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Aircraft parts are often designed as thin-walled structures to reduce the weight of the aircraft. However, thin-walled structures are prone to deformation during the milling process, leading to a decrease in machining accuracy. To address this problem, a method combining milling and electrochemical machining, known as electrolytic milling, is proposed to minimize deformation and enhance the accuracy of thin-walled parts. The factors that affect the machining accuracy of electrolytic milling were analysed, and a model for predicting the thickness of the material removed was established. Electrolytic milling experiments were designed, and the model was solved. Based on the built model, the influences of feed rate, spindle speed, radial cutting depth, voltage and electrolyte concentration on material removal thickness were investigated. Compared with the milling method, the electrolytic milling method can improve the machining accuracy of thin-walled parts. Electrolytic milling experiments were conducted on box-shaped parts to verify the reliability of the proposed method., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

Additive manufacturing technology overcomes the limitations imposed by traditional manufacturing techniques, such as fixtures, tools, and molds, thereby enabling a high degree of design freedom for parts and attracting significant attention. Combined with subtractive manufacturing technology, additive and subtractive hybrid manufacturing (ASHM) has the potential to enhance surface quality and machining accuracy. This paper proposes a method for simulating the additive and subtractive manufacturing process, enabling accurate deformation prediction during processing. The relationship between stress distribution and thermal stress deformation of thin-walled 316L stainless steel parts prepared by Laser Metal Deposition (LMD) was investigated using linear scanning with a laser displacement sensor and finite element simulation. The changes in stress and deformation of these thin-walled parts after milling were also examined. Firstly, 316L stainless steel box-shaped thin-walled parts were fabricated using additive manufacturing, and the profile information was measured using a Micro Laser Displacement Sensor. Then, finite element software was employed to simulate the stress and deformation of the box-shaped thin-walled part during the additive manufacturing process. The experiments mentioned were conducted to validate the finite element model. Finally, based on the simulation of the box-shaped part, a simulation prediction was made for the box-shaped thin-walled parts produced by two-stage additive and subtractive manufacturing. The results show that the deformation tendency of outward twisting and expanding occurs in the additive process to the box-shaped thin-walled part, and the deformation increases gradually with the increase of the height. Meanwhile, the milling process is significant for improving the surface quality and dimensional accuracy of the additive parts. The research process and results of the thesis have laid the foundation for further research on the influence of subtractive process parameters on the surface quality of 316L stainless steel additive parts and subsequent additive and subtractive hybrid manufacturing of complex parts.

When using selective laser sintering to print parts with thin-walled structures, the thermal action of the laser can cause thermal stresses that lead to plastic deformation, resulting in large warpage and dimensional deviations. To address this issue, this study proposes a bottom support method for selective laser sintering. The impact of lattice-type, concentric-type, and cross-type support structures with varying filling densities and thicknesses on the suppression of warpage and dimensional errors was investigated. The optimal process parameters for each support structure were then determined through optimization. The findings of this study demonstrated a reduction in Z -axis dimensional errors of the workpiece following the addition of supports. The reduction amounted to 33.809%, 86.160%, and 66.214%, respectively, compared to the original workpiece. Moreover, the corresponding warpage was reduced by 35.673%, 46.189%, and 46.059% for each respective case, showcasing an improvement in the printing precision. Therefore, the bottom support effectively reduces dimensional and shape errors in thin-walled parts printed by selective laser sintering. Specifically, the results obtained indicated that the concentric type of support is more effective in reducing dimensional errors and enhancing the shape accuracy of the printed workpiece. Conversely, the cross type of support demonstrated superior capabilities in minimizing the consumption of printing materials while still delivering satisfactory results. Thus, this study holds promise for contributing to the advancement of thin-walled part quality using selective laser sintering technology. This research can contribute to achieving greater accuracy in the fabrication of parts through 3D printing.

The process parameters chosen for high-performance machining in the milling of a thin-walled workpiece are determined by a stability prediction model, which needs accurate modal parameters of the machining system. However, the in-process modal parameters are different from the offline modal parameters and are difficult to precisely obtain due to material removal. To address this problem, an accurate time-dependent autoregressive moving average with an exogenous input (TARMAX) method is proposed for the identification of the modal parameters in the milling of a thin-walled workpiece. In this process, a TARMAX model considering external force excitation is constructed to characterize the actual condition in the milling of a thin-walled workpiece. Then, recursive method and sliding window recursive method are used to identify TARMAX model parameters under time-varying cutting conditions. Subsequently, a three-degree of freedom (3-DOF) time-varying structure numerical model under theoretical milling forces and white-noise excitation is established, and the computational results show that the predicted natural frequencies using the proposed method are in close agreement with the simulated values. Finally, several experiments are designed and carried out to validate the effectiveness of the proposed method. The experimental results show that the predicted accuracy of the proposed method using actual cutting forces is 95.68%. Good agreement has been drawn in the numerical simulation and machining experiments. Our further research objectives will focus on the prediction of the damping ratios, modal stiffness, and modal mass., Competing Interests: The authors declare no conflict of interest.

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.

The hybrid process integrates two or more different processes, such as additive and subtractive manufacturing, which have gained appreciable consideration in recent years. The deformation of hybrid manufacturing is an essential factor affecting machining quality. The purpose of this paper is to study the effect of milling on stress release and surface deformation of additive manufacturing (AM) specimens in the process of additive and subtractive hybrid manufacturing (ASHM) of 316L stainless steel thin-walled parts, so as to effectively improve the forming quality of thin-walled parts manufactured by the combined processing of ASHM. To this end, a series of experiments were carried out to study the relationship between stress distribution and thermal stress deformation of 316L stainless steel thin-walled parts prepared by LMD, and the changes of stress and deformation of these thin-walled parts after subsequent milling. An infrared camera and laser distance sensor were used to record the temperature field data and deformation data to analyze the influence factors of temperature and stress on the machining results. Then, the finite element software was used to simulate the stress and deformation of the thin-walled parts in the additive manufacturing process and the subsequent milling process. Meanwhile, the model was verified through the experiments. In addition, the relationship between the milling force and the milling parameters of the AM parts was studied by orthogonal test and regression analysis.

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

Low-rigidity thin-walled parts are components of many machines and devices, including high precision electric micro-machines used in control and tracking systems. Unfortunately, traditional machining methods used for machining such types of parts cause a significant reduction in efficiency and in many cases do not allow obtaining the required accuracy parameters. Moreover, they also fail to meet modern automation requirements and are uneconomical and inefficient. Therefore, the aim of provided studies was to investigate the dependency of cutting forces on cutting parameters and flank wear, as well as changes in cutting forces induced by changes in heating current density and machining parameters during the turning of thin-walled parts. The tests were carried out on a specially designed and constructed turning test stand for measuring cutting forces and temperature at specific cutting speed, feed rate, and depth of cut values. As part of the experiments, the effect of cutting parameters and flank wear on cutting forces, and the effect of heating current density and turning parameters on changes in cutting forces were analyzed. Moreover, the effect of cutting parameters (depth of cut, feed rate, and cutting speed) on temperature has been determined. Additionally, a system for controlling electro-contact heating and investigated the relationship between changes in cutting forces and machining time in the operations of turning micro-machine casings with and without the use of the control system was developed. The obtained results show that the application of an electro-contact heating control system allows to machine conical parts and semi-finished products at lower cutting forces and it leads to an increase in the deformation of the thin-walled casings caused by runout of the workpiece.

Deformation of thin-walled titanium alloys can occur during the milling process due to the cutting force and chatter vibration, which can influence the precision of the finished parts. In this research, a new milling method without auxiliary support for machining of thin-walled parts was proposed. A large cutting depth and layered milling technology were used during rough machining, with a different machining allowance for each subsequent remaining layer. In the finishing stage, the surface of the previous layer needed to be dressed before processing the next layer. A TiAlSiN-coated, cemented carbide milling cutter was used to machine titanium alloy thin-walled parts, which are characterized by continuous multilayers of unequal thickness. The processing path was simulated using HyperMILL software, and the machining accuracy was detected by the 3D optical scanner. The measurement results indicated that the surface contour accuracy of the parts was ±0.21 mm, within a range of ±0.30 mm. The machining efficiency was increased by 40%, while guaranteeing machining accuracy., Competing Interests: The authors declare no conflict of interest.