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Tool orientation is an important factor for improving machining stability in five-axis milling. However, there is still a lack of tool orientation planning strategy that is applied to the milling of curved thin-walled parts. This paper proposes a process mechanics-based method to optimize tool orientation to suppress chatter during five-axis ball-end milling of thin-walled parts. First, a coupling dynamic model considering both the flexible tool and workpiece is presented in the tool coordinate system, the model can predict the cutting stability of the entire process of milling thin-walled parts. Then, a binary search algorithm-based single-frequency method is presented to calculate limiting cutting depth. The method does not rely on the initial cutting depth and the increment of cutting depth which selected for calculation, the proposed method can expedite the convergence process for calculating the limiting cutting depth. Moreover, an iterative strategy of first generating smooth tool orientations through the representative tool orientations (RTOs) of typical cutter locations (CLs), and then checking the machining stability and adjusting the tool orientations is proposed to generate chatter-free tool orientations along the tool path. A machining stability factor is proposed to select tool orientation, and the tool orientation with a higher value of the machining stability factor is selected as the RTO. The proposed method only needs to obtain the chatter-free tool orientation regions at typical CLs, the calculation process is rapid. The proposed method has been experimentally proven in five-axis ball-end milling of the block workpiece and curved thin-walled part. [ABSTRACT FROM AUTHOR]

High-quality and high-efficiency machining of thin-wall, low-stiffness, and large-scale skin parts with complex curved surface is in great demand in the aerospace field. Mirror milling is an efficient and green processing technology for large-scale skin parts. The support technology of mirror milling is the key technology to ensure the machining accuracy and surface quality. Aiming at the problems of poor machining accuracy and surface quality, as well as the scratches on the support surface in the sliding and rolling support modes, a new technology of vacuum preloading hydrostatic support was proposed. The machining accuracy and surface quality of thin-wall parts by mirror milling with vacuum preloading hydrostatic support are systematically studied. The main study and findings are as follows: the vacuum preloaded hydrostatic supporting head was designed. The theoretical analysis of the liquid film stiffness and thickness were carried out. The vacuum preloaded hydrostatic support system was designed and developed. Mirror milling experiments of thin-walled parts supported by vacuum preloading hydrostatic pressure were carried out to compare the surface profile and surface roughness under different fluid feed pressure and vacuum preloading. It is concluded that within a certain range, the smaller the vacuum preload and the larger the inlet pressure, the closer the milling depth is to the target depth; the larger the inlet pressure and the larger the vacuum preload, the better the consistency of workpiece thickness of the milled surface; and the presence of the liquid film effectively reduces the roughness of the milled surface. [ABSTRACT FROM AUTHOR]

Thin-walled parts processed by five-axis CNC machine tools are widely used in aerospace and other fields due to their excellent performance. However, due to the weak rigidity of thin-walled parts, they are prone to deformation during milling, which poses great difficulties for efficient and precise machining of thin-walled parts. This paper introduces the classification and corresponding machining methods of thin-walled parts. By analyzing the causes and evolution mechanisms of errors in the machining process of thin-walled parts, and combining modeling methods with factors such as milling force, residual stress, and cutting chatter, the current research status of domestic and foreign scholars on deformation factors is summarized. At the same time, two deformation control methods, adaptive machining and error compensation, were introduced. Finally, the overall research status of thin-walled parts machining was summarized, and prospects for efficient and precise machining of thin-walled parts were proposed based on actual machining conditions. [ABSTRACT FROM AUTHOR]

As a typical thin-walled workpiece with non-equal thickness and closed section, the steam turbine blade is easy to be deformed and to chatter in the machining process due to its low stiffness, which seriously affects the final machining quality. One effective way to solve this problem is to support the workpiece using an assisted robot simultaneously with five-axis machining. The core critical issue of the aforementioned machining strategy is to coordinate the cutter and the supporter by scheduling the path of the end-supporter during support machining. This paper aims at scheduling the path of the end-supporter. This is unconventional and significant because of the following: (1) Due to the "non-equal thickness" feature of the thin-walled parts, the supporter path is not the equal-distance offset of the existing cutter path; (2) due to the "closed section" feature of the thin-walled parts, cyclic cutter-location path is often adopted, which makes the interference among three bodies in terms of the workpiece, the cutter, and the supporter easy to occur. Therefore, to schedule harmonious and interference-free supporter path corresponding to the existing cutter-location path for support machining of the thin-walled parts, this paper proposes an equal chord-tangent angle method for generating the reasonable support path, followed by a spatial-mapping-based optimization method for generating the shortest interference-avoidance path. The support path and the interference-avoidance path formed the integral end-supporter path. Verification test demonstrates that the scheduled supporter path can not only coordinate with the original cutter, but also has no abrupt directional variation or spatial interferences. [ABSTRACT FROM AUTHOR]

Surface residual stress is generated during the micro-milling thin-walled parts of nickel-based superalloy Inconel 718. Because of the low rigidity, deflection of thin-walled parts is easy to occur under residual stress, which makes it difficult to ensure the dimensional accuracy of thin-walled parts. Additionally, the performance of thin-walled parts is directly impacted by the residual stress state. At present, the research on the prediction of residual stress in micro-milling thin-walled parts is still blank. In this paper, the Inconel 718 mesoscopic thin-walled parts are taken as the machining objects. Firstly, a simulation model of micro-milling thin-walled parts process is established by using the finite element program ABAQUS, which realizes the prediction of residual stress in micro-milling thin-walled parts based on process simulation. And the validity of the prediction results of residual stress is verified by XRD experiments. Then, a four-factor and three-level L9 orthogonal simulation experiment is designed, which uses spindle speed, feed per tooth, axial cutting depth, and radial cutting depth as influencing factors. The prediction model of surface residual stress of Inconel 718 micro-milling thin-walled parts is built based on the results of orthogonal simulation experiments. And the model is verified by variance test and experimental validation. Finally, the optimization of cutting parameters for micro-milling Inconel 718 thin-walled parts is achieved with the maximum residual compressive stress as the optimization objective. [ABSTRACT FROM AUTHOR]

Elastic deformation in thin-walled parts during machining is affected by the coupling of force and flexibility. Obtaining flexibility information along machining tool paths is crucial for online monitoring of this deformation. However, the current finite element method (FEM) is limited by mesh nodes, hampering its ability to accurately determine flexibility along tool paths. To overcome this limitation, this paper proposes a method that combines FEM with the surrogate model to predict flexibility accurately at any position on thin-walled parts' surfaces. The surrogate model is the hybrid model K-K-CNN based on two K-nearest neighbor (KNN-KNN) algorithms and a convolutional neural network (CNN) model. Initially, an initial dataset containing positions and flexibility of mesh nodes is generated automatically through secondary development of ABAQUS. Then, the K-K-CNN hybrid model is introduced and trained on this dataset to calculate flexibility accurately at any position on the surface of thin-walled parts. The hybrid model employs a CNN to address the nonlinear spatial correlation issue in flexibility prediction. Moreover, the hybrid model incorporates two KNN algorithms to alleviate the overfitting challenge stemming from the straightforward input features and extensive dataset size. In comparison to traditional deep learning models, the K-K-CNN hybrid model presents notable benefits in predicting flexibility for complex thin-walled parts at any given position, which affirms its robustness and accuracy. The proposed prediction method for flexibility can provide high-quality data-driven information for monitoring the elastic deformation of thin-walled parts. [ABSTRACT FROM AUTHOR]

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades. However, few papers systematically review these researches. By analyzing existing literature, this paper summarizes the process of fixture layout optimization and the methods applied. The process of optimization is made up of optimization objective setting, assembly variation/deformation modeling, and fixture layout optimization. This paper makes a review of the fixture layout for thin-walled parts according to these three steps. First, two different kinds of optimization objectives are introduced. Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives. Then, modeling methods for assembly variation and deformation are divided into two categories: Mechanism-based and data-based methods. Several common methods are discussed respectively. After that, optimization algorithms are reviewed systematically. There are two kinds of optimization algorithms: Traditional nonlinear programming and heuristic algorithms. Finally, discussions on the current situation are provided. The research direction of fixture layout optimization in the future is discussed from three aspects: Objective setting, improving modeling accuracy and optimization algorithms. Also, a new research point for fixture layout optimization is discussed. This paper systematically reviews the research on fixture layout optimization for thin-walled parts, and provides a reference for future research in this field. [ABSTRACT FROM AUTHOR]

Ti-6Al-4 V titanium alloy has high strength, low density, good heat resistance, high fatigue life and excellent corrosion resistance. It is a preferred material for important parts such as aeroengine fans, compressors, roulette and blades. However, thin-walled titanium alloy parts are easy to deform in the milling process, which affects the machining accuracy. It is necessary to propose appropriate milling parameters. Using finite element software, the stress of thin-walled parts in the milling process was simulated, and the transient stress and the stress rules were analyzed. To predict the stress deformation effects of mutual coupling multiple process parameters during milling thin-walled titanium alloy parts, a set of orthogonal simulation experiments were carried out. The actual milling experiment of thin-walled Ti-6Al-4 V parts was conducted by optimal process parameters. The surface roughnesses in the bottom and the side are Ra 437 nm and Ra 738 nm respectively. [ABSTRACT FROM AUTHOR]

Thin-walled parts such as blades are widely used in aerospace field, and their machining quality directly affects the service performance of core components. Due to obvious time-varying nonlinear effect and complex machining system, it is a great challenge to realize accurate and fast prediction of machining errors of such parts. To solve the above problems, an engineering knowledge based sparse Bayesian learning approach is proposed to realize in-situ prediction of machining errors of thin-walled blades. Firstly, an engineering knowledge based strategy is proposed to improve the generalization ability of the model by integrating multi-source engineering knowledge, including machining information, physical information and online monitoring information. Then, principal component analysis method is utilized for the dimensional reduction of features. Sparse Bayesian learning approach is developed for model training, which significantly reduce the complexity of the regression model. Finally, the superiority and effectiveness of the proposed approach have been proven in blade milling experiments. Experimental results show that the average deviation of the proposed in-situ prediction model is about 11 μm, and the model complexity is reduced by 66%. [ABSTRACT FROM AUTHOR]

In recent years, additive-subtractive hybrid manufacturing (ASHM) technology has become a research hotspot. To realize high-precision manufacturing of complex parts and avoid machining interference, alternating additive manufacturing (AM) and subtractive manufacturing (SM) are valuable to be adopted. In this paper, a finite element numerical model was established to simulate the temperature history, stress distribution, and deformation trend of the thin-walled parts alternately fabricated by AM and milling process. Then, 316L stainless steel thin-walled samples were built by the alternating ASHM process. The molten pool temperature, the surface residual stress, and the surface contour of samples were measured and analyzed. The results show that high tensile stress is exhibited at the junction of the AM segment with the substrate or the previous SM segment. With the increase of deposition height, the tensile stress decreases first and then increases. After the subsequent milling, the surface residual stress level shows a decreasing trend, which is because the milling-induced compressive stress offsets the initial residual tensile stress. Moreover, the deformation of the two ends and the top of the AM segments is more significant than that of the bottom. After SM, the deformation in the top area of the SM segments is still slightly more extensive than the bottom area. The subsequent AM process results in a small increase in the residual stress of the milled segment, but had little effect on its surface deformation. Finally, the repeated cutting height between two segments was studied. Compared with the previous work, this strategy considers the effect of the alternating ASHM process on the residual stress and deformation of thin-walled parts. This study has certain guiding significance for the manufacture of thin-walled parts such as turbine blades and parts with internal structure by alternating ASHM process. [ABSTRACT FROM AUTHOR]

Thin-walled parts refer to lightweight structural parts comprised of thin plates and stiffeners. During the machining process of thin-walled parts, machining distortion often occurs due to uncertain factors such as varying stiffness, cutting force, cutting temperature, residual stress, and other factors. This paper studied the minimization of the failure probability of machining distortion by controlling the uncertainties of inputs. For this, a fuzzy inference model for the machining system was proposed to determine the effects of uncertain factors on the machining distortion errors, which was composed of rule frame and result frame. In the rule frame, machining parameters, outline size, and wall thickness were used as inputs. In the result frame, linear stiffness, cutter path, as well as cutting force were taken as the input parameters. The values of machining distortion were the output, taken into a threshold function. Comprehensive matching was defined to measure the importance of uncertain inputs to outputs. Machining distortion will exceed the specification (failure) with the increase in comprehensive matching. Therefore, the comprehensive matching index evaluates the effects of the uncertainties on the machining distortion and quantifies the effects of given uncertain parameters. Two engineering examples were employed to illustrate the accuracy and efficiency of the proposed approach. It revealed that the comprehensive matching of cutting force to the failure probability of machining distortion was the maximum, 0.040, which was 12 to 13 times greater than that of linear stiffness or cutter path. [ABSTRACT FROM AUTHOR]

Thin-walled parts (TWPs) are widely used in aerospace, and their service performance is significantly affected by the machining accuracy. Multi-pass machining is used to machine the poor stiffness TWPs. However, it is difficult to accurately predict the final machining accuracy due to surface topography error propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWPs based on dynamic factors (cutting force and stiffness). First, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Second, a position-pass-dependent stiffness (PPDS) model is established considering the position dependence of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on a genetic algorithm–back propagation (GA-BP) neural network is proposed. Experiments under various conditions are carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by revealing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameter optimization and machining accuracy improvement in TWP machining. [ABSTRACT FROM AUTHOR]

Thin-walled parts are widely used in the aerospace industry, where the milling deformation of the parts caused by their thin-walled draping and extremely large size ratio characteristics as well as high material removal rate has been the most common quality issue that greatly affects the assembly performance and operation safety of aerospace equipment. Hence, effective management of milling deformation for a thin-walled part will significantly improve its quality, which, however, is still made difficult by the lack of a real-time deformation perception, optimization, and control method. To bridge this gap, a novel online optimal control method of milling deformation for thin-walled parts is proposed by incorporating digital twins into the milling process of thin-walled parts. To this end, a reference framework of the milling process digital twin (MPDT) for thin-walled parts are designed, where the autonomous operation logic of MPDT for online optimal control of milling deformation is further clarified. On that basis, three key enabling technologies of MPDT are introduced from the perspective of multidimensional high-fidelity MPDT modeling, knowledge-driven low-latency milling deformation simulation, and online optimal control of milling deformation, which provide an insight into the industrial implementation of MPDT. Eventually, a MPDT prototype system is implemented, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Residual compressive stress can effectively improve fatigue the life of aerospace thin-walled parts. In this study, residual compressive stress control is taken as the target. Firstly, a surface residual stress prediction model is proposed, which considers both machining parameters and milling force heat. The model of the relationship between milling force, thermal load, and residual stress is established, which quantifies the effects of mechanical and thermal loads on the formation of residual compressive stresses. The results show that the feed rate of each tooth and the cutting-edge radius play an important role in the residual compressive stress of the milling surface. The prediction models of surface residual stress for thermal load, mechanical load, feed per tooth, and radius are established. Secondly, the ratio α of the feed rate per tooth fz to the cutting-edge radius r is quantified. When αxx = 0.42–0.65 and αyy = 0.36–0.7, the surface residual compressive stress in the x and y directions of the workpiece reaches the maximum value. Thus, the ratio of the feed rate per tooth fz to the cutting-edge radius r is optimized to control the mechanical and thermal load quantization. It realizes active control of residual compressive stress on the workpiece surface. [ABSTRACT FROM AUTHOR]

In the milling process, it is easy to produce chatter due to the low rigidity of the thin-walled structure, which leads to the deterioration of workpiece surface quality and reduces the service life of cutting tools and machine tools. Therefore, a new chatter detection method for thin-walled parts based on optimal variational mode decomposition (OVMD) and refined composite multi-scale dispersion entropy (RCMDE) is proposed in this paper. Firstly, to solve the problem that the decomposition effect of the variational mode decomposition (VMD) algorithm is greatly affected by its parameter, a genetic algorithm (GA) is used to iteratively optimize the parameter of the VMD algorithm, and a new index, square envelope spectral correlated kurtosis (SE-SCK), is introduced as the fitness function of the genetic algorithm. Then, the energy ratio of the decomposed signal is calculated as the principle of selecting sub-components, and the sub-components with rich chatter information are selected for signal reconstruction. To solve the problem that the multi-scale dispersion entropy (MDE) will miss some information in the multi-scale process, RCMDE is introduced to detect milling chatter. Finally, the experiment of the variable cutting depth in side milling of titanium alloy thin-walled parts is carried out. The experimental results show that the OVMD algorithm proposed can solve the problem of difficult separation of chatter frequency bands caused by mode aliasing and lay a foundation for subsequent chatter feature extraction. RCMDE is more conducive to chatter detection than the single-scale DE when the scale factor is 4. The distinguishing effect of RCMDE on the machining state is more than 50% higher than that of MDE when the scale factor is 4. [ABSTRACT FROM AUTHOR]

Thin-walled parts are typically difficult-to-cut components due to the complex dynamics in cutting process. The dynamics is variant for part during machining, but invariant for machine tool. The variation of the relative dynamics results in the difference of cutting stage division and cutting parameter selection. This paper develops a novel method for whole cutting process optimization based on the relative varying dynamic characteristic of machining system. A new strategy to distinguish cutting stages depending on the dominated dynamics during machining process is proposed, and a thickness-dependent model to predict the dynamics of part is developed. Optimal cutting parameters change with stages, which can be divided by the critical thickness of part. Based on the dynamics comparison between machine tool and thickness-varying part, the critical thicknesses are predicted by an iterative algorithm. The proposed method is validated by the machining of three benchmarks. Good agreements have been obtained between prediction and experimental results in terms of stages identification, meanwhile, the optimized parameters perform well during the whole cutting process. [ABSTRACT FROM AUTHOR]

The machining of large thin-walled rings is considered. The influence of the cutter geometry and the cutting conditions on the machining precision is analyzed by the finite-element method. Production experiments are compared with the theoretical analysis. Practical recommendations are formulated. [ABSTRACT FROM AUTHOR]

The influence of rotary-cutting parameters on the surface roughness, residual stress, and surface microhardness of the workpiece is investigated. Increase in the cutting depth and the supply is found to increase the surface roughness, residual stress, and surface microhardness. The influence of the other cutting parameters on the surface quality of the workpiece is mainly associated with the kinematic and dynamic features of machining. The quality of machining may be regulated by adjusting the cutting parameters. [ABSTRACT FROM AUTHOR]

Machining deformation compensation technology based on on-machine measurement has been widely used in the field of thin-walled part machining. However, few research has been conducted on sampling methods for the measurement of thin-walled parts. In this study, we considered the influence of machining deformation in thin-walled regions, established a machining deformation prediction model (MDPM) based on the finite element method (FEM), and applied it to the sampling optimization process. Furthermore, we proposed an adaptive sampling method based on the maximum corresponding point deviation (MCPD) at the measurement point interval of the non-uniform rational B-spline (NURBS) curve. The proposed method was compared with three commonly used sampling methods (uniform sampling, curvature-based sampling, and maximum deviation-based sampling). Sampling experiments were performed with one NURBS curve and two machined thin-walled parts. The experimental results show that the proposed method is superior to the three commonly used sampling strategies in terms of reconstruction accuracy, sampling efficiency, and result stability. [ABSTRACT FROM AUTHOR]

Thin-walled structural parts, such as aeroengine casings, impellers, blades, and disks, are widely used in the aerospace industry due to their outstanding performance. A more efficient and accurate prediction approach for machining distortion has also been widely considered. The traditional finite element analysis approach, which is commonly used in the industry, has some limitations in both efficiency and accuracy. To resolve the above problems, a hybrid surrogate model considering uncertainties is proposed to estimate the machining distortion of thin-walled parts. In terms of efficiency improvement, a hybrid three-layer gradient surrogate model to integrate the response surface regression model and the Gaussian process regression model is introduced to replace the multi-physical finite element analysis model. In terms of accuracy improvement, the calibration and quantification of uncertainties within the model are carried out. The uncertainties are divided into four parts, including cutting contact area uncertainty, tool wear uncertainty, mechanism modelling uncertainty, and unquantified uncertainty, which are determined through an iterative solving algorithm, an experimental calibration approach, and Bayesian inference simultaneously. An aeroengine combustor casing is selected as a case study to validate the effectiveness of the proposed estimation model. The predicted results indicate that the estimation time is less than 24 ms , and the average estimation deviation is 3.1400 μ m . Compared with the traditional finite element analysis model, the proposed methodology can significantly reduce the estimation time with higher accuracy. [ABSTRACT FROM AUTHOR]

Both modal shape and tool orientation affect dynamic responses of thin-walled parts. However, the influences of these two aspects on dynamic responses have not been studied simultaneously. This paper studies the influences of modal shape and tool orientation on evolution of dynamic responses during 5-axis ball-end milling of thin-walled parts. Firstly, a modal shape-based model is established to calculate the dynamic response of the workpiece, the model considers forced and regenerative effects, and it is related to the tool orientation and cutting position. Next, the acceleration signal of workpiece during machining process and the roughness of machined surface are measured. The wavelet transform is performed on the acceleration signal to identify the state of the machining process. The test results show that the proposed model can predict the trend of dynamic response with the change of the tool orientation and can predict the stability of milling processes, the dominant vibration modes of workpiece and its conversion boundary. Moreover, the interpretations are given to the effects of modal shape and tool orientation on the evolution of dominant vibration modes and dynamic response. This research can provide guidelines for optimizing tool orientations. [ABSTRACT FROM AUTHOR]

Due to the unique low rigidity of thin-walled parts, significant machining deformation may occur in the milling process. A deformation prediction model is presented in this paper while considering the effect of the removed material on the global stiffness matrix. Aiming at reducing the scale of the global stiffness matrix, the super-element method is firstly used and the scale of the stiffness matrix is significantly reduced about 30%. And then, the stiffness matrix of the in-process workpiece (IPW) is directly obtained by eliminating the contribution of the removed nodes in the global stiffness matrix. This method can avoid re-building the geometric model or re-meshing the finite element (FE) model in the machining process. To improve the inverse calculation efficiency of the stiffness matrix, a novel nodes re-sorting method is proposed based on the calculation order of the matrix blocks in LU decomposition and inverse calculation. Furthermore, the optimal stiffness direction is discussed and applying the cutting force in this direction can minimize the machining deformation. The simulation results verified the existence of the optimal stiffness direction. Finally, the simulation and experiment are carried out to validate the accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

Thin-walled parts has been extensively used in the field of aerospace, refrigeration and electronic equipment for its characteristics of lightweight, material-saving and compact-structure. Selective laser melting (SLM) technology has been regarded as an effective way to manufacture parts with complex structures due to its high flexibility and efficiency. In this work, tungsten thin-walled parts are manufactured at different laser energy densities, and the finite element modelling of manufacturing process is combined with densification, dimensional accuracy and surface roughness measurement to optimize the SLM process parameters of tungsten thin-walled parts. The results indicated that the transient temperature peak is higher at the top position than at the bottom during the manufacturing process of thin-walled parts, which is also the same in the roughness. With the increase of laser energy input, the temperature of the molten pool is improved, which leads to an increment in the wall thickness. A sample with a maximum relative density of 84.5% was obtained at the optimal energy density about 1000 J/mm and its surface morphology exhibits fewer pores and cracks. The sample prepared at the laser energy input of 800 J/m exhibits the superior property of microhardness with the value of 504.3HV0.2. All these results are significant in the manufacture of thin-walled parts with high performances. [ABSTRACT FROM AUTHOR]

Thin-walled parts are widely applied in the automotive and aerospace industry for their superior properties. However, severe machining error may occur due to their low rigidity under the effects of multiple error sources in the machining process. Solutions based on mechanism analysis and finite element method have been developed while most of them are not robust under the complex machining conditions. Aiming to solve this problem, a comprehensive error compensation method that includes three major error sources, which are geometric error, thermal error, and force-induced error, is proposed. The geometric error and thermal-induced error of the machining center are firstly modeled and compensated to provide a high precision movement system for the on-machine measurement inspection. The force-induced error model is then established based on the probing data. Finally, the comprehensive error model is obtained through the transformation of the coordinate systems. Besides, a real-time compensation system is developed based on the specific functions of the NC system. To validate the proposed method, two sets of compensation cases are conducted, the objects of which are a thin web workpiece and a valve body part, respectively. The experiment results reveal that the machining errors of both experiment sets are decreased by more than 60.7% and the machining productivity is improved by more than 41.9%. [ABSTRACT FROM AUTHOR]

Chatter is a very common phenomenon in the milling process. The occurrence of chatter will cause chatter marks on the processed surface, cause the tool to jump, and even bring sharp and harsh noise, making the processed surface unable to meet the accuracy requirements. Thin-walled parts are more prone to chatter due to their lower stiffness. In order to avoid the occurrence of chatter, this paper adopts the method of combining theoretical analysis with milling experiments to conduct in-depth research on the prediction of chatter. Considering the first-order and second-order modal parameters of the tool and thin-walled part as well as the specific processing parameters, based on the milling dynamics model, a semi-discretization method based on the improved Runge-Kutta method (IRKM-SDM) is used to draw the chatter stability lobe diagram. Through simulation and experiments, it is proved that in terms of simulation accuracy and calculation speed, compared with the zero-order analysis (ZOA), multi-frequency solution (MFS), and the traditional semi-discretization method (SDM), the stability lobe diagram obtained by the IRKM-SDM is more advantageous. This study is of great significance for optimizing cutting parameters and suppressing chatter in the actual machining process. [ABSTRACT FROM AUTHOR]

The system dynamic model is essential for chatter prediction in milling of thin-walled structures, which is largely affected by the estimation precision of dynamic modal parameters. This paper presents a method of dynamic modal parameter identification from response signals through operational modal analysis (OMA). To avoid omission of the modes and improve recognition accuracy, multichannel least square complex exponential (LSCE) method and multichannel autoregressive moving average (ARMA) method are applied to identify the modal parameters by processing multiple sets of response signals simultaneously. The convergence characteristics of the damping stability diagram at different natural frequencies are employed to eliminate the false modes caused by harmonics and model order. After that, the predicted stable boundaries of the milling system are estimated by an extended semi-discretization method, which incorporates the effects of multi-modes, multi-point contact, and regeneration chatter caused by the interaction between tool and thin-walled part. Through the milling experiment validation, it is shown that the dynamic modal parameters can be identified accurately, and chatter can be well predicted. [ABSTRACT FROM AUTHOR]

Weak rigid parts are widely used in aerospace industry. These parts are characterized by thin walls. Most of these parts have the meter-level size and mesh surface, thus have a requirement for tool accessibility. Therefore, the end milling is an applicable process to reduce the thickness of these parts. Due to the weak rigidity of these parts in the normal direction, severe chatter will occur in the machining process, which will reduce the machining efficiency and spoil the machining surface, so the processing stability of the thin-walled part needs to be studied. In this paper, the dynamic model for end milling of thin-walled parts is presented. The regeneration effect in the surface normal direction is proposed, and the dynamic equation is constructed considering the vibration of the workpiece in the cutting tool axial direction. The stability lobe diagram is obtained by numerical method. It is found that the stability of weak rigid parts end milling is related to the cutting speed, the feed rate, and the width of cut, but not to the cutting axial depth. Finally, cutting experiments are conducted. The experimental result is coincident well with the prediction, thus verifying the proposed dynamic model and the stability analysis method. [ABSTRACT FROM AUTHOR]

Micro-scale thin-walled parts have the characteristics of small size and low rigidity, so chatter is very easy to occur in high speed micro-milling, which influences machining precision and surface quality of the parts. To solve this problem, the authors first establish micro-milling force models in micro-milling thin-walled parts that consider the elastic deflection of both thin-walled part and micro-milling tool. According to the Lagrange's equation, the dynamic characteristics of thin-walled part that vary with the cutting position of the tool are obtained combined with the Rayleigh-Ritz method. The relative transfer function between the micro-milling tool and the thin-walled part is achieved through the relationship between cutting force and vibration vector and then the dynamic characteristics of the system are obtained. Finally, the stability lobe diagram is drawn through time-domain simulation and verified by micro-milling experiments. The comparison results show that the prediction results of the stability lobe diagram are consistent with the experimental results. The research is a meaningful exploration of the mechanism of micro-milling thin-walled parts and provides a basis for the selection of cutting parameters for stable cutting. [ABSTRACT FROM AUTHOR]

This paper mainly analyzes and experiments the processing technology of high speed (12000 r/min) and large proportion thin-wall parts (thickness/width = 1:80 and thickness/height = 1:120), so as to guide the machining of closed centrifugal impellers in aerospace. In this process, low melting point alloy is selected as the filling material to increase the rigidity of thin-walled parts and improve the indicated roughness of parts. Different low melting point alloy filling structures are carried out on large proportion of thin-wall sample parts. In addition, an auxiliary angle R is added at the stress concentration of the part itself to reduce the vibration caused by the machining path. For the results of simulation and test, the parts without filling low melting point alloy can not be processed and used at all, while the "tower" filling structure has fine cracks at the top of the thin-walled parts. The "column" filling structure increases the rigidity by about 19.97 % and the roughness by about 50 %. [ABSTRACT FROM AUTHOR]

In the current study, the experiments are conducted to firstly measure the machining deformation change regulations of two typical thin-walled parts during 1–720 h after machining. Results demonstrate that the deformations of the specimens evidently change in 1–72 h after machining due to the residual stress relaxation. The specimens reach a relatively stable state at 72 h and deformed slowly in 72–720 h. Subsequently, a 3-step machining process was proposed to control the final deformation of two groups of thin-walled specimens. The process was shown as follows: (1) fabricating the workpiece with the extra stiffening ribs in the first-time machining; (2) placing the workpiece freely for 72 h or conducting stress relief process; and (3) machining the reserved stiffening ribs in the second-time machining. Compared with the conventional machining method, the final maximum machining deformations of specimens in group 1 and 2 manufactured by the 3-step machining process are reduced by 29.68% and 48.09%, respectively. Eventually, a machining deformation control method of thin-walled parts based on enhancing the equivalent bending stiffness was summarized and proposed. [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]

Local surface plastic deformation may potentially be used for precision shaping of components characterized by low and nonuniform rigidity in the control systems of airplane engines. Analysis of options for the control of technological shape inheritance gives rise to methods of effective deformation. By simulating the stress–strain state of the surface layer, a method is proposed for specifying the local deformation conditions so as ensure shape precision of the part's working surfaces. [ABSTRACT FROM AUTHOR]

Significant machining errors may occur in thin-walled parts during the milling process due to their low rigidity. Conventionally, deformation prediction is conducted via the finite element method (FEM) due to its convenience and accuracy. However, it is extensively time-consuming because of the huge computation amount, which is caused by repeated computation of the displacements of all the nodes at each cutting location and iteration process. To solve this problem, a rapid deformation computation method based on stiffness matrix reduction was proposed. Furthermore, an iterative cutting force-induced error prediction model was established considering the tool-workpiece dynamic interaction, which shortened each cutting force-induced error prediction time from tens of seconds by traditional FEM to dozens of milliseconds. Lastly, the proposed method was applied to the compensation of thin-walled aluminum alloy blocks. The compensation experiment results revealed that the proposed model reduced the machining error by more than 53.6% with high real-time performance. This method demonstrates immense potential for further applications in deformation prediction of thin-walled freeform surface parts. [ABSTRACT FROM AUTHOR]

Chatter is a self-excited vibration that frequently occurs in thin-walled parts milling, which has become a major limitation to productivity and quality. Additionally, convolutional neural network (CNN) has been widely used in detection and classification, but the accuracy and convergence are affected by the initial weight and hyperparameters. Therefore, based on CNN, a method of chatter detection for the milling of thin-walled parts is proposed, which is realized by recognizing the image of the machined surface. First, aiming at the challenges of neural networks in which the weight is randomly initialized, a weight initialization method is proposed based on an improved magnetic bacteria optimization algorithm. The optimal magnetosome of each generation is used to adjust the magnetic moment of the next generation so that the population approaches the optimal solution direction to search for the global optimal value. Second, an improved genetic algorithm is proposed to optimize the network structure and improve the optimization efficiency of hyperparameters. The tabu list and the hill climbing algorithm are introduced into the improved genetic algorithm to avoid repeatedly counting the fitness of the same point and solving the oscillation problem near the optimal solution. The experimental results show that the accuracy, the value of the Matthew correlation coefficient, and the F1 Score value of proposed CNN are 98.3%, 95.5%, and 98.8%, respectively. Compared with other algorithms, the proposed method, which has outstanding recognition performance, is competent in contactless chatter detection. [ABSTRACT FROM AUTHOR]

Chatter is a kind of undesired vibration with multiple adverse effects in machining operations, and online detection of chatter is crucial for chatter avoidance or suppression. However, it is observed that time-varying harmonic components are abundant in turning of thin-walled parts. The emergence of harmonics alters conventional frequency distribution patterns of the measured signal. In particular, it is a very challenging task to detect chatter in an early stage when the signal spectrum is dominated by the harmonics. This paper firstly investigates theoretically the relationship between the chatter frequency and the natural frequency based on a well-accepted chatter model of turning. The chatter frequency is found to vary far more slowly than the natural frequency during the thin-walled turning. Based on this finding, the adaptive signal predictor is proposed as a preprocessor for chatter detection, which can alleviate harmonics interference and noise with no prior knowledge of the workpiece dynamics. Furthermore, an improved adaptive filter algorithm is developed to enhance the performance of time-varying harmonics removal. Finally, simulation and experimental results validate the effectiveness of the proposed approach in harmonics removal and noise reduction for chatter detection in thin-walled turning. [ABSTRACT FROM AUTHOR]

This paper presents the development and application of fuzzy logic in the milling of thin-walled parts for the purpose of analyzing surface roughness. Surface roughness is an important performance indicator of finished components. Depending on conditions such as feed ratio and wall thickness, different machining strategies can be applied. The objective was to analyze and determine the influence of the machining conditions on surface roughness. The model for analyzing and determining surface roughness of the aluminum alloy AL 7075 was trained (design rules) and compared by using the experimental data. The average deviation of the compared data for surface roughness was 12.3%. The effect of the feed ratio, wall thickness and machining strategy as well as their interactions in machining are thoroughly analyzed and presented in this study. [ABSTRACT FROM AUTHOR]

Applications of thin-walled parts are substantially affected by residual stress generated during the cutting process. However, in multi-process milling, the redistribution mechanisms of residual stress after roughing, semi-finishing, finishing, and heat treatment are highly complex and remain underexplored. As a result, the control of deformation of multi-process thin-walled parts remains challenging. In this study, to reduce the influence of residual stress on the deformation of complex thin-walled parts, simulation models of the machining process and heat treatment are carried out based on multi-process analysis. By comparing different process parameters, the redistribution mechanism of residual stress during multi-process milling is analyzed. The findings reveal that with a stress-relief treatment between every process, especially before finishing machining, compressive stress on the surface of thin-walled parts is greatly reduced; residual stress on the sub-surface enters a stable state quickly; and the internal metal structure and structural performance are more reliable. Experiments are conducted to verify these results by comparing residual stress. Stress-relief treatment after every cutting process is ultimately recommended for controlling residual stress and deformation of thin-walled parts in multi-process machining. [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]

Chatter occurs more frequently due to the lower stiffness of the thin-walled parts, which may exert damaging effect on the machined surface of workpiece. To avoid chatter and predict the stable zone more precisely, a relative transfer function was introduced to consider dynamic properties of both milling tool and workpiece, and an improved multi-frequency solution was employed to predict the critical cutting depth in axial direction. Verified by a cutting test and time domain simulation, improved multi-frequency solution had been proven to be more accurate than zero-order analysis. The proposal of improved multi-frequency solution is important for the chatter suppression techniques to improve the processing efficiency and quality in the aerospace industry. [ABSTRACT FROM AUTHOR]

Mirror milling has been regarded as an effective way for large monolithic thin-walled parts machining. However, the supporting force not only improves the stiffness of the cutting point but also affects the dynamic behavior of the machining system. Essentially, the influence mechanism of supporting force on the mirror milling stability should be analyzed. Firstly, a 3-DOF dynamic system model has been developed considering the supporting head-workpiece interaction during mirror milling. And then, the evolution of modal parameters of thin-walled parts under different supporting forces is investigated with a series of impulse harmer tests, which will supply the dynamic parameters for the mirror milling stability lobes prediction using the full-discretization method. On this basis, the optimum supporting force can be determined according to the relationship of the limited cutting depth and the supporting force. At last, the analysis resulted was verified with a series of mirror milling slot test. From the comparison, the optimum supporting force could maintain the stable mirror milling and avoid excess deformation simultaneously. [ABSTRACT FROM AUTHOR]

Because of the low rigidity of thin-walled parts, the cutting vibration is commonly encountered and has a vital influence on machined surface quality. Theoretical simulation of surface topography is one of the main methods to evaluate and control surface quality in practice. In this paper, a simulation model of surface topography is developed based on cutting edge motion model that incorporates the dynamics of thin-walled parts milling system. The theoretical model used to describe the trajectory of cutting edge takes tool vibration and workpiece vibration into account. Then, the influence of system vibration on surface topography is investigated. Particularly, a new method is proposed in this paper to predict the texture interval, texture distribution, and residual height for different milling areas by identifying the dynamic characteristics of thin-walled parts. In addition, the validity of surface topography model is conducted by experiment. The results show that the simulated topography is consistent with the experimental topography, and the model is proved to be able to predict roughness accurately. [ABSTRACT FROM AUTHOR]

Due to its relatively high gravity material removal, the thin-walled part machining would go through a complex process, from stable to unstable and/or reverse repeatedly. As a result, the monitored signals generally exhibit full-oscillatory behaviors, which require that the chatter indicators should meet the dynamic conditions. However, the conventional indicators, including time domain indicators and time-frequency domain indicators, could only capture the state mutation point in the continuous process. In this paper, a novel chatter indicator, Q-factors, is proposed for chatter detection. The relationship between Q-factor and signal oscillatory behavior is illustrated from the perspective of signal’s frequency characteristics and tool-workpiece system’s response. Chatter indicator’s identification ability for thin-walled part flank and mirror milling is analyzed, i.e., its ability to express characteristics of machining state, sensibility to change machining state, and its chatter-related information inclusion. It can be indicated that as a multi-dimensional indicator, Q-factor can be used to identify chatter-related signal component and quantify the level of chatter simultaneously. The value of Q-factor exhibits obvious difference between stable state and chatter state. The obvious mutation at the place where the machining state changes will supply more useful and effective information for the following chatter prediction and suppression before the chatter is completely developed. [ABSTRACT FROM AUTHOR]

Mirror milling is an effective approach to improve large-scale monolithic thin-walled parts machining quality through ensuring the mirror relations of cutter and supporting head. However, the introduction of supporting head influences the dynamic characteristics of the tool-workpiece system. Essentially, the measured raw signal contains more coupled components and shows more oscillatory and aperiodic behaviors. Therefore, it is difficult to identify the mirror milling chatter using the monitoring signals. Comparing with traditional indicators, Q-factor can be used to describe the machining state in the view of signal oscillatory behavior, which is suitable for chatter-related component extraction in thin-walled part machining. In this paper, chatter-related signal component identification and diagnosis of thin-walled parts based on signal Q-factor and support vector machine (SVM) is proposed. The frequency band with maximal variation of Q-factors is taken as the chatter-related signal component. Using the feature vector constructed by Q-factors and power spectrum values of the determined frequency band, the SVM is used for milling state diagnosis. The prediction accuracy is much higher than the other frequency band and traditional indicators. It indicates the effectiveness of the proposed mirror machining chatter identification method. [ABSTRACT FROM AUTHOR]

Residual stress on the machined surface and the subsurface is known to influence the distortion of thin-walled parts. Therefore, it is essential to predict the distribution of surface residual stress accurately. In this paper, the coupled distribution law of initial residual stress and machining-induced residual stress is investigated. Firstly, a model with initial residual stress is established and incorporated into thermal mechanical coupled finite element model of 2-D cutting. Then, a tensile fixture is designed to impose initial stress into a thin-walled part of Al-6061, and cutting experiments are carried out. The residual stress distribution is measured by X-ray diffraction/electropolishing method. The results of experiments and simulation show that in the plastic deformation zone, the initial residual stress has no significant influence on the distribution of the machining-induced residual stress. In the elastic deformation zone, the stress that linear grows along depth from zero to initial residual stress is superimposed on machining-induced residual stress. The mathematical model of stress coupling distribution on the surface of thin-walled parts is established by numerical method. Finally, it is found that the effect of coupled stress distribution on distortion is more significant with the decrease of thickness (from 3 to 0.5 mm) of the thin-walled parts. [ABSTRACT FROM AUTHOR]

Due to its low rigidity and poor stiffness, machining deformation in numerical control milling is a big obstacle to achieve accurate shape of thin-walled honeycomb-core sandwich structural parts. In this paper, the factors which affect the machining deformation of these structural parts are analyzed at first. Then, a finite element analysis model is established, which considers the affecting factors of workpiece original stress, fixture constraints and cutting forces, to simulate the magnitude and distribution of the machining deformation. Afterwards, a prediction model is developed by using back propagation neural network to build the relationship between the deformation and machining parameters. Finally, the milling and measurement experiments are performed to validate the proposed prediction model. By comparing the experimental result with the simulation result and the prediction result, it is found that the experimental result was 7.2 and 2.2% less than the simulation result and the prediction result, respectively. Thus, a conclusion is drawn that the machining deformation prediction methodology is feasible. [ABSTRACT FROM AUTHOR]

A novel micro-flank milling force prediction model is developed in this study by considering the deflection of tool and workpiece, tool run-out, and material strengthening effects during the flank milling of thin-walled parts. The model of the cutting tool applied in this study is closer to the actual structure and its deflection model is established based on Euler Bernoulli cantilever beam theory under mesoscale. The values of the workpiece deflection are obtained with an online Keyence LK-H020 laser sensor. The Johnson-Cook constitutive model is adopted to estimate the flow stress sJC, which takes in consideration the effects that strain-hardening, strain-rate, and thermal softening have on the flow stress. The mechanistic model is validated by a series of micro-thin-wall experiments with a two-flute KENNA micro-milling cutter as tool and Ti-6Al-4V titanium alloy as workpiece material. Experimental results illustrate that the proposed model performs well for the micro-flank milling forces in the x-direction, with an average error of 4.153%, while the error in the ydirection is slightly larger at 4.458%. [ABSTRACT FROM AUTHOR]

This paper proposed a condition monitoring system using least square fitting method and lifting wavelet transform (LWT) to realize online monitoring of the thin-walled parts' deformation. Firstly, the overall deformation matrix under LWT method is established. Then, the force signals were transformed by LWT, which were applied to the overall deformation matrix to monitor the machining deformation of the whole parts. The error between monitoring deformation and the actual deformation is within 10 μm through experiment, which verifies the accuracy and practicability of the developed monitoring method. [ABSTRACT FROM AUTHOR]

High-speed milling of thin-walled part is a widely used application for aerospace industry. The low rigidity components, large quantities of material removed in machining progress, are in the risk of the instability of the progress. In this paper, the thin-walled parts have the similar characteristics with the tools. Therefore, the dynamic model and the stability critical condition determined by the relative dynamic behavior between tool subsystem and workpiece subsystem are put forward. The thin-walled parts' dynamic character varies greatly with time when machining. The whole workpiece has been divided into several stages by finite element analysis (FEA) so that its various modal parameters in the milling progress can be obtained gradually; thus, the variation due to metal removal has been accurately taken into account. The stability critical condition is predicted by frequency domain method based on the dynamic behavior of the two subsystems. With the respect to time-varying critical stability condition, a three-dimensional lobe diagram has been developed to show the changing conditions of chatter. Finally, the proposed methods and models were proven by series milling experiments. [ABSTRACT FROM AUTHOR]

As the main factor, residual stress undoubtedly restricts the long-term stability of high-precision machining quality parts, arresting an extensive attention on studying its control method. Therefore, a model is meticulously proposed, along with the optimization method of the overlap rate of cutting tool path. In the roughing, the selection of a larger magnitude of overlap coefficient K is found to be necessary for the optimization of the surface machined residual stress, followed by an indispensable selection of smaller K values in the finishing. The shrinking overlap coefficient K echoes with the decreasing influence of the deformation affected by the residual stress distribution, inevitably accompanied by the lowering material removal rate. Based on all the optimization results, an aerospace thin-walled part is adopted in the application case to verify the proposed approach. [ABSTRACT FROM AUTHOR]

Thin-walled parts are important structural parts in the aerospace industry. Two common problems encountered in milling are workpiece deformation and tool wear, mainly due to the low rigidity of thin-walled structures and the poor machinability of difficult-to-cut materials. In this paper, we propose a novel online measuring and allowance compensation method that can be applied to thin-walled parts to improve machining accuracy. Automatic on-machine inspection is used to measure a workpiece after rough/semi-finishing without transferring and reclamping the workpiece. Then, a compensation value is calculated to adaptively adjust the depth of cutting during finishing. The method does not require complex cutting force modeling or nonlinear calculation of workpiece-tool deflection and avoids the uneconomical monitoring of the cutting force, power, and torque of the machine and cutting tools and can be extended to engineering applications easily. Its effectiveness and adaptability is demonstrated by an orthogonal experiment in which the structure, cutting depth, and material are considered. [ABSTRACT FROM AUTHOR]