Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

This study investigates the mechanical properties, surface integrity, and chemical configuration of PVD-coated high-speed steel (HSS) cutting tools, with a particular focus on titanium nitride (TiN) and titanium aluminium nitride (TiAlN) coatings. A range of characterisation methodologies were employed to examine the impact of pre-coating surface conditions on the resulting coatings. This impact includes the effects of gas bubble production and unequal distribution of elements, which are two unwanted occurrences. Notwithstanding these difficulties, coatings applied on surfaces that were highly polished exhibited more consistency in their mechanical and elemental characteristics, with a thickness ranging from 2 to 4 µm. The study of mechanical characteristics confirms a significant increase in hardness, from an initial value of roughly 1000 HV0.5 for untreated tools to 1300 HV0.5 for tools with physical vapour deposition (PVD) coatings. Although PVD coatings produced on an industrial scale might not exceed the quality of coatings manufactured in a laboratory, they do offer substantial enhancements in terms of hardness. This study highlights the significant importance of thorough surface preparation in achieving enhanced coating performance, hence contributing to the efforts to prolong the lifespan of tools and enhance their performance even under demanding operational circumstances.

The performance and lifespan of cutting tools are significantly influenced by their surface quality. The present report highlights recent advances in enhancing the surface characteristics of tungsten carbide and high-speed steel cutting tools using a novel micro-machining technique for polishing and edge-honing. Notably, the main aim was to reduce the surface roughness while maintaining the hardness of the materials at an optimal level. By conducting a thorough analysis of surfaces obtained using different techniques, it was found that the micro-machining method effectively decreased the surface roughness of the cutting tools the most effectively out of the techniques investigated. Significantly, the surface roughness was reduced from an initial measurement of 400 nm to an impressive value of 60 nm. No significant change in hardness was observed, which guarantees the maintenance of the mechanical properties of the cutting tools. This analysis enhances the comprehension of surface enhancement methodologies for cutting tools through the presentation of these findings. The observed decrease in surface roughness, along with the consistent hardness, exhibits potential for improving tool performance. These enhancements possess the capacity to optimise manufacturing processes, increase tool reliability, and minimise waste generation.