-making process in this area. The research problem, which was considered in the paper, is an insufficient means of assessing the degree of the implementation of lean maintenance. This problem results in not only the possibility of achieving high efficiency of the exploited machines, but, foremost, it influences a decision process and the formulation of maintenance policy of an enterprise. The purpose of this paper is to present the possibility of using intelligent systems to support decision-making processes in the implementation of the lean maintenance concept, which allows the increase in the operational efficiency of the company’s technical infrastructure. In particular, artificial intelligence methods were used to search for relationships between specific activities carried out under the implementation of lean maintenance and the results obtained. Decision trees and rough set theory were used for the analysis. The decision trees were made for the average value of the overall equipment effectiveness (OEE) indicator. The rough set theory was used to assess the degree of utilization of the lean maintenance strategy. Decision rules were generated based on the proposed algorithms, using RSES software, and their correctness was assessed.

The efficiency of maintenance processes in an enterprise largely depends on ensuring adequate resources for its implementation. The main factor that affects the quality of these processes is competent employees. Their knowledge, skills and ability to respond to unexpected situations largely determine the efficiency of the functioning of the technical infrastructure in an enterprise. In the light of the prospects for the development of the Industry 4.0 concept, and, thus, for the development of highly automated systems, the demand for qualified maintenance employees will increase. Therefore, in order to ensure the right level of competency of maintenance workers, through the proper assessment and identification of their competency gap, is an important task of managers. In many enterprises this is not implemented. The aim of the presented work was to developed a comprehensive model of the competency assessment of maintenance workers. The implementation of the developed model enables the identification of the current level of employees’ competencies and identification of the competency gap, as well as it allows to assess the effects of a failure to meet the required level of competency. Additionally, the results of the identification of the real activities taken by the surveyed enterprises concerning the competency assessment of maintenance services employees are presented in this article. The study was carried out in manufacturing enterprises in different industries on a specific area. The results were analysed and presented in a graphic form.