Anodic and spontaneous corrosion of different types of stainless steel (AISI 304L, AISI 316L and 2205 DSS) in phosphate-buffered saline solution (PBS, pH = 7.4) at 37 °C (i.e., in simulated physiological solution in the human body) were examined using open circuit potential measurements, linear and cyclic polarization, and electrochemical impedance spectroscopy methods. After the anodic and spontaneous corrosion, the surface of the tested samples was investigated by light and scanning electron microscopy (SEM) with EDS analysis. It has been established that the tendency of the examined steel materials towards local corrosion decreases in the order: AISI 304L < AISI 316L < 2205 DSS. Namely, the possibility of repassivation and the resistance to local corrosion increases in the same order. The corrosion resistance of steel samples at open circuit potential is a consequence of forming a natural oxide film with a bi-layer structure and consists of an inner barrier and an outer porous film. The inner barrier film has a small thickness and extremely high resistance, while the outer porous film is much thicker but also has significantly lower resistance. The inner barrier layer mainly prevents corrosion of examined steel samples in order: AISI 304L < AISI 316L < 2205 DSS. Light microscopy and SEM/EDS analysis after pitting and spontaneous corrosion showed damage on the AISI 304L and AISI 316L surface, while the surface of 2205 DSS was almost undamaged by corrosion.

This paper presents a new approach improving the reliability of flank wear prediction during the end milling process. In the present work, prediction of flank wear has been achieved by using cutting parameters and force signals as the sensitive carriers of information about the machining process. A series of experiments were conducted to establish the relationship between flank wear and cutting force components as well as the cutting parameters such as cutting speed, feed per tooth, and radial depth of cut. In order to be able to predict flank wear a new linear regression mathematical model has been developed by utilizing functional data analysis methodology. Regression coefficients of the model are in the form of time dependent functions that have been determined through the use of functional data analysis methodology. The mathematical model has been developed by means of applied cutting parameters and measured cutting forces components during the end milling of workpiece made of 42CrMo4 steel. The efficiency and flexibility of the developed model have been verified by comparing it with the separate experimental data set.

Choosing the appropriate cutting tool material is essential for enhancing machining processes because it directly affects product quality, surface finish, dimensional accuracy, tool longevity, and overall efficiency. Different materials are used for cutting tools, i.e., for cutting inserts. Due to their high hardness and high temperature resistance, ceramics cutting inserts allow for increased cutting speeds, resulting in shorter manufacturing times and reduced costs, despite being pricier than traditional cemented carbide and facing certain technical challenges due to their brittleness. Alumina-based ceramics dominate the market, accounting for about two-thirds of usage, followed by silicon nitride and zirconia. This paper provides a comprehensive overview of recent advances in alumina ceramic materials used as cutting inserts, focusing on research conducted in the last five years to optimize static and dynamic mechanical and thermal properties, wear resistance, density, etc. They ways in which the properties are altered through the incorporation of whiskers, nanoparticles, or nanotubes; the modification of the structure; the optimization of sintering parameters; and the application of advanced sintering techniques are demonstrated. The paper also addresses sustainability, environmental impact, and the management of critical raw materials associated with cutting inserts, which pertains to the future development of cutting insert materials.

Improving machining performance and meeting the requirements of sustainable production at the same time represents a major challenge for the metalworking industry and scientific community. One approach to satisfying the above challenge is to apply different types of cutting fluids or to optimise their usage during the machining process. The fact that cutting fluids are well known as significant environmental pollutants in the metalworking industry has encouraged researchers to discover new environmentally friendly ways of cooling and lubricating in the machining process. Therefore, the main goal is to investigate the influence of different machining conditions on the efficiency of hard machining and find a sustainable solution towards smart manufacturing. In the experimental part of the work, the influence of various machining parameters and conditions on the efficiency of the process was investigated and measured through the surface roughness, tool wear and cutting force components. Statistical data processing was carried out, and predictive mathematical models were developed. An important achievement is the knowledge of the efficiency of compressed cold air cooling for hard milling with the resulting lowest average flank wear of 0.05 mm, average surface roughness of 0.28 µm, which corresponds to grinding procedure roughness classes of N4 and N5, and average tool durability increase of 26% compared to dry cutting and conventional use of cutting fluids. Becoming a smart machining system was assured via technological improvement achieved through the reliable prediction of tool wear obtained by radial basis neural networks modelling, with a relative prediction error of 3.97%.

The main aim of this research is to mathematically describe the influence of the processing parameters of metal foam production from machining chip waste. Using this method, metal foams were produced without a remelting step, which should be both economically and environmentally effective. Firstly, expensive metal powders were replaced with waste in the form of machining chips. Secondly, machining chip waste was recycled without any significant material losses, which usually occurs during conventional recycling (using the melting process). To describe the innovative process and to relate metal foam properties to foaming temperature, the blowing agent weight percentage, and foam density (controlled by foaming height), response surface methodology, and the design of experiments were used. The quality of the produced metal foams was evaluated by determination of density, yield strength, compression strength, plateau stress, energy absorption, pore perimeter, and pore inhomogeneity for specimens obtained following the experimental plan. It was proven that pore inhomogeneity increased in the range from 1.41 to 4.81 mm with a higher temperature and the addition of a foaming agent. However, higher energy absorption and yield strength were obtained with a higher temperature but a lower percentage of TiH₂. Despite the production from machining chips, pores were homogenous without significant cracks. These kinds of metal foams are comparable to commercial foams made of metal powders.

The latest trends in machining research show that great efforts are being made to understand the impact of different cooling and lubrication techniques as well as cutting parameters on machining performances. This paper presents the investigation results of different cutting parameters and different cutting environments such as dry machining, minimum quantity lubrication (MQL) and minimum quantity lubrication with compressed cold air (MQL+CCA) on average surface roughness, cutting force and material removal rate. The experiments were designed based on three input parameters and three different cutting environments when turning of EN AW-2011 alloy. Taguchi-based grey relational analysis was used to identify the optimal process parameters by which minimum values of surface roughness, minimum value of cutting force and maximum value of material removal rate will be achieved. The results showed that minimum quantity lubrication in the stream of compressed cold air, in comparison to dry and minimum quantity lubrication machining, gives the best machining performances. Therefore, the use of MQL + CCA method, which reduces the amount of lubricant may represent in the described extent of turning operations an alternative to turning processes most often carried out by wet method that causes considerable costs for purchasing, maintaining and using cutting fluids.