Search

Additive manufacturing encompasses technologies that produce three-dimensional computer-aided design (CAD) models through a layer-by-layer production process. Compared to traditional manufacturing methods, additive manufacturing technologies offer significant advantages in producing intricate components with minimal energy consumption, reduced raw material waste, and shortened production timelines. AM methods based on shielded gas welding have recently piqued the interest of researchers due to their high efficiency and cost-effectiveness in manufacturing critical components. However, one of the most formidable challenges in additive manufacturing methods based on shielded gas welding lies in the irregularity of weld bead height at different points, compromising the precision of components produced using these techniques. In this current research, we aimed to achieve uniform weld heights along the welding path by considering the most influential parameters on weld bead geometry and conducting experimental tests. Input parameters of the process, including nozzle angle, welding speed, wire speed, and voltage, were considered. Simultaneously, image processing and wavelet transform were employed to assess the uniformity of weld bead height. These parameters were applied to produce intricate parts after identifying optimal parameters that yielded the smoothest weld lines. According to the results, the appropriate bead for manufacturing the part was extracted. The results show that the smoothest bead line is achieved in 27 V as the highest level of voltage, at a 90° nozzle position and the maximum wire feed rate. Parts manufactured using this method across different layers exhibited no distortions, and the repeatability of production substantiated the high reliability of this approach for component manufacturing. [ABSTRACT FROM AUTHOR]

Due to their homogeneity and excellent ratio between load capacity and weight, thin-walled aluminum structures are used as structural parts in the aerospace, automotive and military industries. The manufacture of these thin-walled structures is mainly done by removing a large amount of material from full raw pieces, sometimes up to 95% of their initial mass. Because of a large volume of material removing, it is necessary to achieve high productivity, which is limited by the lack of rigidity of the thin walls of these structures. As a result, errors occur, while reducing accuracy and machining quality. The main subject and objective of this paper is related to the optimization of high-speed machining parameters of linear thin-walled structures made of aluminum alloy Al7075 from the aspect of surface quality of processed superficial as a goal function. For this purpose, experiments were carried out based on which conclusions were made of the influence of input parameters on the surface roughness.

GH4169 thin-walled parts are widely used in aerospace due to their high-temperature, corrosion resistance, and fatigue strength. However, they often deform from machining-induced residual stress, which is a significant unresolved manufacturing challenge. Additionally, initial residual stress from previous steps critically impacts subsequent processes. This study found that the residual stress of side milling of thin-walled parts under smaller cutting parameters is mainly caused by mechanical effects, and the influence of milling heat can be ignored. In this study, an analytical prediction model for residual stress of multi-step side milling thin-walled parts based on the deformation of thin-walled parts is proposed for the first time, which is suitable for smaller cutting parameters. Then, the proposed model is examined the mechanisms through which residual stresses develop during side milling and defined the applicability of model based on specific assumptions. To validate these assumptions, an experimental setup was devloped to simulate the movement of the heat source during milling. Subsequent two-step side milling experiments on GH4169 confirmed the accuracy of the analytical prediction model in predicting the residual stresses of multi-step side milling in thin-walled parts. It was observed that higher equivalent bending stiffness in thin-walled parts correlates with reduced machining deformation. The analytical model also provides a strategic approach to control machining deformation by managing the equivalent bending stiffness of the parts.

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]

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%. [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]

Due to their homogeneity and excellent ratio between load capacity and weight, thinwalled aluminum structures are used as structural parts in the aerospace, automotive and military industries. The manufacture of these thin-walled structures is mainly done by removing a large amount of material from full raw pieces, sometimes up to 95% of their initial mass. Because of a large volume of material removing, it is necessary to achieve high productivity, which is limited by the lack of rigidity of the thin walls of these structures. As a result, errors occur, while reducing accuracy and machining quality. The main subject and objective of this paper is related to the optimization of high-speed machining parameters of linear thin-walled structures made of aluminum alloy Al7075 from the aspect of surface quality of processed superficial as a goal function. For this purpose, experiments were carried out based on which conclusions were made of the influence of input parameters on the surface roughness. [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]

In order to achieve visual measurement error compensation for the surface of thin-walled components with chatter, a new real-time adaptive correction method for chatter motion error is proposed in this paper. Firstly, according to the mechanism of the chatter effect, an error analysis model of the dynamic structured light phase principal value is derived. Then, a real-time optimization method for the motion phase error in the frame of structured light projection is proposed. Meanwhile, a compensation method based on an adaptive grating projection period is proposed to eliminate the cumulative error of inter-frame chatter motion. Finally, a structured light visual inspection system and an experimental platform for chattering thin-walled parts are established for experimental verification. The experimental results under slight and severe chatter conditions indicate that the proposed method can optimize the phase errors within the projected frame in real time under any scenario, compensate for the cumulative errors between projected frames, and improve the point cloud accuracy of real-time 3D reconstruction. [ABSTRACT FROM AUTHOR]

CNC machining realizes the automatic control of machine tools with digital information, which is an advanced technology widely used in today's machinery manufacturing industry. In the process of NC lathe machining of parts, thin‐walled workpieces are prone to deformation due to poor rigidity and thinner wall thickness. Therefore, it is necessary to study the deformation suppression of NC parts. By combining fuzzy control with single neuron PID and introducing fast non dominated sorting genetic algorithm, the parameters of thin‐walled workpiece milling are optimized. The results show that the neuron PID algorithm of fuzzy control has no overshoot in the environment with or without interference, and has fast response speed and strong anti‐interference ability. In the case verification, the cutting force controlled by fuzzy neuron PID can quickly reach the reference 240 N and remain stable. The removal rate fluctuates less in the range of 8000–12,000. It can improve the metal removal rate while maintaining a constant cutting force, so as to restrain the machining deformation of parts. At the same time, the introduced fast non dominated sorting genetic algorithm can increase the maximum rotational speed to 10,486.5, the maximum feed rate per tooth from 0.075 to 0.112, and the rotational speed is nearly doubled, effectively improving the processing quality, providing a technical reference for the stable control of the NC machining system, and providing a new idea and method for part deformation suppression. [ABSTRACT FROM AUTHOR]

The paper deals with the issue of chatter reduction by modification of both the cutting tool geometry and the machining strategy. Tool wear, cutting forces, surface roughness, and flatness of milled thin-walled parts produced by WAAM (Wire and Arc Additive Manufacturing) technology are evaluated. The machining strategy and parameters of the cutting tool macrogeometry were designed on the basis of experimental machining. Due to the combination of custom geometry of the cutting tool and adapted machining strategy, it is possible to significantly reduce chatter during milling and thereby reduce the roughness of the machined surface, as well as improve machining precision. The experimental results contribute to the effort to expand the knowledge in the field of machining thin-walled parts. [ABSTRACT FROM AUTHOR]

Background. The object of the study is a set of design and technological solutions that determine the stability during assembly with tension of thin-walled cylindrical parts. The subject of the study is the interrelated parameters of the actual contact area and the specific friction force. The purpose of the work is the theoretical determination of the parameters that determine the criteria for stability during assembly (using the Mises formula) and the factors that contribute to the reduction of the friction force during assembly. Materials and methods. Research of the assembly process of thin-walled cylindrical parts assembled with tension using the Mises formula. The specificity of the interaction of the connection parts during assembly is shown and the interrelated parameters of the actual contact area and friction force that affect the stability of thin-walled parts during assembly are determined. Results. The shortcomings of information in the design of thin-walled joints in terms of stability are noted, which, as a rule, lead to the complication of assembly technology. A definition of the critical contact pressure in a joint during assembly using the well-known Mises formula is proposed. The issues of assembling a joint with tension under the influence of a press and the nature of the interaction of the mating surfaces, as well as the issues of forming the actual contact area are considered. The issues of the dual nature of friction and friction force are noted. A number of design and technological solutions are given that contribute to the reduction of friction force. Conclusions. Consideration of stability during assembly of thin-walled cylindrical parts under the influence of a press allows us to note that the determining parameters of critical pressure, in addition to the geometric dimensions of the mating parts, are the actual contact area and the friction force. Control of the actual contact area without reducing operational characteristics is possible by selecting methods for processing mating surfaces. An effective method is to control the friction force, where it is possible to use various technological solutions such as introducing surfactants into the contact zone and applying ultrasonic vibrations.

Accurately predicting the time-varying dynamic parameters of a workpiece during the milling of thin-walled parts is the foundation of adaptively selecting chatter-free machining parameters. Hence, a method for accurately and quickly predicting the time-varying dynamic parameters for milling thin-walled parts is proposed, which is based on the shell FEM and a three-layer neural network. The time-dependent dynamics of the workpiece can be calculated using the FEM by obtaining the geometrical parameters of the arc-faced junctions within the discrete cells of the initial and machined workpiece. It is unnecessary to re-divide the mesh cells of the thin-walled parts at each cutting position, which enhances the computational efficiency of the workpiece dynamics. Meanwhile, in comparison with the three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to the computation of the characteristic equation that is about nine times faster. The results of the modal test show that the maximum error of the shell FEM in predicting the natural frequency of the workpiece is about 4%. Furthermore, a three-layer neural network is constructed, and the results of the shell FEM are used as samples to train the model. The neural network model has a maximum prediction error of 0.409% when benchmarked against the results of the FEM. Furthermore, the three-layer neural network effectively enhances computational efficiency while guaranteeing accuracy. [ABSTRACT FROM AUTHOR]

Thin-walled parts are characterized by weak rigidity and are very prone to deformation during machining, which directly affects the machining accuracy and performance of the parts, so controlling the machining deformation of thin-walled parts is an urgent process problem to be solved. To address such problems, this paper proposes a flatness control machining method based on ice holding of workpieces. The method uses frozen suction cups to solidify liquid water to achieve stress-free clamping of workpieces. We conducted a low-temperature tensile test to investigate the low-temperature mechanical properties of the material. Comparative tests of ice-free and low-temperature ice-fixed milling were conducted to compare and analyze the changes of flatness and milling force in the two working conditions, to investigate the influence of machining parameters on the flatness of thin-walled parts, and to reveal the mechanism of low-temperature milling of ice-fixed workpieces. In addition, the low-temperature milling performance of titanium/aluminum alloy based on ice-fixation was compared. The results show that TC4 has good plasticity, high flexural strength ratio and strong resistance to deformation at low temperatures. Compared with no ice-holding, ice-holding machining effectively improves the flatness of the workpiece, and the order of the machining parameters affecting flatness is: feed rate > milling depth > spindle speed. The milling forces under ice-holding conditions were all greater than those without ice holding. The stiffness and hardness, resistance to damage and deformation of titanium alloy at low temperature are greater than those of aluminum alloy. This method provides a new method for high-precision machining of thin-walled parts. [ABSTRACT FROM AUTHOR]

In this study, we designed an integrated fixture system using eddy current sensors and piezoelectric actuators. With this system, active supporting forces are exerted on thin-walled workpieces to adjust their stiffness in a real-time manner and thus to suppress chatter in milling. When modeling this system, we used a mass–stiffness–damping element to characterize the dynamic behavior of the actuator under high-speed responses, and we considered the electromechanical coupling between the mechanical elements and the drive circuit of the actuator. We then proposed an optimal delayed state feedback controller for the closed-loop system. The delayed state is obtained by introducing the time delay in milling dynamics into the regular state feedback, and the optimal gain is worked out through the differential quadrature and gradient descent, which is a much more computationally efficient method than the classic semi-discretization. Among all similar approaches, our strategy leads to the largest stability region for milling of thin-walled parts and requires the least energy input, which are proved by simulations and experiments. [ABSTRACT FROM AUTHOR]

Abstract 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.

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Investigation of a new process and method to control the planarity of thin-walled parts can provide an important theoretical and application foundation for the high-precision, high-quality fabrication of thin-walled components in aerospace and other areas. A planarity control method based on ice fixation for the milling of thin-walled parts is proposed for thin-walled aluminum alloy 7075 flat parts. The heart of the method is the application of a low temperature of about −5 °C to a flat thin-walled part using a freezing tray to hold the workpiece in place in order to control the flatness of the thin wall face grinding process. Investigation of the influence of the machining process and workpiece shape parameters on the machining flatness of thin-walled parts was conducted based on the practicality and efficiency of the method. Experimental results demonstrate that: compared to traditional milling (non-ice fixation), this method of fixing the ice can significantly improve the milling flatness of thin-walled parts, with the reduction of flatness ranging from 16.4% to 60.0%; however, the reduction of flatness is accompanied by the phenomenon of increasing milling force, with the mean increase in milling strength ranging between 5.8% and 40.3%; the flatness of the machining process decreases with increasing workpiece thickness (15.6%–65.0%) and increases with an increasing aspect ratio of the workpieces (17.4%–48.7%). The orthogonal test results show that the best-machined flatness can be achieved at large spindle speed, high feed rate, and low cutting depth working conditions, i.e. spindle speed 3000r·min−1, feed rate 350 mm·min−1, and cutting depth 0.01 mm.

Additive manufacturing encompasses technologies that produce three-dimensional computer-aided design (CAD) models through a layer-by-layer production process. Compared to traditional manufacturing methods, additive manufacturing technologies offer significant advantages in producing intricate components with minimal energy consumption, reduced raw material waste, and shortened production timelines. AM methods based on shielded gas welding have recently piqued the interest of researchers due to their high efficiency and cost-effectiveness in manufacturing critical components. However, one of the most formidable challenges in additive manufacturing methods based on shielded gas welding lies in the irregularity of weld bead height at different points, compromising the precision of components produced using these techniques. In this current research, we aimed to achieve uniform weld heights along the welding path by considering the most influential parameters on weld bead geometry and conducting experimental tests. Input parameters of the process, including nozzle angle, welding speed, wire speed, and voltage, were considered. Simultaneously, image processing and wavelet transform were employed to assess the uniformity of weld bead height. These parameters were applied to produce intricate parts after identifying optimal parameters that yielded the smoothest weld lines. According to the results, the appropriate bead for manufacturing the part was extracted. The results show that the smoothest bead line is achieved in 27 V as the highest level of voltage, at a 90° nozzle position and the maximum wire feed rate. Parts manufactured using this method across different layers exhibited no distortions, and the repeatability of production substantiated the high reliability of this approach for component manufacturing.

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]

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems. [ABSTRACT FROM AUTHOR]

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. [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]

During the milling of thin-walled blades, the removal of material exhibits strong time-varying dynamics, leading to chatter and a decrease in surface quality. To address the issue of milling vibrations in the machining of complex thin-walled blades used in aerospace applications, this work proposes a process optimization approach involving non-uniform allowances. The objective is to enhance of he stiffness of the thin-walled parts during the milling process by establishing a non-uniform allowance distribution for the finishing process of thin-walled blades. By applying the theory of sensitive process stiffness and conducting finite element simulations, two processing strategies, namely uniform allowances and non-uniform allowances, are evaluated through cutting experiments. The experimental results demonstrate that the non-uniform allowance processing strategy leads to a more evenly distributed acceleration spectrum and a 50% reduction in amplitude. Moreover, the surface exhibits no discernible vibration pattern, resulting in a 35% decrease in roughness. The non-uniform allowance-processing strategy proves to be effective in significantly improving the rigidity of the thin-walled blade processing system, thereby enhancing the stability of the cutting process. These findings hold significant relevance in guiding the machining of typical complex thin-walled aerospace components. [ABSTRACT FROM AUTHOR]

Thin-walled components have extensive usage in the aviation, aerospace, automotive, and energy sectors. Wire Arc Additive Manufacturing (WAAM) additive technology is a technology that is used to produce thin-walled components by adding layers by layers. MIG/MAG welding technology is used in WAAM. The milling of thin-walled components often results in chatter, which causes waves on the milled surfaces. The variable helix angle reduces chatter during milling. The study found that a constant helix angle of 30°-30°-30° caused the active part of the wall to deflect towards the cutting tool, resulting in the least desirable outcomes. In contrast, cutting tools with 30°-30°-25° and 30°-30°- 35° helix angles produced comparable results with minor surface waves. [ABSTRACT FROM AUTHOR]

Niobium-based alloys are vital in high-temperature environments due to their unique chemical composition, phase structure, high density, and oxidation resistance. These alloys find extensive applications in industries such as medicine, engine manufacturing, and space technology. This study delves into the complexities of modeling laser welding processes, necessitated by the multitude of dynamic parameters specific to each surface configuration. Finite element modeling was conducted using COMSOL Multiphysics to analyze the heat transfer in butt joints between Nb-15W-5Mo-1Zr alloy plates under 400 W laser radiation power. The research employed finite element modeling to analyze heat transfer in Nb-15W-5Mo-1Zr alloy plates, considering non-uniform movement at the start and end of the welding process, Gaussian heat flux distribution along the laser spot radius, and temperature-dependent reflection coefficient. The models accounted for non-uniform movement, Gaussian heat flux distribution, and temperature-dependent reflection coefficient. The study demonstrated that thin plates of Nb-15W-5Mo-1Zr alloy can be effectively welded using specific laser processing modes, leveraging their high initial cooling rate of 1.2-1.3 s. Successful welding of Nb-15W-5Mo-1Zr alloy plates requires careful consideration of laser processing parameters. While the alloy's thermodynamic properties allow for effective welding, ensuring weld quality mandates additional production processes aimed at preventing potential part failure.

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.

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. [ABSTRACT FROM AUTHOR]

Niobium-based alloys are vital in high-temperature environments due to their unique chemical composition, phase structure, high density, and oxidation resistance. These alloys find extensive applications in industries such as medicine, engine manufacturing, and space technology. This study delves into the complexities of modeling laser welding processes, necessitated by the multitude of dynamic parameters specific to each surface configuration. Finite element modeling was conducted using COMSOL Multiphysics to analyze the heat transfer in butt joints between Nb-15W-5Mo-1Zr alloy plates under 400 W laser radiation power. The research employed finite element modeling to analyze heat transfer in Nb-15W-5Mo-1Zr alloy plates, considering non-uniform movement at the start and end of the welding process, Gaussian heat flux distribution along the laser spot radius, and temperature-dependent reflection coefficient. The models accounted for non-uniform movement, Gaussian heat flux distribution, and temperature-dependent reflection coefficient. The study demonstrated that thin plates of Nb-15W-5Mo-1Zr alloy can be effectively welded using specific laser processing modes, leveraging their high initial cooling rate of 1.2-1.3 s. Successful welding of Nb-15W-5Mo-1Zr alloy plates requires careful consideration of laser processing parameters. While the alloy's thermodynamic properties allow for effective welding, ensuring weld quality mandates additional production processes aimed at preventing potential part failure. [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]

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. [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]

Abstract 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.

In the aerospace field, Ti–Al alloy thin-walled parts, such as blades, generally undergo a large amount of material removal and have a low processing efficiency. Scheduling the feed rate during machining can significantly improve machining efficiency. However, existing feed-rate scheduling methods rarely consider the influence of machining deformation factors and cannot be applied in the finishing stages of thin-walled parts. This study proposes an offline feed-rate scheduling method based on a local stiffness estimation model that can be used to reduce machining errors and improve efficiency in the finishing stage of thin-walled parts. In the proposed method, a predictive model that can rapidly calculate the local stiffness at each cutter location point and a cutting-force prediction model that considers the effect of cutting angle are established. Based on the above model, an offline feed-rate scheduling method that considers machining deformation error constraints is introduced. Finally, an experiment is performed by taking the finishing of actual blade parts as an example. The experimental results demonstrate that the proposed feed-rate scheduling method can improve the machining efficiency of parts while ensuring machining accuracy. The proposed method can also be conveniently applied to feed-rate scheduling in the finishing stage of other thin-walled parts without being limited by machine tools. [ABSTRACT FROM AUTHOR]

Chatter in thin-walled parts is easy to occur in the process of machining, so the analysis of the stability of thin-walled parts has always been a research hotspot. In this paper, considering the influence of cutter eccentricity on milling force first, the coefficients of milling force were able to be identified by combining the milling force model with genetic algorithm. The results show that this method can obtain the milling force coefficients only by one experiment, and the accuracy is higher. Then the tool point Frequency Response Function (FRF) for a given combination can be calculated by using the Receptance coupling substructure analysis (RCSA) method that uses Timoshenko beam theory. Finally, the milling system can be divided into three types by aspect ratio. That is, when aspect ratio is less than 0.03, the system is considered to be a rigid tool-flexible workpiece system, but aspect ratio is between 0.03 and 0.2, the system is considered to be a flexible tool-flexible system, then aspect ratio is greater than 0.2, the system is considered to be a flexible cutter-rigid workpiece system. [ABSTRACT FROM AUTHOR]

Aerospace framework products typically have a thin-walled structure. To ensure the aerodynamic performance of the product, it is necessary to precisely control the shim clearance between the understructure and the skin. However, it is difficult to meet the requirements of product design by digital measurements and calculations of the shim clearance after installing parts on the final assembly or special test stand. To improve the efficiency and accuracy of the shimming process, a method to determine the shimming quantity by a flexible virtual assembly without a physical assembly process is proposed. The proposed method measures the initial shape of the assembly using digital technology that analyzes the assembled shape using a finite element model and calculates the shimming space using the assembled shape. First, the geometric information of the understructure, skin inner mold line (IML), and outer mold line (OML), or the actual pre-assembly shape, are obtained using a three-dimensional laser digitizer. The geometric features are reconstructed and the coordinate transformation matrix is then calculated. The coordinate system is unified based on the positioning features of each part, such as the positioning surface and positioning hole. Finally, the OML of the measured model and the theoretical OML are flexibly fitted to simulate the ideal state of the assembly. The shimming space between the IML and the understructure is calculated and the OML is precisely controlled by shimming. The physical verification results show that the deviation between the analysis results and the actual situation is within 0.1 mm. This method can predict the space of shimming when the OML reaches the ideal state without real assembly thereby reducing the time required for a repeated trial assembly of products and the cost of special measuring tools and conformal tools. [ABSTRACT FROM AUTHOR]

In the milling process, significant machining errors may occur due to the low stiffness of thin-walled parts. To reduce the cutting force-induced deformation in milling thin-walled parts such as blades, a cutter orientation optimization algorithm based on stiffness matching is proposed. The concept of maximum stiffness direction is put forward according to the phenomenon that different deformations are produced when applying forces with the same magnitude but in different directions. The stiffness of the in-process workpiece is obtained using the stiffness updating method. The cutter orientation optimization algorithm is presented to match the force with the maximum stiffness direction. The best cutter orientation is obtained by adopting the quantum particle swarms optimization algorithm at the key cutter location points, and then the cutter orientations of all cutter location points are obtained by the quaternion interpolation algorithm. The proposed deformation control method is verified on thin-walled blade milling experiments, and the experimental results show that the machining deformation of the blade with the optimized cutter orientation is reduced by about 36.51%, indicating that the proposed method can effectively reduce the machining deformation of the thin-walled parts. [ABSTRACT FROM AUTHOR]

Accurately predicting the time-varying dynamic parameters of a workpiece during the milling of thin-walled parts is the foundation of adaptively selecting chatter-free machining parameters. Hence, a method for accurately and quickly predicting the time-varying dynamic parameters for milling thin-walled parts is proposed, which is based on the shell FEM and a three-layer neural network. The time-dependent dynamics of the workpiece can be calculated using the FEM by obtaining the geometrical parameters of the arc-faced junctions within the discrete cells of the initial and machined workpiece. It is unnecessary to re-divide the mesh cells of the thin-walled parts at each cutting position, which enhances the computational efficiency of the workpiece dynamics. Meanwhile, in comparison with the three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to the computation of the characteristic equation that is about nine times faster. The results of the modal test show that the maximum error of the shell FEM in predicting the natural frequency of the workpiece is about 4%. Furthermore, a three-layer neural network is constructed, and the results of the shell FEM are used as samples to train the model. The neural network model has a maximum prediction error of 0.409% when benchmarked against the results of the FEM. Furthermore, the three-layer neural network effectively enhances computational efficiency while guaranteeing accuracy.

Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.

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.

During the milling of thin-walled blades, the removal of material exhibits strong time-varying dynamics, leading to chatter and a decrease in surface quality. To address the issue of milling vibrations in the machining of complex thin-walled blades used in aerospace applications, this work proposes a process optimization approach involving non-uniform allowances. The objective is to enhance of he stiffness of the thin-walled parts during the milling process by establishing a non-uniform allowance distribution for the finishing process of thin-walled blades. By applying the theory of sensitive process stiffness and conducting finite element simulations, two processing strategies, namely uniform allowances and non-uniform allowances, are evaluated through cutting experiments. The experimental results demonstrate that the non-uniform allowance processing strategy leads to a more evenly distributed acceleration spectrum and a 50% reduction in amplitude. Moreover, the surface exhibits no discernible vibration pattern, resulting in a 35% decrease in roughness. The non-uniform allowance-processing strategy proves to be effective in significantly improving the rigidity of the thin-walled blade processing system, thereby enhancing the stability of the cutting process. These findings hold significant relevance in guiding the machining of typical complex thin-walled aerospace components.

A 6-prismatic-spherical-spherical (6-PSS) parallel 3D printer with six degrees of freedom was introduced. The motion planning and control method of 6-PSS parallel 3D printer is proposed. The effects of vertical and variable-angle printing modes on the surface quality, porosities, and tensile strengths of printed specimens were investigated. Thin-walled specimens with oblique angles of 0°, 10°, 20°, and 30° were printed in vertical printing mode and variable-angle printing mode. In order to reveal the relationship between the printing mode and the quality of printed parts, the surface roughness and angle errors of the specimens were analyzed by the digital microscope, the internal defects of the specimens were analyzed by the micro-X-ray 3D imaging system, and the tensile strength of the specimens was tested. The results show that the surface roughness of the specimens increased with the increase of the angle. The surface roughness and angle errors of specimens printed in the vertical printing mode were significantly higher than those of specimens printed in the variable-angle mode. For the specimens printed in the two printing modes, the porosity of specimens increased and the tensile strength decreased with the increase of the oblique angle. For the specimens with the same oblique angle, the specimens printed by the variable-angle mode have smaller porosity and higher tensile strength. A ship-shaped thin-walled part was successfully printed on a cylindrical surface, indicating that the real-time variable-angle printing mode of the 6-PSS parallel 3D printer was very flexible.