Search Results (278)

Cobalt–chromium (CoCr) alloys have gained significant importance in the field of medical implants due to their outstanding combination of mechanical strength and excellent wear and corrosion resistance. Compared with other state-of-the-art materials, such as stainless steel or titanium, CoCr alloys typically exhibit superior fatigue strength, which is particularly advantageous for implants and components exposed to long-term repetitive loading. The present study investigates the feasibility of using commercially available CoCr alloy powders in the Laser Powder Bed Fusion (PBF-LB/M) process for the fabrication of biomedical implants. Microstructural characterization of the PBF-LB/M-manufactured CoCr samples revealed a dense, refined cellular–dendritic microstructure with a high degree of densification, characteristic of the rapid solidification associated with the PBF-LB/M process. The evaluation of mechanical performance, wear behavior, and corrosion resistance provides valuable insights into the suitability of these alloys for biomedical applications, especially in the design of complex implants requiring enhanced durability and long-term reliability. Furthermore, compression testing highlighted the influence of layer orientation on mechanical properties, emphasizing the importance of strategic prototyping and building orientation selection in the PBF-LB/M process. Tribological behavior assessed under dry sliding conditions demonstrated a significantly reduced coefficient of friction and lower wear rate compared to a conventional 316L stainless steel, which is frequently used in similar applications. Corrosion resistance was evaluated by potentiodynamic polarization measurements in Ringer electrolyte, showing that the PBF-LB/M-fabricated CoCr samples exhibit good corrosion resistance in environments resembling physiological fluids. Overall, the PBF-LB/M technique represents a promising manufacturing route for next-generation CoCr biomedical implants, particularly for orthopedic and dental applications. Beyond the biomedical field, the findings of this study also support the potential extension of PBF-LB/M-processed CoCr alloys to industrial sectors requiring high wear and corrosion resistance, including aerospace and automotive applications. Full article

Additive manufacturing (AM) has emerged as a transformative manufacturing technology for producing complex components with unprecedented design flexibility. However, the widespread application of AM parts in tribological environments is often limited by inherent defects such as high surface roughness, porosity, residual stresses, anisotropy, and weak interlayer bonding, which adversely affect friction, wear resistance, and tribocorrosion performance. This review critically examines the tribological behavior of AM materials and components, emphasizing the influence of processing routes, material selection, secondary reinforcing phases, and microstructural evolution on tribological performance. Particular attention is given to surface engineering strategies, including thermal spray coatings, laser surface treatments, plasma electrolytic oxidation, vapor deposition technologies, and mechanical surface modification techniques for mitigating AM-induced defects and improving surface durability. Recent advances in machine learning (ML) and artificial intelligence (AI) for wear prediction, process optimization, and intelligent tribological monitoring are also discussed. The review highlights the relationships among manufacturing parameters, surface integrity, and wear mechanisms, while identifying key challenges associated with process variability, long-term reliability, and industrial implementation. Future research should focus on multifunctional surface systems, smart coatings, real-time condition monitoring, and data-driven design approaches to accelerate the deployment of tribologically optimized AM components in aerospace, biomedical, automotive, and energy applications. Full article

Titanium alloys are irreplaceable in aerospace, biomedical and marine industries due to their low density, high specific strength and excellent biocompatibility. Conventional manufacturing methods suffer from low material utilization and difficulty in fabricating complex components, while additive manufacturing (AM) realizes near-net-shape forming of customized structures but introduces unique non-equilibrium microstructures and defects, which significantly alter the corrosion behavior and limit the long-term service reliability of additively manufactured (AMed) titanium alloys. This work systematically analyzes the corrosion behavior of titanium alloys fabricated by four mainstream AM processes: LPBF (laser powder bed fusion)/SLM (selective laser melting), EBM (electron beam melting), DED (directed energy deposition) and WAAM (wire arc additive manufacturing). It quantitatively summarizes the key electrochemical parameters and discusses the regulatory effects of matrix composition, post-treatment and service environment on their corrosion behaviors. The universal corrosion mechanisms—namely, passive film breakdown, micro-galvanic corrosion, and defect-induced localized corrosion—as well as process-specific corrosion mechanisms inherent to AMed titanium alloys are systematically elucidated. This study offers theoretical foundations for optimizing corrosion resistance and ensuring the reliable engineering implementation of AMed titanium alloys. Full article

Curvature-based descriptors are increasingly used in surface metrology for the characterization of complex geometries. However, their sensitivity to measurement uncertainty remains insufficiently understood, particularly in comparison with conventional deviation-based metrics. This study investigates the propagation of coordinate measurement noise into curvature estimation using a numerical framework combining differential geometry, local quadratic surface fitting, and Monte Carlo simulation. A set of nominal surfaces, including spherical, cylindrical, and free-form geometries, was analyzed under controlled stochastic perturbations. The results show that curvature uncertainty increases nonlinearly with coordinate noise and is significantly more sensitive to measurement errors than point-wise deviations. Even small perturbations in measured coordinates lead to amplified variability in curvature due to its dependence on second-order derivatives. The analysis further reveals the presence of systematic bias in curvature estimation and demonstrates that the resulting distributions deviate from normality, despite Gaussian input noise. This finding highlights the limitations of classical uncertainty evaluation approaches based on linear propagation and normality assumptions. In addition, the study shows that increasing sampling density does not necessarily improve estimation reliability, while the size of the local fitting window plays a key role in stabilizing curvature estimation, acting as an implicit regularization parameter. The comparison with conventional form deviation metrics confirms that curvature-based analysis provides complementary information about local geometric stability, which is not captured by global measures. The proposed simulation-based approach offers a practical framework for evaluating uncertainty in nonlinear geometric measurements and supports the integration of curvature-based descriptors into advanced metrological applications. The proposed framework can support uncertainty-aware evaluation of free-form surfaces in practical measurement tasks, including coordinate measurement of turbine blades and aerodynamic components in the aerospace industry, as well as optical scanning and verification of patient-specific biomedical implants, where accurate curvature characterization is essential for quality assessment. Full article

To address the high experimental costs and data scarcity inherent in Directed Energy Deposition (DED), this study proposes a data-efficient hybrid optimization framework for the precision manufacturing of Inconel 718 aerospace components. The framework leverages a two-stage strategy to bridge traditional experimental design with advanced machine learning, ensuring robust process optimization even with limited datasets. In the first stage, the Taguchi method (L16 orthogonal array) was employed for coarse-grained screening to identify influential control factors. In the second stage, a Fully Connected Neural Network (FNN) coupled with Bayesian Optimization (BO) was deployed. Crucially, this machine learning component functions as an optimization-oriented trend surrogate rather than a global regressor, successfully guiding the optimization under extreme data scarcity. The optimized process window yielded exceptional structural integrity, achieving a porosity as low as 0.03%. To thoroughly validate its practical efficacy, tensile testing (ASTM E8/E8M) and Rockwell hardness measurements (ASTM E18) were systematically conducted on the optimized specimens. The mechanical characterization demonstrated an average tensile strength of approximately 1358 MPa and a hardness of ~40 HRC. Finally, the framework was successfully validated through the robotic DED fabrication of a complex-geometry aerospace engine combustion chamber casing, bridging laboratory-scale optimization with authentic industrial applications. Full article

Dual 6 degrees of freedom (6-DOF) series-parallel cooperative robot actuators are core execution components in modern intelligent manufacturing systems, which are widely used in high-end manufacturing scenarios such as aerospace precision assembly, laser precision machining, and core component assembly of new energy vehicles. However, in actual manufacturing processes, the pose deviation between theoretical model prediction and actual motion execution of the actuator, caused by kinematic model mismatch, unquantified core parameters, incomplete error processing chain, and complex on-site environmental interference, severely restricts the assembly accuracy, product qualification rate and production efficiency of the manufacturing system. To address these critical pain points of robot actuators in precision manufacturing systems, this paper proposes a four-layer hierarchical decoupling multi-modal fusion calibration algorithm for high-precision pose control of dual series-parallel robot actuators. The algorithm integrates singular value decomposition (SVD) for cross-structure coordinate alignment of heterogeneous actuators, chaotic mapping-enhanced particle swarm optimization (PSO) for nonlinear error suppression of the actuator system, attention-enhanced deep residual network (DRN) for unmodeled residual learning of the actuator, and Kalman filter (KF) for dynamic noise reduction in the manufacturing process. Meanwhile, a full-chain error transfer model of the actuator system in the manufacturing process is constructed, and the core parameters of the algorithm are quantified via dimensional sensitivity analysis and orthogonal experiments. Experimental results show that the static position error of the actuator system after calibration reaches 1.4 ± 0.08 mm, and the static pose error reaches 0.0059 ± 0.0003 rad in the laboratory environment; in the engineering application of laser precision machining in an actual manufacturing line, the position error and pose error only increase by 8.6% and 6.8% respectively, maintaining high stability in industrial manufacturing scenarios. Compared with mainstream calibration methods, the proposed algorithm reduces the position error and pose error of the actuator by up to 55.7% and 17.9% respectively, with lower computational complexity and higher engineering reproducibility. This work constructs an end-to-end error suppression chain with quantitative parameter criteria for the series-parallel actuator system in manufacturing systems, which provides a reliable high-precision calibration solution for industrial dual-robot cooperative manufacturing and has important guiding significance for improving the motion accuracy and operation stability of actuators in precision manufacturing systems. Full article

Powder Bed Fusion–Laser Based (PBF-LB) represents the most-used metal Additive Manufacturing technology thanks to its capability of producing high-complexity geometries. The need for industries to define a qualification framework of additive components drew attention to post-processing approaches that can be applied to mitigate or reduce inherent defects. Among these post-processing approaches, Hot Isostatic Pressing (HIP) is recognized as one of the most effective techniques to address these challenges. Among materials employed with PBF-LB, especially in the aerospace sector, 316L stainless steel and the AlSi10Mg aluminum alloy are the most investigated, while among innovative copper alloys, there is GRCop42. Thus, the aim of this paper is to investigate the effects of low-temperature HIP on the tensile properties and microstructure of these materials. For this reason, tensile tests, metallographic analysis and X-ray computer tomography were conducted. The results highlight the influence of low-temperature HIP treatment with respect to the as-built condition. In particular, the Yield and Ultimate Tensile Strength for 316L and GRCop42 clearly improved, while for AlSi10Mg a relevant reduction was detected. However, an unexpected result was the reduction in the GRCop42 elongation that fell from ~10% down to ~2.5%, even though the porosity of the material was reduced to close to zero. Full article

Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by layer, leading to degraded surface quality and impaired mechanical performance of as-built components—a critical bottleneck limiting their large-scale industrial adoption. Accurate and robust layer-wise melting quality recognition remains a challenge due to the complex surface morphologies induced by such melting anomalies. This study presents a machine learning-enabled in situ monitoring approach for layer-wise melting quality identification in L-PBF. By systematically varying laser power and scanning speed, 24 parameter combinations were designed to fabricate specimens with three distinct melting states: over-melting (OM), lack of fusion (LOF), and normal melting. A high-resolution complementary meta–oxide–semiconductor (CMOS) camera was used to capture layer-wise surface images of the specimens, and following abnormal layer filtering and manual validation, a high-quality dataset comprising 5110 layer-wise images was constructed. Two mainstream machine learning approaches were systematically evaluated and optimized for melting quality classification: a support vector machine (SVM) model leveraging handcrafted gray-level co-occurrence matrix (GLCM) texture features achieved a classification accuracy of 96.77%, while a convolutional neural network (CNN) model with end-to-end feature learning directly from raw images attained a superior accuracy of 98.14%. In terms of computational efficiency, the CNN model exhibited a faster inference speed with a per-layer inference time of just 0.036 s, nearly half that of the SVM model (0.068 s per layer). Most critically, the CNN model completely eliminated fatal cross-class misclassification between OM and LOF—an error mode common in the SVM model that would trigger erroneous process corrective actions in practical industrial applications. The findings demonstrate that image-based machine learning provides a reliable technical foundation for intelligent in situ monitoring of the L-PBF process. With its high accuracy, strong robustness, and superior computational efficiency, the CNN model can effectively support on-site operational decision-making, reduce material and time losses, and enhance process stability in industrial settings, thus exhibiting significant potential for practical engineering deployment. Full article

Short carbon fiber-reinforced thermoplastic composites (SCFRTPCs) are widely employed in energy, aerospace and competitive sports due to their high specific strength/stiffness and design freedom. The Large Format Additive Manufacturing (LFAM) process, as an advanced technology for fabricating thermoplastic composite components, enables the rapid production of complex large-scale composite components and prototypes. Nevertheless, achieving satisfactory mechanical load-bearing performance remains a key challenge. To overcome this limitation, a methodology was developed for manufacturing short carbon fiber/Nylon 6 (SCF/PA6) composite components with programmable load-bearing performance via large-format additive manufacturing–compression molding (LFAM-CM). This process innovatively synergizes material stiffness enhancement with component deposition path planning, utilizing the high-orientation and low-porosity tape-shaped beads produced by LFAM to fabricate components. The experimental results demonstrate a peak load capacity of 549N, representing 33%, 231%, and 144% enhancements versus randomly oriented fiber, high-porosity, and non-path-planned components, respectively. Simultaneous meso- and macro-scale bearing performance analysis demonstrated the cross-scale synergistic enhancement effect of this process on component load-bearing capacity. Finally, a systematic analysis of energy dissipation, stiffness, and damage tolerance revealed the underlying mechanisms for enhanced load-bearing performance. This work establishes an expanded design paradigm where multivariate coupling replaces linear structure–property relationships, providing practical frameworks for the development of next-generation functionally graded components with tailored mechanical–electrical–thermal multifunctionality. Full article

Riveting quality inspection is critical for ensuring structural integrity and safety in aerospace, automotive, and civil engineering, as rivet defects during the riveting process may cause catastrophic failures in structural connections. This study focuses on the detection method for multi-type rivet head defects and aims to improve the performance of feature extraction and classification for various head defects. The research is carried out to develop a lightweight classification network with a Dynamic Screening Self-Attention (DSSA) mechanism for 3D point clouds. To achieve the rivet head dataset, we employ Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering to extract each target head data from the dataset of riveted plates. The head dataset can be further simplified using the Non-Maximum Eigenvalue Curvature Method (NMECM). In this way, redundant information can be reduced. The PointGhost network is then designed for the classification of head defects. It contains a sampling module with a Virtual Block Sampling (VBS) mechanism that reduces the computational complexity. In addition, there exists a feature extraction module with a Grouped Pointwise Convolution Ghost (GPC-Ghost) lightweight model that performs local and global feature learning, together with the DSSA mechanism to enhance the riveted head defects. Lastly, the severity levels of rivet protrusion and indentation are quantified using Principal Component Analysis (PCA) and the Total Least Squares (TLS) fitting algorithm. In terms of the experiment, six popular lightweight models are compared, wherein GPC-Ghost shows more significant performance, achieving a 4.31% higher mean accuracy than PointNet++, with less computational cost of 0.66 GFLOPs. Based on the comparative analysis of six attention mechanisms and seven classification networks, the PointGhost model possesses the highest mean accuracy of 99.49%, with an average misclassification rate of 1.19%. The method can balance the accuracy and efficiency effectively, demonstrating its potential for engineering inspection. Full article

This study aims to clarify the effects of initial microstructure and friction coefficient on the high-temperature formability of TA17 titanium alloy. Three typical microstructures were prepared via different annealing processes to provide theoretical and experimental support for hot forming process optimization. Existing studies on TA17 titanium alloy mainly focus on its room-temperature mechanical properties and corrosion resistance, while quantitative investigations on the influence of different initial microstructures on its high-temperature forming limit are still scarce. Moreover, the temperature evolution characteristics of the friction coefficient for TA17 alloy and its quantitative influence on deep-drawing forming limit remain unclear, and few studies have considered the effect of initial microstructure and friction coefficient on the high-temperature formability of titanium alloys. Combined with high-temperature tensile tests, high-temperature friction- wear tests and finite element (FE) simulation, the effects of microstructure characteristics and friction coefficient on the high-temperature formability of TA17 alloy were systematically investigated. The results show that the equiaxed microstructure obtained by annealing at 850 °C for 2 h exhibits the best high-temperature plasticity at 900 °C and strain rate of 0.01 s⁻¹, while the plasticity of Widmanstätten and bimodal structures is significantly reduced. The high-temperature friction coefficient of TA17 alloy decreases sharply with increasing temperature, dropping from 0.56 at 650 °C to 0.19 at 800 °C, and can be stably controlled below 0.2 in the optimal forming temperature range of 800–900 °C. The friction coefficient has a remarkable influence on the deep-drawing forming limit: as the friction coefficient increases from 0.05 to 0.2, the limit principal strain and limit punch stroke decrease accordingly. This study reveals that fine equiaxed microstructure and low friction coefficient can enhance the high-temperature forming formability of TA17 titanium alloy. In actual industrial hot forming processes, it is recommended to use TA17 alloy with equiaxed microstructure and control the friction coefficient below 0.15 by using high-temperature lubricants, which can effectively improve the forming quality of complex aerospace structural components. Full article

In response to the growing demand for complex thin-walled lightweight alloy components in the automotive and aerospace industries, this study investigates the limitations of traditional gas pressure forming technologies. Using 2A12 aluminum alloy thin sheets as the research material, hot high-speed gas bulging experiments were conducted to study the effects of rapid inflation and rapid deflation processes on the forming accuracy, wall thickness, and strain distribution of bulged components. This aims to provide guidance for theoretical research and validate the superiority of the rapid deflation process. The results show that: (1) When forming cup-shaped components at 400 °C, the die-fitting degree of the component formed by the rapid deflation process reaches 89.5% and the minimum corner radius is 2.5 mm. Overall, the forming accuracy of this process is significantly superior to that of the rapid inflation process. (2) Within the temperature range of 400–450 °C, the rapid deflation process successfully formed a spherical-bottom component with a depth of 30 mm, overcoming the cracking defects induced by localized cooling and non-uniform temperature fields in the rapid inflation process, thereby improving the forming limit. (3) Under consistent conditions, the wall thickness uniformity of the sheet formed by the rapid deflation process is significantly higher than that of the sheet formed by rapid inflation, and the wall thickness uniformity improves with increasing temperature. Future work is expected to further enhance the repeatability and stability of forming accuracy and the forming limits of extreme geometries by further optimizing process parameters and expanding the material applicability range. This will provide practical technical support for the manufacturing of lightweight, high-performance aerospace equipment and automotive components. Full article

Infrared small target detection is widely used in aerospace surveillance, maritime search and rescue, and military reconnaissance. However, the performance of detection algorithms is highly dependent on scene characteristics, and methods that perform well in simple backgrounds may degrade substantially in complex environments. Existing indicators, such as information entropy, average gradient, and peak signal-to-noise ratio, can reflect detection difficulty from individual perspectives, but they do not provide a unified measure that jointly considers target saliency, background complexity, and target–background coupling. To address this issue, this study proposes a scene detection complexity (SDC) metric for quantifying the difficulty of infrared small target detection. Six basic indicators are selected from three dimensions, namely target saliency, background complexity, and target–background coupling: statistical variance, target–background contrast, signal-to-clutter ratio, information entropy, structural similarity, and target size. After Min–Max normalization, objective weights are determined by combining the entropy weight method and principal component analysis, and the weighted indicators are fused into an SDC value in the range of $[0,1]$. Experiments on 100 test images selected from IRST640, MSISTD, SIRST-V2, and an infrared small-aircraft sequence dataset show that the proposed SDC achieves a Pearson linear correlation coefficient of 0.956 with subjective difficulty ratings and $-0.902$ with image-level detection scores obtained from seven representative algorithms. The results further indicate that traditional methods are more sensitive to increasing scene complexity, whereas deep-learning-based methods are comparatively more robust in complex backgrounds. The proposed SDC provides a unified and objective tool for performance evaluation, algorithm selection, and pre-assessment of scene difficulty in infrared small target detection. Full article

This study presents research on optimizing the parameters of the passivation process for precipitation-hardening stainless steels (PHSS) to improve the corrosion resistance of AM350 and CUSTOM 450 alloys, which are extensively utilized in the aerospace and aviation sectors, since, as this is a complex process, it requires the implementation of a robust methodological approach that allows for multi-response optimization. Experiments were designed using the Taguchi method, which offered a strong framework for examining the impact of material, type of passivation solution, concentration, temperature, and passivation process time on the corrosion resistance of both PHSS alloys. To confirm the ideal PHSS passivation process parameters and measure the significance of each component, gray relational analysis (GRA) and analysis of variance (ANOVA) were also employed. The combined use of the Taguchi/GRA represents a robust and efficient methodological approach to the multi-response optimization of complex processes, overcoming the limitations inherent in the individual application of each technique. It was determined that the optimized parameters were a PHSS AM 350, a solution composed of a combination of citric acid and oxalic acid, acid concentration of 25% v/v, temperature of 50 °C, and time of 120 min. This combination of parameters resulted in significant improvements of up to 55% in corrosion resistance in the H₂SO₄ and NaCl evaluation solutions, demonstrating the effectiveness of the optimized conditions. This work emphasizes the efficacy of integrating Taguchi, GRA, and ANOVA techniques to significantly reduce the corrosion rate of PHSS undergoing the passivation process using alternatives to nitric acid. The integration of the Taguchi methodology with GRA enables the normalization and combination of responses with different scales and performance criteria into a single gray relational index, facilitating the overall evaluation of the system. Full article

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible. Full article