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Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials, such as poor machinability, low cutting efficiency, and high energy consumption. High-speed dry milling has emerged as a typical green processing technology due to its high processing efficiency and avoidance of cutting fluids. However, the lack of necessary cooling and lubrication in high-speed dry milling makes it difficult to meet the continuous milling requirements for difficult-to-machine metal materials. The introduction of advanced energy-field-assisted green processing technology can improve the machinability of such metallic materials and achieve efficient precision manufacturing, making it a focus of academic and industrial research. In this review, the characteristics and limitations of high-speed dry milling of difficult-to-machine metal materials, including titanium alloys, nickel-based alloys, and high-strength steel, are systematically explored. The laser energy field, ultrasonic energy field, and cryogenic minimum quantity lubrication energy fields are introduced. By analyzing the effects of changing the energy field and cutting parameters on tool wear, chip morphology, cutting force, temperature, and surface quality of the workpiece during milling, the superiority of energy-field-assisted milling of difficult-to-machine metal materials is demonstrated. Finally, the shortcomings and technical challenges of energy-field-assisted milling are summarized in detail, providing feasible ideas for realizing multi-energy field collaborative green machining of difficult-to-machine metal materials in the future.

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Existing research on coated tools does not predict data while exploring the changing rules. As well as the traditional cutting process parameters neither guarantees the surface quality of the 30CrMnSiNi2A nor attains high material removal rate (MRR). Accurate control and prediction of workpiece three-dimensional surface roughness (Sq) and specific cutting energy consumption (SCEC) are of vital significance to improve quality, reduce cost and improve efficiency. Here, according to the new SCEC calculation model and the influence of measuring position on Sq, the SCEC and Sq values are accurately obtained. Then, based on the idea of fitting formula, the influence of cutting parameters on SCEC and Sq in high-speed dry (HSD) milling of 30CrMnSiNi2A steel is analyzed according to CVD and PVD coated inserts. Finally, the SCEC and Sq prediction models considering coating type, cutting speed, feed per tooth and cutting width are established by using the XGBoost algorithm. The R2 values of SCEC and Sq are 0.92465 and 0.91527, respectively, indicating that the model has a good prediction effect on experimental data. The feasibility of HSD milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts is verified by analyzing SCEC, Sq and cutting temperature, which provides experimental basis for high efficiency and high precision machining of 30CrMnSiNi2A steel.