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Latest articles for Measurement Science and Technology
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A review on in-situ monitoring of the temperature field in metal-based laser additive manufacturing
Metal additive manufacturing (MAM) presents unparalleled opportunities for fabricating complex and high-performance components. While achieving consistent part quality and process repeatability remains challenging. The temperature field is one of the dominant factors influencing the evolution of microstructure, distribution of residual stress, and mechanical properties during MAM. Therefore, it is significant to monitor and control the temperature field. In this review, the influences of the temperature field on the microstructure, residual stress, and mechanical performance are overviewed. The coupling mechanisms between thermal behavior and defect formation are explored. Secondly, a detailed review of the current state-of-the-art in-situ process monitoring techniques for the temperature field is provided. These techniques are evaluated for their capabilities and limitations in detecting defects. Thirdly, the application of machine learning (ML) algorithms in temperature monitoring and defect prediction based on thermal information during the MAM process is summarized. Finally, the advantages and current challenges—such as multiple sensors data fusion, physics-informed modeling, and ML models—are also discussed. This paper aims to provide a comprehensive overview of the precise and efficient monitoring of temperature field in MAM and equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions of in-situ process monitoring of temperature field during MAM.
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IGD-YOLOv8s: insulator defect detection via iterative attention and generalized dynamic feature pyramids
Insulators are critical components in transmission lines. Common defects, such as structural loss of the insulator caused by spontaneous rupture, breakage, and fouling can lead to short circuits and tripping faults, posing serious threats to power grid stability and the safety of the power supply. However, in practical applications, insulator defect detection faces several challenges, including small target sizes, insufficient representation of multiscale features, complex backgrounds, and imbalanced datasets with a limited number of defective samples. Traditional detection methods often struggle with missed detections of small targets and lack robustness in scenarios with large-scale variations and complex environments. To address these issues, this paper proposes an enhanced detection model based on YOLOv8s. The model introduces an iterative attentional feature fusion module to optimize multiscale feature representation and incorporates a Generalized Dynamic Feature Pyramid Network (GDFPN) to improve feature retention for small target detection, thereby enhancing robustness in complex backgrounds. Additionally, to mitigate the problem of limited defective sample data, the Stable Diffusion generative model is utilized to augment the dataset, effectively improving detection performance in small-sample scenarios. Experimental results demonstrate that the proposed method significantly outperforms the original YOLOv8s model in terms of recall, accuracy, and precision on the insulator defect dataset. The model exhibits strong detection capabilities and generalization performance, making it well-suited for real-world challenges such as small targets, multiscale variation, and complex backgrounds.
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Characterization of shear-flow behaviors of rock fractures using a newly-developed shear-flow apparatus
To investigate the hydraulic characteristics of rock fractures during sliding, we designed and fabricated a novel apparatus for conducting shear-flow tests. The apparatus was mainly composed of a servo-controlled loading system, a specially-designed pressure vessel, confining pressure loading system, and fluid control system. It can apply a maximum confining pressure of 20 MPa and a fluid pressure of 10 MPa while achieving a maximum shear displacement of ∼10 mm. Specialized monitoring sensors enable direct measurement of the sample normal displacement. The test results revealed that the fluid flow in rough fractures in granite is highly nonlinear, and is affected by factors such as fracture roughness, dilatancy of fractures, and gouge production during shearing. In addition, the synchronous change in the normal displacement and fracture slip rate during the quasi–static slip stage of the hydro-shearing test demonstrates the dependency of the normal displacement on the slip rate, which is consistent with previous studies. The apparatus provides a reliable platform for future shear-flow tests on rock fractures under various operating and experimental conditions.
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Enhancing defect detection with diffusion model
Due to the high complexity and technical requirements of industrial production processes, surface defects will inevitably appear, which seriously affect the quality of products. Although existing lightweight detection networks are highly efficient, they are susceptible to false or missed detection of non-salient defects due to the lack of semantic information. In contrast, the diffusion model can generate higher-order semantic representations in the denoising process. Therefore, this paper aims to incorporate the higher-order modeling capability of diffusion models into the detection framework, to better support the classification and localization of challenging targets. First, the denoising diffusion probabilistic model (DDPM) is pre-trained to extract the features of the denoising process to construct a feature repository. In particular, to avoid the potential bottleneck of memory caused by the dataloader loading high-dimensional features, a residual convolutional variational auto-encoder is designed to further compress the feature repository. The image is fed into both the image backbone and feature repository for feature extraction and querying respectively. The queried latent features are reconstructed and filtered to obtain high-dimensional DDPM features. A dynamic cross-fusion method is proposed to fully refine the contextual features of DDPM to optimize the detection model. Finally, we employ knowledge distillation to migrate the higher-order modeling capabilities back into the lightweight baseline model without additional efficiency cost. Experiment results demonstrate that our method achieves competitive results on several industrial datasets.
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A holistic measurement and profile evaluation method for spur gears based on line-structured light
Acquiring holistic gear point clouds (HGPC), encompassing both left and right flanks, and conducting global evaluation are of considerable significance for ensuring gear quality. While line-structured light (LSL) is an effective non-contact method, the high reflectivity of metallic flanks restricts it to one-sided sampling, complicating HGPC acquisition. Moreover, the absence of a unified paradigm hinders comprehensive assessment. Therefore, this paper proposes a holistic gear measurement and profile evaluation method based on LSL. A novel multi-target calibration artifact, combined with gear offset parameters, is developed to achieve high-precision alignment and complete HGPC reconstruction. A global deviation model is then established to visualize flank morphology, and a statistical framework is introduced for probabilistic quality grading, enhancing result reliability while preserving sensitivity to local details. Experimental comparisons with a gear measurement center (GMC) show that the proposed method enables micron-level HGPC acquisition, with a maximum expanded uncertainty of 1.0 μm. Confidence levels of 87.9% for Q5 grade and 100% for Q6 grade are achieved, supporting reliable quality evaluation in safety-critical applications. These results confirm the effectiveness and application potential of the method, which fills the gap in gear holistic evaluation using LSL.