Despite the surge in plasma nitriding research, few reviews thoroughly examine how surface decarburization—occurring both before and during the treatment—affects AISI 4140 and similar steel grades. This review addresses that shortfall by providing an investigation into the decarburization process and its consequences. It compiles essential findings from prior studies, demonstrating instances of decarburization in both plasma-free and plasma-containing environments. Furthermore, this analysis explores strategies to prevent decarburization and assesses its significant impact on the steel’s microstructure, hardness, corrosion resistance, and wear properties in surface and near-surface regions. Moreover, this study proposes directions for future research, emphasizing the necessity for a more detailed understanding of the decarburization mechanisms and their influence on the properties of plasma-nitrided steels.

This paper provides a comprehensive review of research on the influence of machining parameters—depth of cut, feed rate, and cutting speed—on surface roughness, with a focus on aluminum alloys. Surface quality in CNC machining is significantly affected by these parameters, with numerous studies highlighting their impact on achieving desired surface roughness. The review analyzes findings from ten studies, emphasizing that feed rate is generally identified as the most influential parameter for surface roughness. While feed rate shows a dominant effect, cutting speed and depth of cut also contribute, though to a lesser extent. The research includes a discussion of various methodologies, including ANOVA, the Taguchi method, and more simple machine learning regression models, which demonstrate strong alignment with experimental results and highlight the effectiveness of advanced regression-based models in predicting surface roughness. The study concludes that optimizing feed rate is crucial for achieving high surface quality values, while cutting speed and depth of cut should be managed appropriately. The findings underscore the importance of machine learning tools in analyzing and optimizing machining parameters, offering practical guidance for CNC machine operators and engineers.

This work presents Combined Heat and Power (CHP) gas engine factory as one of the possible solutions for cogeneration in the industry which has high values of electrical and total efficiency, and the electrical efficiency relatively changes little with the change in power of the plant. The cost‐effectiveness of such a plant is significant to the supply of industrial consumer electricity from the grid, and heat from the factory boiler. A problem in assessing the cost‐effectiveness of using such a plant is that it uses more fuel here than in the case of energy supply from the boiler room factory and electrical distribution networks. In this case, the price of fuel and electricity as well as the level of placement of thermal energy, as shown here, determines the cost‐effectiveness of the plant. The final decision on the application of the CHP plant must be preceded by a careful technical analysis of the plant and energy system as well as a financial analysis of the selected case. Due to errors that occurred in practice, this paper aimed to point out the basic problems in choosing the power and mode of operation of these plants.