Robotic machining is an effective method for processing complex inner cavities of spacecraft cabins. For the machining requirements of some narrow and deep cavity sections, an extension rod is attached to the robotic endeffector to enhance operational accessibility. While extending the machining coverage, the rod increases the dynamic compliance of the system and is highly prone to inducing machining chatter, which in turn impairs machining quality and efficiency. To address this problem, a structural design and parameter optimization method for a tunable-frequency tuned mass damper (TMD) is proposed, and a dynamic model integrating the tuned mass damper with the robotic machining system is established. This method achieves frequency tunability based on an eccentric crank-slider mechanism and realizes damping parameter regulation according to the principle of eddy current damping. Further dynamic compliance control experiments on the robotic machining system are carried out. The results show that the proposed method can reduce the peak value of end-effector dynamic compliance by 67.8%, significantly expanding the stable machining boundary.

To address the impact of surface defects in aircraft skin—critical structural components—on overall structural performance, this paper proposes a deep learning detection network based on the RT-DETR model, aiming to enhance the accuracy and robustness of aircraft skin defect detection. In response to the challenges posed by defects of varying scales, morphologies, and complex distributions, a series of innovative techniques were adopted for optimization. First, in the feature extraction stage, multilevel convolution blocks (MCB) were introduced. Through multi-layer convolution operations, the discriminative power of features at different scales was strengthened, effectively capturing details at various levels. Second, in the feature fusion stage, a multiscale feature enhancement (MSFE) module was employed, using multi-size depthwise convolution kernels to build contextual information, thereby improving the network’s robustness and adaptability to multiscale defect features. Finally, in the regression stage, a shape-aware (Shape-IoU) optimization module was introduced, which optimizes the matching between bounding boxes and defect contours, significantly improving detection accuracy. Experimental results show that the proposed network achieved an mAP@0.5 of 94.8% on the Aircraft dataset, representing a 12.7% improvement over the original RT-DETR model. Furthermore, on the NEU-DET test set, the model attained an mAP@0.5 of 92.5%. These results validate the effectiveness of the model in improving both the precision and generalization capability of aircraft skin defect detection.

Heavy-duty industrial robots are increasingly utilized in machining applications due to their advantages such as large workspace and flexible posture. However, their inherent stiffness characteristics render them highly susceptible to chatter during machining operations, significantly constraining their application and development. Research on chatter prediction based on high-precision dynamic modeling, along with various active and passive chatter suppression techniques, is crucial for achieving chatter-free machining. This paper systematically reviews the research progress in the dynamics of heavy-duty robotic machining systems globally. Firstly, the methods for modeling and predicting the dynamic characteristics of heavy-duty robots are introduced. Subsequently, an in-depth analysis of the dynamics model of the milling process and its solution methods is provided, elucidating the mechanisms of chatter, influencing factors, and variations in dynamic behavior. Furthermore, online chatter monitoring techniques and comprehensive suppression strategies are presented, evaluating the characteristics, applicable scenarios, advantages, and limitations of different approaches. Finally, based on the above analysis, a summary is provided along with key directions for future research. Through a comprehensive review and in-depth discussion of the dynamics research in heavy-duty robotic machining, this work aims to serve as a valuable reference and provide directional guidance for scholars in related fields.

To address the significant application value of ultrasonic rolling surface strengthening technology in the aerospace field, a multi-sensor fusion-based processing monitoring system was independently constructed using existing robotic ultrasonic rolling equipment. This system collected and analyzed physical signals in real-time during processing. The relationship between physical signals and processing states was explored, leading to the development of a multi-sensor fusion signal processing and feature extraction method, which resolved issues with signal synchronization. Time-frequency domain analysis methods were employed to extract signal features, followed by feature selection and dimensionality reduction. Machine learning models, including support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP), were utilized for intelligent prediction of surface integrity in ultrasonic rolling machining. Hyperparameters of these models were optimized using particle swarm optimization (PSO) and grid search to enhance prediction accuracy. Results show that the interval prediction accuracy for surface roughness is 91.4%, while the average relative errors for surface hardness and residual stress are 10.82% and 11.99%, respectively.

Wire arc additive manufacturing (WAAM) offers advantages such as rapid fabrication and compatibility with lightweight structural design, and thus exhibits great potential for manufacturing critical aerospace components. To address the various defects that may arise during the WAAM process, an unsupervised cross-modal anomaly detection method is introduced, integrating an improved residual deep temporal convolutional network (IRD-TCN) with a residualenhanced lightweight attention (RELA) module for the fusion of acoustic and electrical signals. Owing to the complexity of WAAM operating conditions, the detection capability of a single sensing modality is limited. Therefore, wavelet-based features from acoustic signals are fused with electrical signal features through cross-modal relational analysis, enabling IRD-TCN and RELA to identify changes in the correlation between the two sensing signals and achieve high-quality detection. Experimental results indicate that the proposed approach attains a precision of 98.37%, a recall of 97.73%, and an F1-score of 98.10%, effectively addressing the limitations of traditional data-fusion methods in terms of accuracy and robustness for anomaly identification in WAAM processes.

Wire arc additive manufacturing (WAAM) offers advantages such as rapid fabrication and compatibility with lightweight structural design, and thus exhibits great potential for manufacturing critical aerospace components. To address the various defects that may arise during the WAAM process, an unsupervised cross-modal anomaly detection method is introduced, integrating an improved residual deep temporal convolutional network (IRD-TCN) with a residualenhanced lightweight attention (RELA) module for the fusion of acoustic and electrical signals. Owing to the complexity of WAAM operating conditions, the detection capability of a single sensing modality is limited. Therefore, wavelet-based features from acoustic signals are fused with electrical signal features through cross-modal relational analysis, enabling IRD-TCN and RELA to identify changes in the correlation between the two sensing signals and achieve high-quality detection. Experimental results indicate that the proposed approach attains a precision of 98.37%, a recall of 97.73%, and an F1-score of 98.10%, effectively addressing the limitations of traditional data-fusion methods in terms of accuracy and robustness for anomaly identification in WAAM processes.

To investigate the effect of clearance induced by manufacturing errors on the performance of carbon fiber composite bolted joints, a top-down multi-scale analysis method was proposed. First, a three-dimensional progressive damage model was established to analyze the macroscopic mechanical response and damage mechanisms of the joint. The macroscopic strain was transferred to the representative volume element (RVE) model, where the crack initiation and propagation process at the microscopic scale were further explored. The accuracy of the numerical model was validated through five sets of experimental data. The results reveal a nonlinear relationship between the clearance amount and joint bearing strength. The bearing strength decreases most significantly when the clearance amount is 1.56%, with a reduction of 7.9%. Moreover, the clearance fit leads to the uneven stress distribution around the hole, which significantly reduces the joint stiffness. When the clearance amount is 3.08%, the joint stiffness decreases by 22.1%. The study also finds that matrix cracks initiate mainly in areas with dense fiber, while regions with high resin content tend to deterring the crack propagation. The results provide valuable theoretical and experimental guidance for the design and assembly of composite bolted joints.

To realize stable forming of the one-sided stitching (OSS) of the composite preform and avoid missing loop, wrong hooking and unstable stitch forming during the process of stitching the preform, the original OSS process is improved, and a wire-pulling mechanism which can realize the stable forming of the trajectory is added. The  mathematical model of the wire-pulling mechanism is established, and the trajectory of the end actuator of the wire-pulling mechanism is simulated and analyzed, which proves the rationality of its design. The working principle of each mechanism of the OSS device is analyzed. By determining the phase differences among the crank angles of the yarn-lifting, yarn-leading, yarn-hooking and wire-pulling mechanisms, the coordination relationship of the four mechanisms of the OSS device is analyzed and planned, and the motion cycle diagram of each mechanism is established, which lays a theoretical foundation for debugging of the OSS device. Finally, multiple controlled OSS experiments were conducted at varying parameters, including preform thickness, stitch pitch, and stitching speed. The result of loop formation rate of 100% proves the rationality and effectiveness of the motion planning, enhances the stability of the OSS trajectory, and thereby ensuring consistent stitching quality of the composite preforms.

To address the demand for rapid and precise drilling of carbon fiber composite laminated structures, a robotic drilling system was developed, the corresponding robotic drilling process technology was investigated, and the key process parameters were optimized. The drilling cutting forces of CFRP/CFRP and CFRP/Al laminated structures were measured. The influence of pressing force on drilling axial force was analyzed to determine the optimal range of pressing force. The variation characteristics of force during the drilling process were analyzed, and the variation laws of drilling quality of CFRP/CFRP and CFRP/Al laminated structures with cutting conditions were studied. Taking hole diameter accuracy, cylindricity and hole diameter consistency of laminated structures, which have significant impacts on assembly quality, as the evaluation indicators of drilling quality, the influence mechanism of robotic drilling process parameters on the drilling quality of CFRP/CFRP and CFRP/Al laminated structures were explored. The experimental results show that the robotic drilling technology can meet the drilling quality requirements of carbon fiber composite laminated structures. Based on the variation laws of drilling quality with process parameters, it is recommended that when drilling with threepoint drills, the process parameter range for CFRP/CFRP laminated structures is a spindle speed of 6000 – 10000 r/min and a feed rate of 0.02 – 0.06 mm/r; The range for CFRP/Al laminated structures is a spindle speed of 4000 – 6000 r/min and a feed rate of 0.06 – 0.08 mm/r. Within the selected process parameter ranges, the robotic drilling of CFRP/CFRP and CFRP/Al laminated structures can achieve hole diameter accuracies of grade H7 and grade H8, respectively.

Weakly rigid thin-walled components exhibit excellent lightweight performance and are widely used in aerospace and other fields. However, chatter is highly prone to occur during their milling process, which severely impairs machining quality and efficiency. Existing models mostly focus on the dynamic characteristics of the system, while neglecting the dynamic time-varying effect on radial cutting depth induced by the workpiece–tool elastic deformation caused by cutting forces under the combined action of different cutting positions of the workpiece and machining parameters. To address this problem, a time-varying prediction method for milling stability of weakly rigid thin-walled components is proposed, which takes into account the effect of machining-induced elastic deformation. First, an efficient iterative prediction model for continuous machining elastic deformation is established based on stiffness matrix reconstruction and the element birth and death method, enabling the rapid calculation of deformation along the tool path and the dynamic update of cutting
parameters. Second, a tool–workpiece multi-point contact milling dynamic model coupled with the elastic deformation effect is constructed, revealing the time-varying dynamic coupling mechanism. Third, a high-precision and efficient solution algorithm is developed based on the extended Newton – Cotes rule (O(τ7)), which significantly improves the solution efficiency of the complex dynamic model. Finally, systematic milling experiments are carried out to verify the accuracy and efficiency of the proposed method in predicting the machining stability domain. The results show that the proposed method can effectively predict the stable cutting parameter domain considering the influence of deformation, providing theoretical support for the high-efficiency and high-quality machining of weakly rigid thin-walled components.

Spherical EP741NP alloy powders were prepared by the supreme-speed plasma rotating electrode process (SS-PREP) and argon atomization (AA) process. A comparative study was conducted to investigate the effects of the two powder preparation processes on the powder characteristics. The results show that the EP741NP alloy powders prepared by the SS-PREP process have a more regular morphology, lower contents of hollow powders and irregularly shaped powders, and obvious advantages in physical properties such as oxygen pickup, sphericity and fluidity, but with a larger powder particle size. The powders prepared by the AA process contain more irregular particles, with many satellite particles on the surface of the powder particles, and adhesion between some powders. The surface of the EP741NP alloy powder particles prepared by the SS-PREP process exhibits a typical dendritic structure that grows in a radial pattern. The surface structure of the powders prepared by the AA process is unclear, a small amount of dendrites can be observed locally, and there are more hollow powder particles and individual powders with abnormal microstructures in the powders. The internal pores of the hollow powders are completely enclosed, and the inner surface of the pores is not smooth, showing a flocculent and irregular morphology. This phenomenon is mainly attributed to the incomplete breakup of the liquid film caused by the impact of the atomizing gas; The atomizing gas gets entrapped, and the internal atomizing gas does not escape before the pouch-shaped liquid film collapses, thereby forming hollow powders. At the test temperatures of room temperature, 650 ℃ and 750 ℃, the tensile properties of the EP741NP alloys fabricated from the two types of powders exhibit consistent trends in tensile properties, with the tensile strength and yield strength of the powders prepared by the SS-PREP method higher than those prepared by the AA process.

To address the challenges of lengthy design cycles and high verification costs in the casting industry, which arise from insufficient design experience during the research and development of complex new parts, this study proposes a similarity measurement method for casting parts tailored to intelligent design assistance. An adaptive multifeature fusion framework based on adaptive weight allocation was constructed, which enables efficient comparison with production-verified mature cases in the historical part database and provides decision support for new part design. Leveraging Python, the method implements multi-dimensional feature fusion, incorporating methods including feature preprocessing, 3D point cloud sampling, part spatial slicing and KD-tree construction, as well as cosine distance and surface normal vector matching for similarity evaluation, significantly enhancing the effectiveness and efficiency of similarity calculations. Experimental results demonstrate that when adaptive weights control the proportion of each feature extraction, the optimal part similarity reaches 81.25% for the example parts in this study. This occurs when the weighted values of cosine similarity, KD-tree similarity, slicing similarity, and normal vectors are set to 0.2, 0.15, 0.4, and 0.25 respectively—A performance that significantly outperforms any single-feature measurement. The proposed algorithm has been integrated into 3D design software and validated through case studies, proving its capability to rapidly and accurately retrieve and recommend similar parts and their mold structures. This provides reliable references for casting design, thereby reducing redundant design processes, shortening the research and development cycle, and lowering verification costs.

Aluminum matrix composites (SiC_p /Al) exhibit superior specific strength, excellent wear resistance, good thermal stability, and tunable properties, and thus have become key structural materials in high-tech fields including aerospace and rail transit. However, the presence of high-hardness particle reinforcement phases poses severe challenges to machining tools. Aiming at the demand for efficient machining of SiC_p /Al composites, this study systematically compared the performance differences between two superhard tools (PCD and PCBN) under varying cutting parameters by dynamically monitoring process parameters (e.g., cutting force and cutting temperature) and conducting correlation analysis of tool life and wear morphology, the cutting stability and failure mechanisms of the two tool types were comprehensively evaluated. The results show that PCD tools yield lower cutting forces and temperatures under different cutting speeds and high feed conditions due to their high thermal conductivity and low friction characteristics; Under the high-efficiency cutting process parameters (cutting speed v_c = 250 m/min, feed per tooth f_z = 0.1 mm/z), when the cutting distance reaches 2100 mm, the wear amount of the PCD tool is significantly lower than that of the PCBN tool, and its wear resistance is relatively improved by 61.80%. The dominant wear mechanisms differ between the two: PCBN tools are dominated by abrasive wear, random brittle spalling and diffusion wear, while PCD tools are characterized by adhesive wear and uniform micro-chipping with less severe abrasive wear. This study provides a theoretical and practical basis for optimizing the efficient and precision machining processes of SiC_p /Al composites.