Mechanical micro-milling has become a prominent micromachining technique in recent years, and it has advanced high machining efficiency and precision. The advantages of versatility, utility, cost-effectiveness, and efficiency make it suitable for varied industries such as biomedicine, electronics, aerospace, and aviation. However, Conventional Micro-Milling (CMM) faces difficulties, particularly in dealing with difficult-to-cut materials. To solve the above problems, Ultrasonic Vibration-Assisted Micro-Milling (UVAMM) is proposed, which can efficiently address the challenges of machining difficult-to-cut materials. UVAMM is able to inhibit chip formation and reduce the intense friction between the flank surface of the tool and the machined surface. What’s more, it can reduce cutting forces, cutting temperature, and residual stress on the workpiece surface. Finally, it leads to an enhancement in the finished surface quality of difficult-to-cut materials, maximizing the overall machining performance. This paper reviewed UVAMM processing, such as mathematical modeling, chip formation, burr formation, tool wear, cutting forces, cutting temperature, and surface morphology. Furthermore, the finite element simulation of UVAMM and the significance of Minimum Quantity Lubrication (MQL) in UVAMM are discussed. At the end, advantages of UVAMM for difficult-to-cut materials such as titanium alloys, steel alloys, nickel-based alloys, aluminum alloys, composites, brass, and optical glass are summarized.

Undesirable self-excited chatter has always been a typical issue restricting the improvement of robotic milling quality and efficiency. Sensitive chatter identification based on processing signals can prompt operators to adjust the machining process and prevent chatter damage. Compared with the traditional machine tool, the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process make it more challenging to extract chatter information. This paper proposes a novel method of chatter identification using optimized variational mode decomposition (OVMD) with multi-band information fusion and compression technology (MT). During the robotic milling process, the number of decomposed modes k and the penalty coefficient α are optimized based on the dominant component of frequency scope partition and fitness of the mode center frequency. Moreover, the mayfly optimization algorithm (MA) is employed to obtain the global optimal parameter selection. In order to conquer information collection about the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process, MT is presented to reduce computation and extract signal characteristics. Finally, the cross entropy of the image (CEI) is proposed as the final chatter indicator to identify the chatter occurrence. The robotic milling experiments are carried out to verify the proposed method, and the results show that it can distinguish the robotic milling condition by extracting the uncertain multiple chatter frequency bands and overcome the band-moving of the chatter frequency in robotic milling process.