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In the milling process, chatter is easy to occur and has a very adverse impact on the quality of the workpiece and the production efficiency. A chatter feature extraction method based on optimized variational mode decomposition (OVMD) and multi-scale permutation entropy (MPE) was proposed to solve the problem that it is difficult to detect the machining chatter state during milling. The methodology presented in this article allows the occurrence of machining chatter to be effectively identified through real-time digital signal processing and analysis. First, in order to solve the problem of variational mode decomposition (VMD) parameter selection, an automatic selection method based on particle swarm optimization (PSO) and the maximum crest factor of the envelope spectrum (CE) was proposed. Then, the decomposed signal was reconstructed based on the energy ratio. In order to solve the problem that the single-scale permutation entropy (PE) cannot detect milling chatter well, the MPE was introduced to detect milling chatter. Finally, experimental verification was carried out, and the MPE of the reconstructed signals at different scales was extracted and analyzed. The results show that using the OVMD algorithm to process the signals can significantly improve the discrimination of MPE. With the increase of the scale factor, the MPE of the milling signals tends to decrease. At the same time, MPE is better than single-scale PE in chatter detection, and the MPE at scale factor of 4 is more conducive to chatter detection. [ABSTRACT FROM AUTHOR]

Deformation is an unavoidable phenomenon in a thin-walled milling operation, which causes the radial cutting depth to deviate from the initial position and change the tool-workpiece meshing boundary. Milling force is an important factor in the deformation; thus, the phenomenon of machining deformation mechanism is revealed and a machining error prediction model based on the force-deformation coupling relationship is developed in this paper. Discrete micro-cutting disks of the thin-walled parts take into account the deformations and milling cutting force of different contact relationships in the cutting area. The tool-workpiece contact relationship (single-flute cutting and double-flute cutting) is determined by different cutting parameters and the deflection of thin-walled parts is introduced. A detail iterative strategy is developed to calculate the milling force after deformation and the tool-workpiece meshing boundary. By modifying the instantaneous chip thickness of each micro-cutting disk, a new force-deformation coupling relationship and a time-based deformation matrix of different contact relationships are obtained. The machining error is calculated by considering the part deformation and the surface generation mechanism. After finishing systematic experiments, a comprehensive comparison between the model in mechanical prediction and experiments proves that the model captures the material removal mechanism that produces machining error. The results show that the proposed method can effectively predict the machining error. [ABSTRACT FROM AUTHOR]

In the milling process of titanium alloy thin-walled parts, because of its low stiffness, processing deformation easily occurs, which results in in low-dimensional accuracy of machined surface and affecting the workpiece performance. Cutting force is the main factor that causes cutting deformation. Cutting deformation also affects cutting force. There is a coupling relationship between them. To solve the above problems, a method is proposed to predict the surface error by calculating the milling force by varying the chip thickness and by coupling the force with the elastic deformation of the workpiece. Firstly, the analytical model of bending elasticity deformation of thin-walled parts is established. Then, the micro-unit entrance angle and instantaneous chip thickness are calculated by the contact relationship of workpiece deformation and the chip boundary decision conditions. The cutting force and workpiece deformation at random rotating angle are obtained by iterative calculation method. Finally, the surface error is predicted by calculating the deformation matrix and the principle of surface generation mechanism. The simulation results are in good agreement with the experimental results, which verifies the accuracy of the proposed method. The results provide theoretical support for milling process optimization and profile accuracy control of titanium alloy thin-walled parts. [ABSTRACT FROM AUTHOR]

Milling is a kind of interrupted cutting. When the tool cuts into the workpiece, it is often accompanied by instantaneous impact, which results in impact vibration of the milling system. Meanwhile, tool wear will occur gradually on flank face with the cutting progress. The impact vibration and tool wear affect the morphological characteristics of machined surfaces. In the present work, the instantaneous impact force is obtained by introducing the italic impact model of single degree of freedom, and the free vibration in the x and y directions under the impact force is obtained by combining the damped vibration equation. The cutting vibration in the x and y directions is obtained by solving the dynamic equation of the milling system with the fourth-order Runge–Kutta method. The equation of the cutting edge is modified according to the tool flank wear, and the machined surface topography considering the dynamic characteristics of cutting system and tool flank wear is obtained by combining the Z-MAP algorithm. The verification milling experiment was carried out on the hardened steel Cr12MoV workpiece. The simulation results are in good agreement with the experimental results. The research results have important guiding significance for the reasonable selection of processing parameters in actual production. [ABSTRACT FROM AUTHOR]

It has been proved that surface integrity alteration induced by machining process has a profound influence on the performance of a component. As a widely used processing technology, milling technology can process parts of different quality grades according to the processing conditions. The different cutting conditions will directly affect the surface state of the machined parts (surface texture, surface morphology, surface residual stress, etc.) and affect the final performance of the workpiece. Therefore, it is of great significance to reveal the mapping relationship between working conditions, surface integrity, and parts performance in milling process for the rational selection of cutting conditions. The effects of cutting parameters such as cutting speed, feed speed, cutting depth, and tool wear on the machined surface integrity during milling are emphatically reviewed. At the same time, the relationship between the machined surface integrity and the performance of parts is also revealed. Furthermore, problems that exist in the study of surface integrity and workpiece performance in milling process are pointed out and we also suggest that more research should be conducted in this area in future. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the performance of modern aerospace aircraft, the overall strength and lightweight control of aircraft has become a significant feature of modern aerospace parts. With the wide application of thin-walled parts, the requirements for dimensional accuracy and surface quality of workpieces are increasing. In this paper, a numerical model for predicting surface topography of thin-walled parts after elastic deformation is proposed. In view of the geometric characteristics in the cutting process, the cutting force model of thin-walled parts is established, and the meshing relationship between the tool and the workpiece is studied. In addition, the influence of workpiece deformation is considered based on the beam deformation model. Cutting force is calculated based on deformed cutting thickness, and the next cutting–meshing relationship is predicted. The model combines the radial deflection of the workpiece in the feed direction and the changing meshing relationship of the tool–workpiece to determine the three-dimensional topography of the workpiece. The error range between the experimental and the simulation results of surface roughness is 7.45–13.09%, so the simulation three-dimensional morphology has good similarity. The surface topography prediction model provides a fast solution for surface quality control in the thin-walled parts' milling process. [ABSTRACT FROM AUTHOR]

Ball-end cutters are widely used for machining the parts of Ti-6Al-4V, which have the problem of poor machined surface quality due to the low cutting speed near the tool tip. In this paper, through the experiments of inclined surface machining in different feed directions, it is found that the surface adhered damages will form on the machined surface under certain tool postures. It is determined that the formation of surface adhered damage is related to the material adhesion near the cutting edge and the cutting-into/out position within the tool per-rotation cycle. In order to analyze the cutting-into/out process more clearly under different tool postures, the projection models of the cutting edge and the cutter workpiece engagement on the contact plane are established; thus, the complex geometry problem of space is transformed into that of plane. Combined with the case of cutting-into/out, chip morphology, and surface morphology, the formation mechanism of surface adhered damage is analyzed. The analysis results show that the adhered damage can increase the height parameters Sku, Sz, Sp, and Sv of surface topographies. Sz, Sp, and Sv of the normal machined surface without damage (Sku ≈ 3) are about 4–6, 2–3, and 2–3 μm, while Sz, Sp, and Sv with adhered damage (Sku > 3) can reach about 8–20, 4–14, and 3–6 μm in down-milling and 10–25, 7–18, and 3–7 μm in up-milling. The feed direction should be selected along the upper left (Q2: β ∈ [0°, 90°]) or lower left (Q3: β ∈ [90°, 180°]) to avoid surface adhered damage in the down-milling process. For up-milling, the feed direction should be selected along the upper right (Q1: β ∈ (−90°, 0°]) or upper left (Q2: β ∈ [0°, 90°)). [ABSTRACT FROM AUTHOR]

Cr12MoV die steel is a typical high-strength and high-hardness material. Because of the high hardness of Cr12MoV die steel, which is approximately 50–65 HRC after quenching, and the tool's weak rigidity, cutting vibration, and tool deformation are inevitable during the cutting process. In this paper, a model for predicting the surface topography of a convex curved die steel machined by a ball-end milling cutter was established. In addition, the surface springback of the workpiece is considered. According to the surface characteristics of the convex curved workpiece, the vector algorithm and transformation matrix are applied to calculate the milling cutter motion trajectory equation. Then, the influence of dynamic factors on the tool path is calculated, and finally the surface topography of the workpiece is simulated through the Z-map model. The simulation error of three-dimensional surface roughness Sa at different positions of the curved surface is between 10% and 16%. After considering the dynamic factors, the simulation error is reduced by about 50%. [ABSTRACT FROM AUTHOR]

Tool condition monitoring (TCM) in machining operations is crucial to maximise the useful tool life while reducing the risks associated with tool breakage. Unlike progressive tool wear, tool breakage occurs randomly, with more severe implications for workpiece quality, machining system stiffness, and even operator safety. Existing literature reviews on TCM focus on tool wear monitoring, including wear state recognition and remaining useful life prediction. However, a comprehensive review of tool breakage monitoring (TBM) techniques is lacking. Generic signal processing and intelligent decision-making methods cannot fully satisfy the practical requirements of the TBM. In addition, developing and evaluating TBM models using imbalanced data is more challenging. Herein, we present the first systematic review on TBM to bridge these limitations, and provide adequate guidance for avoiding catastrophic tool failures during cutting processes. Signal acquisition, feature extraction, and decision-making methodologies for the TBM are outlined and compared with related techniques for tool wear monitoring. The effects of data imbalance on TBM models are considered, and feasible solutions are provided at the data and algorithm levels. Finally, the challenges faced by the TBM are discussed, and potential research directions are suggested. The research and application of TBM techniques will certainly better empower various machining operations in response to intelligent manufacturing demands. [Display omitted] • Tool breakage monitoring techniques in machining operations are systematically reviewed. • The difference between tool wear and breakage monitoring are compared. • TBM data imbalance problems are discussed at the data and algorithm levels. • The performance metrics commonly used to evaluate TBM models are highlighted. • The specific research route of tool life cycle management is proposed. [ABSTRACT FROM AUTHOR]