During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool–chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.

In the aluminum die casting process, erosion, corrosion, soldering, and die sticking have a significant influence on tool life and product quality. A number of coatings such as TiN, CrN, and (Cr,Al)N deposited by physical vapor deposition (PVD) have been employed to act as protective coatings due to their high hardness and chemical stability. In this study, the wear performance of two nanocomposite AlTiN and AlCrN coatings with different structures were evaluated. These coatings were deposited on aluminum die casting mold tool substrates (AISI H13 hot work steel) by PVD using pulsed cathodic arc evaporation, equipped with three lateral arc-rotating cathodes (LARC) and one central rotating cathode (CERC). The research was performed in two stages: in the first stage, the outlined coatings were characterized regarding their chemical composition, morphology, and structure using glow discharge optical emission spectroscopy (GDOES), scanning electron microscopy (SEM), and X-ray diffraction (XRD), respectively. Surface morphology and mechanical properties were evaluated by atomic force microscopy (AFM) and nanoindentation. The coating adhesion was studied using Mersedes test and scratch testing. During the second stage, industrial tests were carried out for coated die casting molds. In parallel, tribological tests were also performed in order to determine if a correlation between laboratory and industrial tests can be drawn. All of the results were compared with a benchmark monolayer AlCrN coating. The data obtained show that the best performance was achieved for the AlCrN/Si₃N₄ nanocomposite coating that displays an optimum combination of hardness, adhesion, soldering behavior, oxidation resistance, and stress state. These characteristics are essential for improving the die mold service life. Therefore, this coating emerges as a novelty to be used to protect aluminum die casting molds.

In this study, the wear mechanisms and tribological performance of uncoated and coated carbide tools were investigated during the turning of super duplex stainless steel (SDSS)—Grade UNS S32750, known commercially as SAF 2507. The tool wear was evaluated throughout the cutting tests and the wear mechanisms were investigated using an Alicona Infinite Focus microscope and a scanning electron microscope (SEM) equipped with energy dispersive spectroscopy (EDS). Tribo-film formation on the worn rake surface of the tool was analyzed using X-ray Photoelectron Spectroscopy (XPS). In addition, tribological performance was evaluated by studying chip characteristics such as thickness, compression ratio, shear angle, and undersurface morphology. Finally, surface integrity of the machined surface was investigated using the Alicona microscope to measure surface roughness and SEM to reveal the surface distortions created during the cutting process, combined with cutting force analyses. The results obtained showed that the predominant wear mechanisms are adhesion and chipping for all tools investigated and that the AlTiN coating system exhibited better performance in all aspects when compared with CVD TiCN + Al₂O₃ coated cutting insert and uncoated carbide insert; in particular, built-up edge formation was significantly reduced.

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques.