Additive manufacturing (AM) enables integrated one-piece fabrication of parts, high material utilization efficiency, and unparalleled design freedom. However, problems such as low production efficiency, difficulties in ensuring quality stability and defect control limit the large-scale industrial application of AM. In-situ active modulation for AM enables dynamic regulation of parts during the fabrication process, thereby enhancing the quality of the final fabricated parts without introducing extra processing steps. In-situ active regulation enables direct intervention during defect nucleation, providing better effectiveness than post-printing repairs while avoiding performance degradation risks associated with post-processing. Based on the difference of core factors directly affected during regulation, in-situ active regulation is categorized into the following. (1) Process and path parameter optimization, where regulation directly impacts manufacturing-related procedural rules. It is the simplest method of control and the preferred approach, with widespread attention focused on its effects on microstructure and mechanical properties. (2) Laser beam shaping, where regulation directly influences the energy carrier morphology. To address issues such as edge over-melting and localized energy deficiency caused by non-uniform energy distribution, laser beam shaping should be employed. (3) Additional physical field modulation achieved by superimposing supplementary physical fields. When optimal process and path parameters still fail to obtain the desired microstructure and mechanical properties, additional physical field control may be considered. Meanwhile, this work summarized the effects of different additional physical fields on the mechanical properties of various metallic base materials. The future trends of in-situ modulation in additive manufacturing are also discussed.

Soft materials are the fundamental of numerous cutting-edge fields, propelling their rapid development and widespread adoption. Strain measurement for soft materials is critical for characterizing their mechanical behavior, yet current strain measurement techniques suffer from either limited measurement accuracy or insufficient measurement range. To address this, we propose a feature-tracking soft-material extensometer for high-accuracy strain and stretch ratio measurement method. This method utilizes Speeded-Up Robust Features (SURF) algorithm to detect and match feature point, deriving strain from their coordinate displacement between reference and deformed states. Using simulations and experiments, we examine how speckle size and tensile deformation influence the measurement accuracy of SURF algorithm, and we validate the method through uniaxial tensile testing on metals. Furthermore, we employ a speckle fabrication technique alongside an adaptive image processing algorithm to monitor the large deformation of soft materials. Uniaxial tensile tests on hydrogel and Polydimethylsiloxane (PDMS) specimens confirm that the proposed method significantly improves stretch ratio measurement accuracy over the universal testing machine (UTM). This study offers a novel, non-contact strategy for precise strain measurement in soft materials.

Marangoni spreading is frequently observed in nature and is utilised in industrial processes, including contaminant removal, drug delivery, and fabrication of complex structures. The spreading of a hydrophilic organic droplet on the free surface of an aqueous solution represents a convenient reference process. Spreading on an infinitely miscible interface is known to reach a quasi-steady state, with the spreading diameter determined by molecular diffusion into the bulk. However, the coupled effects of the spreading flow, the interface's solubility, and the evaporation of the volatile droplet are not well understood. In this work, we experimentally investigate the Marangoni spreading of a hydrophilic organic droplet on the surface of a saline solution. Widely available salts are often used to reduce the solubility of non-electrolytes in water by exploiting the salting-out effect, which controls the spreading of the organic droplet on the free surface of the saline. Quasi-static spreading-diameter measurements are used to quantify this spreading. The free surface's height, measured using the transmission speckle method, indicates that a high-concentration saline solution renders the interface temporarily non-miscible. Schlieren images capture the structure of the evaporative field, demonstrating that the evaporation is significantly reduced by mixing between the spreading droplet and the bulk fluid. It can, however, be retained through partial mixing due to the salting-out effect. A scaling law is deduced to interpret variations of the spreading diameter controlled by the salting-out effect. This work presents an effective experimental method for determining the salting-out constant, a crucial parameter in regulating interfacial reactions.

To address the limitations of existing methods in large-scale structural surface damage inspection - including poor path adaptability, insufficient three-dimensional (3D) reconstruction accuracy, and inadequate precision in damage detection and quantification - this study proposes an integrated multimodal inspection framework. An Improved Marine Predators Algorithm (IMPA) is first developed to solve the three-dimensional Open Traveling Salesman Problem (TO-TSP) involved in viewpoint planning. By introducing a hybrid Weibull-distribution-based motion strategy and a nonlinear adaptive step-size mechanism, the algorithm overcomes the limited exploration capability and rigid adjustment characteristics of conventional optimization approaches. The optimized path planning is further integrated with a lightweight deep learning network (LE-YOLOv5), forming a coherent system in which two-dimensional detection facilitates damage localization, while high-quality three-dimensional reconstruction enables accurate physical quantification. A bridge case study demonstrates the superior performance of the proposed approach. Compared with traditional paths, the IMPA-optimized trajectory increases the number of effective feature points by 33.98 % and the valid point cloud count by 25.12 %, while reducing the reprojection error by 17.06 %. Moreover, the system achieves high-precision damage quantification, relative measurement errors for crack length and width were 0.35 % and 8.91 % based on dense point clouds, and 1.30 % and 7.23 % based on triangular mesh models. These results confirm that the proposed framework substantially enhances reconstruction quality, improves damage detection accuracy, and provides a practical solution for structural health assessment of large-scale infrastructures.

Assessing the performance of materials and structures in extreme environments relies heavily on measurement technologies. Among these, optical measurement techniques have emerged as the optimal solution and the preferred metrology method due to their non-contact and non-destructive advantages. This special issue highlights the latest developments and applications of optical and laser-based measurement technologies in extreme environments. As of May 2025, this special issue has finalized the inclusion of 29 outstanding papers from around the world. These submissions cover advancements in optical and laser measurement technologies, their applications in extreme environments, integration with technologies such as artificial intelligence and neural networks, and other research related to extreme-environment measurement techniques. In the current era, the term “extreme environments” is no longer confined to deep space or reactor core scenarios. With the increasing integration and multifunctionality of instruments and equipment across industries, many materials and structures endure increasingly complex loads and frequently operate under extreme conditions. Assessing the performance of materials and structures in extreme environments relies heavily on measurement technologies. Among these, optical measurement techniques have emerged as the optimal solution and the preferred metrology method due to their non-contact and non-destructive advantages.

High-precision 3D reconstruction of large-size weak feature curved surfaces has long been a challenge in industrial manufacturing. Noise interference, occlusions, low overlap rates, and sparse geometric features often cause traditional point cloud registration methods to converge to local optima or fail to meet accuracy requirements. To this end, we propose a novel registration framework that integrates spatial positioning guidance with a multi-scale robust iterative closest point algorithm (MR-ICP). This method first establishes a world coordinate system using a global positioning system and achieves coarse registration of multi-view point clouds by introducing a transition coordinate system, thereby providing precise initial registration positions. Subsequently, the innovative MR-ICP algorithm incorporates a multi-scale extended convex hull to identify effective overlapping regions, while integrating a multi-scale feature fusion weighting mechanism to achieve synergistic optimization of global constraints and local details. Furthermore, we design an adaptive robust loss function, which dynamically adjusts residual weights to suppress noise during fine registration effectively. In Gaussian noise simulation stitching, this method reduces the root mean square error (RMSE) by up to 85% compared to the point-to-surface benchmark method. In the experiment, the average error achieved in the reconstruction of the engine hood (1.8 & times; 1.2 m) reached 0.261 mm, significantly outperforming existing advanced algorithms. This provides an efficient and scalable solution for large-scale industrial surface 3D reconstruction.

High-precision three-dimensional (3D) dynamic pose measurement and tracking are of crucial value in applications such as condition monitoring of rotating machinery, robotic navigation, and assembly and docking of aircrafts. To address the limitations of traditional visual measurement methods in dynamic environments, including noise susceptibility, feature instability, and insufficient real-time performance, an optimized digital encoded markers and their recognition and localization method combining deep learning and binocular vision are proposed in this study to achieve high-precision 3D pose measurement and tracking. In particular, the recognition network, constructed using the RefineDet framework and enhanced by an automated sample generation strategy based on real-world scenarios, significantly improves recognition performance. The integration of multiple image processing algorithms such as Sobel operator, Non-Maximum Suppression (NMS), and double threshold algorithm further enables precise marker recognition and center localization, providing a reliable foundation for 3D reconstruction and pose measurement. Experiments with color-coded markers under complex backgrounds and lighting conditions, as well as tests with markers arranged across multiple spatial planes, indicate that the method exhibits excellent generalizability and stability. The condition monitoring experiment about the rotating blades verified that the optimized coded markers have high encoding capacity, excellent anti-interference ability and fast recognition performance, and meanwhile, the proposed method accurately measured key parameters, including blade pose, flapping angle, and common taper, demonstrating outstanding precision and robustness. This approach provides reliable technological support for pose measurement, tracking, and fault diagnosis in complex dynamic systems.

Abstract The evolution of welding deformation significantly impacts the structural safety and dimensional stability of aerospace aluminum alloy propellant tanks throughout their manufacturing and service cycles. To address the challenges of complex variable temperature environments (-20 °C to +80 °C) and thermal strain interference encountered during cooling, storage, and operation, this paper proposes an in-situ synchronous multi-field measurement method integrating 3D Digital Image Correlation (3D-DIC) and dual-band colorimetric thermometry. Specifically, by constructing an integrated optical measurement system based on a single-camera beam-splitting optical path, the proposed method achieves spatiotemporal synchronous acquisition of full-field displacement and high-precision temperature data. Furthermore, utilizing the Inverse Compositional Gauss-Newton (IC-GN) algorithm for sub-pixel spatial registration, this approach effectively overcomes technical bottlenecks associated with temperature compensation and strain separation under non-isothermal conditions. Experimental results demonstrate that the method enables high-precision reconstruction of the welding residual deformation field in aluminum alloy structures and effectively characterizes the dynamic evolution behavior of residual strain.

Industrial gears are highly susceptible to surface defects under high-load and high-speed operating conditions, which can lead to reduced service life or even complete machine failure. However, complex operating environments pose significant challenges to achieving high-precision online defect detection. This paper proposes an online detection method integrating synchronous in situ tooth surface imaging with a two-stage deep segmentation strategy. The system achieves complete coverage of tooth surface images through motion-aligned imaging mechanisms and employs a two-stage cascaded architecture to enhance detection performance: the first stage rapidly segments the active tooth surface area, while the second stage utilizes an improved U-shaped dual-resolution network (UDDRNet) for precise identification of minute defects. Experimental results demonstrate that this method achieves 87.42% mIoU, 91.89% Recall, and 92.15% F1 scores while maintaining real-time performance, significantly outperforming single-stage methods and existing semantic segmentation models. These findings not only validate the high accuracy and practicality of the proposed method in complex industrial scenarios but also provide a scalable technical pathway for intelligent quality monitoring of critical industrial components.

With the growing demand for high-temperature deformation measurements in industrial environments, digital image correlation technology has been widely applied due to its non-contact measurement capabilities. However, its accuracy and reliability heavily depend on the durability of the deformation carrier under extreme conditions. To address this challenge, this paper develops a method for preparing high-temperature deformation carriers that combines operational simplicity with high customizability. The proposed method utilizes soft imprinting technology, employing custom rubber stamps and high-temperature resistant ink. The rubber stamps feature tailored patterns (e.g., dot matrices) fabricated via computer numerical control machining, whereas the ink is formulated by mixing inorganic binders and refractory powders in an optimized ratio. During preparation, the pattern is transferred onto the specimen's surface via imprinting, followed by a rapid pretreatment at 1500 degrees C for 2 min to achieve robust carrier formation. This paper further analyzes the thermal resistance and vibration stability mechanisms of the proposed carrier. High-temperature durability is achieved by transforming the ink into a stable alumina-based ceramic framework after pretreatment, maintaining structural integrity under the tested conditions up to 2000 degrees C. The ink penetrates surface pores to form strong mechanical interlocking and chemical interactions, ensuring robust interfacial adhesion. Meanwhile, the soft imprinting process facilitates a thinner and more uniform carrier layer, which minimizes the mass addition effect. To evaluate the reliability, high-temperature vibration experiments were conducted on the fabricated deformation carrier under coupled conditions of 2000 degrees C and 20-g acceleration, successfully capturing the full-field displacement distributions and time-domain vibration responses. The results demonstrate that the carrier prepared by this method possesses significant advantages, including strong interfacial bonding strength and exceptional tolerance to extreme thermal-mechanical environments. Combined with its capability for rapid, large-scale, and customized preparation, the proposed method holds great potential for engineering applications in extreme environment monitoring.

During laser-directed energy deposition (L-DED), the three-dimensional morphology and surface flow behavior of the melt pool is considered as critical physical fields which fundamentally determine the quality of fabricated components. Nevertheless, conventional in-situ monitoring systems have limitations in achieving synchronized observation of both morphological and flow characteristics. In this study, we develop an in-situ optical measurement system based on four-mirror module, enabling binocular vision imaging of the melt pool using a single high-speed camera. By incorporating deep learning-assisted feature extraction and matching algorithms, three-dimensional reconstruction of the melt pool morphology is accomplished. Simultaneously, digital image correlation techniques are employed to quantify surface flow fields. The dynamics evolution of melt pool morphology and surface flow are characterized under different process parameter combinations. On this basis, surrogate models correlating process parameters with melt pool characteristics are established. Sobol sensitivity analysis further reveals the influence of process parameters on the characteristics of melt pool. The obtained results demonstrate that the melt pool height is predominantly regulated by scanning speed and powder feed rate, whereas the melt pool width exhibits stronger dependence on laser power and scanning speed. The melt pool surface flow exhibits unsteady state, with turbulent kinetic energy concentrating predominantly in the central region. Marangoni convection is identified as the dominant mechanism governing melt transport along the scanning direction. This work provides a robust experimental framework for investigating multi-physics coupling phenomena in L-DED melt pools, offering technical support for process optimization and closed-loop control strategies of L-DED process.

The accurate characterization of three-dimensional temperature fields is of great significance in aerospace and other fields. This paper proposes a three-dimensional temperature field measurement method based on an improved multiplicative regularization reconstruction algorithm. To address the challenge of selecting regularization parameters in regularization algorithms, the multiplicative regularization algorithm with self-adaptive parameter adjustment is developed. Based on Dual-wavelength thermometry principles and leveraging the multi-channel imaging advantages of color cameras, a temperature measurement system is designed, incorporating dual or quad color cameras, bandpass filter elements, and synchronous triggering devices. Additionally, a sub-pixel-level matching method for dual-band image registration in four optical paths is developed, which greatly simplifies the measurement setup while ensuring reconstruction accuracy. Validation experiments demonstrate the feasibility of the proposed method, achieving the three-dimensional temperature field reconstruction and evolution analysis of solid propellant combustion flames.

In metal additive manufacturing, accurate monitoring and measurement of multi-physics fields during molten pool evolution are crucial for defect mechanism analysis and quality control. However, current monitoring techniques are limited to two-dimensional (2D) and single-physics observations, hindering the simultaneous acquisition of multi-physics information, particularly in three dimensions. To address these challenges, this study proposes an online in situ monitoring system for capturing multi-physics fields of the molten pool. Comprehensive molten pool information is obtained through dual-waveband, dual-view synergistic imaging. By combining two-color thermometry, intelligent identification and tracking of unmolten powders, the SETC (Speckle, Epipolar geometry, Temperature consistency, and Cluster) multi-constraint matching method, and data fusion techniques, the system enables, for the first time, single-camera synchronized measurement of highresolution 2D temperature and flow fields, as well as local three-dimensional (3D) multi-physics fields of the molten pool. Validation using cylinder experiments demonstrates high accuracy, with radius error below 0.74 %, a mean point cloud error of 0.025 mm and a mean displacement error of 0.004 mm. In Ti-6Al-4V powder printing, two-view 2D temperature and flow fields of the molten pool were captured by the system. Furthermore, local 3D information and pattern analysis of the molten pool were realized based on the identification, tracking and reconstruction of the unmolten powders. With its compact design, affordability, tunable optical pathways, high accuracy, and comprehensive data acquisition, the system offers an efficient approach for online in situ monitoring, high-precision measurement, and analysis of coupling mechanisms in the multi-physics fields of the molten pool during additive manufacturing.

Carrying out the constitutive model research of melt-cast explosives is helpful to evaluate the safety of explosives, optimize the performance design, guide the material research and improve the accuracy of engineering design. In this paper, the Karagozian & Case model is applied to melt-cast explosives for the first time. Taking the 3,4-dinitropyrazole-based melt-cast explosive as an example, quasi-static compression mechanical property, dynamic compression mechanical property and quasi-static Brazilian disc tests of its typical formulation were carried out by the universal materials testing machine and the split Hopkinson pressure bar test setup. Based on the test results, the parameters of the Karagozian & Case model were calibrated. Using the calibrated model parameters, the mechanical responses of the 3,4-dinitropyrazole-based melt-cast explosive under dynamic impact, quasi-static compression and triaxial confining pressure were calculated. The results show that the model’s capability in describing the complete mechanical behavior of melt-cast explosives, from elastic deformation through damage evolution to ultimate failure. The Karagozian & Case model effectively reproduces the material’s strain-rate sensitivity and accurately captures the transition from brittle to ductile behavior under confining pressure. Consequently, the Karagozian & Case model is established as an effective tool for predicting the mechanical response of melt-cast explosives under various loading conditions, and valuable insights are provided for safety evaluation and performance optimization in engineering applications.

The grating phase analysis method, as an approach based on the deformation field of gratings, has been widely used for measuring both out-of-plane and in-plane displacements on specimen surfaces. However, when being applied to the dynamic deformation measurement, the computing process relates to large amounts of image data, which leads to high computation cost and limited efficiency. With the recent progress in artificial intelligence technique, deep learning has emerged as promising alternatives to overcome these limitations. In this study, a high-precision two-dimensional in-plane displacement prediction method for moiré gratings based on deep learning is proposed. A dual-branch convolutional neural network, named by GratingNet, is constructed to effectively fuse spatial-domain and frequency-domain features. The network integrates deformable convolutions, attention mechanisms, and multi-scale feature extraction modules to enable effective prediction of in-plane displacement fields using deep analysis of pre- and post-deformation images. Furthermore, an uncertainty-weighted multi-task loss function is employed to achieve adaptive balancing between spatial and frequency domain learning tasks, enhancing the robustness of the model. The proposed method is validated with the star-shaped displacement fields and randomly generated displacement field data with varying control point intervals, respectively. The results demonstrate that GratingNet has better performance than the existing methods. Compared with the classical U-Net network, GratingNet achieves better displacement measurement accuracy, with the mean absolute error (MAE) reduced by 55.03% and 54.02% in the x-direction (u) and y-direction (v), respectively, while the root mean square error (RMSE) is reduced by 52.61% and 46.74%, respectively. In comparison with the traditional Sampling Moiré method, GratingNet exhibits higher measurement efficiency, achieving a 90.72% improvement in computational efficiency. These results indicate that the proposed method is well-suited for in-plane displacement field characterization, offering high computational efficiency and prediction accuracy.

Deposited height deviation (DHD) of printed layers is a common surface defect that restricts vertical printing accuracy during the additive manufacturing process. The accumulation of DHD layer by layer inevitably leads to the failure of subsequent additive manufacturing tasks. Therefore, accurate online measurement of DHD is crucial. This study proposed a novel amplification computer-vision measurement (ACVM) method that effectively utilizes both melt pool images and temperature information, achieving a DHD detection sensitivity of approximately 9.96 mu m. Theoretical connections between image features and DHD, as well as the theoretical associations between instantaneous temperature characteristic and DHD, have been systematically deduced. Based on these two theoretical relationships, DHD can be accurately and synchronously detected directly through the positions of image features and temperature. A single-camera dual-channel multi-signal detection (SDMD) system was developed and implemented within a laser-engineered net shaping (LENS) additive manufacturing system. Subsequently, an online measuring and verification experiment was designed to assess the height deviation of thin-walled structural parts. The experimental results demonstrated that the ACVM method provided an early response to DHD. The method exhibits significant technical application value in quality control in future.

Designing an efficient alkaline hydrogen evolution reaction (HER) catalyst requires enhanced hydrogen adsorption to facilitate water dissociation while noting that this would be detrimental to H2 desorption. Although hydrogen spillover-assisted HER has been emerging as a promising strategy due to separated active sites for water dissociation and hydrogen formation enabled by heterogeneous catalysts, interfacial charge accumulation, and strong interfacial proton adsorption would hinder proton transfer due to a high energy barrier. Herein, a novel strategy to realize hydrogen spillover-assisted HER enabled by edge dislocations of Mo2C catalyst is presented. The coupled tensile-compressive strain regions induced by edge dislocations serve as nano-reactors for HER. The Volmer process is greatly enhanced by strong water adsorption and efficient *H2O dissociation in the tensile regions, meantime the generated *H rapidly transfers to the compressive regions for easy hydrogen molecule release. As a result, the edge dislocation-rich catalyst achieves a low overpotential of only 61 and 179 mV at 10 and 300 mA cm-2, showcasing a new way to apply hydrogen spillover in single-phase catalysts and offering potential for developing cost-effective and efficient HER catalysts.

The miniaturization of microelectronic packaging brings complex reliability challenges, with corner bumps being particularly failure-prone. Precise measurement of thermal deformation failure behavior of these micro regions is critical. However, the actual deformation is hindered by rigid body motion caused by thermal mismatch-induced package warpage, which varies for each bump and may displace them from the field of view during temperature changes. In this study, a deformation evaluation method combined adaptive rigid body motion eliminating with digital image correlation is developed, which is based on the characteristics of each interconnect bump. The method uses local feature recognition and matching to address rigid body rotation and these features are identified by an algorithm that has been developed. Through reasonable assumptions, the rotation angle is obtained by fitting the identified features to eliminate the rigid body rotation, and eliminating rigid body translation through displacement transformation. The verification experiment indicates that the proposed method is validated, achieving an average error below 0.72%. Furthermore, the displacement and strain field evolution behavior of corner bumps were analyzed under a temperature cycle from -40 degrees C to 150 degrees C. Obtained experimental results indicate that the most fatigue-prone area is located at the inner interface between the chip and the bump, while the most tensile-stress-prone area is at the outer interface between the bump and the substrate. This study provides an effective approach to eliminate rigid body motion and offers valuable insights into the failure-prone regions of corner bumps, contributing to the reliability analysis of microelectronics packaging.

Accurate model parameters are crucial for reliable metal additive manufacturing (AM) simulations, which are essential for understanding AM material formation mechanisms, designing AM components, and controlling manufacturing processes. This study addresses the discrepancy between AM simulations and experimental results by developing an Additive Manufacturing Finite Element Model Updating (AM-FEMU) method. The AM-FEMU method updates and optimizes the simulation parameters based on the temperature field of the melt pool and the deformation field of the substrate during the AM process. Online measurements of three-dimensional displacement and melt pool temperature were conducted using three-dimensional sampling moire and multi- spectral colorimetric temperature measurement technologies. By comparing these measurements with finite element (FE) simulation, the heat source parameters and thermal expansion coefficient were updated successfully. Verification tests confirmed that the updated parameters significantly improved the accuracy of residual stress in AM simulations compared to the original parameters. This method promotes the application of FEMU in metal AM simulations, further providing a deeper understanding of the physical mechanism in metal AM process.

The measurement field of view of the conventional transmission electron microscopy (TEM) nano-moiré and scanning transmission electron microscopy (STEM) nano-moiré methods is limited to the hundred-nanometer scale, unable to meet the deformation field measurement requirements of micrometer-scale materials such as transistors and micro-devices. This paper proposed a novel measurement method based on scanning secondary moiré, which can realize cross-scale deformation field measurement from nanometers to micrometers and solve the problem of insufficient measurement accuracy when using only the TEM moiré method. This method utilized the electron wave in the TEM passing through the atomic lattice of two layers of different materials to generate TEM moiré. On this basis, the TEM was tuned to the STEM mode, and by adjusting parameters such as the amount of defocusing, magnification, scanning angle, etc., the electron beam was focused on the position near the interface of the two layers of materials, and at the same time, the scanning line was made approximately parallel to the direction of one of the TEM moiré fringes. The scanning secondary moiré patterns were generated when the scanning spacing was close to the TEM moiré spacing. Through this method, the deformation field, mechanical properties, and internal defects of crystals can be detected by a large field of view with high sensitivity and high efficiency. Compared to traditional methods, the advantages of scanning secondary moiré method lie in significantly improving the measurement field of TEM moiré and STEM moiré methods, realizing the cross-scale visualization measurement from nanometers to micrometers, and possessing atomic-level displacement measurement sensitivity. It can also simplify and efficiently identify dislocations, offering a new method for large-area visualization observation of dislocation density in broad application prospects.