Qibo Feng

Accurately measuring geometric parameters of high-aspect-ratio blind holes is a critical requirement for microelectronics and micro–nanooptics applications. Conventional measurement techniques are restricted by limited measurement precision, incomplete measurable geometric indicators, bulky optical hardware, and potential irreversible damage to test specimens. To tackle these drawbacks, this paper proposes a deep-learning-driven image reconstruction method for full-parameter geometric measurement of high-aspect-ratio blind holes. In the proposed method, a Denoising Convolutional Neural Network (DnCNN) is deployed to restore low signal-to-noise ratio (SNR) microscopic images captured at blind hole bottoms. Meanwhile, Laplacian variance sharpness evaluation coupled with local quadratic polynomial fitting is adopted to boost the precision of Z-axis focal positioning and depth calculation. A dedicated machine vision measurement system is custom-developed, where annular ring illumination is integrated to strengthen light irradiation at hole bottoms and guarantee high-quality image acquisition. To address the scarcity of authentic paired clean-noisy training data, high-clarity surface micrographs are artificially degraded in reverse to synthesize low-quality counterparts, forming a dataset containing 2000 image pairs for DnCNN training and optimization. Furthermore, a dedicated feature extraction pipeline combining image preprocessing, Otsu-based adaptive threshold segmentation and least-squares ellipse fitting is designed to extract core geometric metrics, including microhole diameter, depth, taper angle, and relative ellipticity deviation. Experimental validation reveals that the absolute measurement deviations of the mean measured top diameter, bottom diameter, and depth reach 4 μm, 7 μm, and 8 μm, respectively, with a calculated depth-to-diameter-ratio error of 0.06. The results demonstrate that this method delivers a high-precision, low-cost non-destructive micrometer-scale solution for comprehensive geometric inspection of high-aspect-ratio blind holes.

High-precision measurement of six-degree-of-freedom relative pose is a critical challenge for deformation control and stability improvement of structural components in precision assembly and aerospace structural components. To address incomplete pose-parameter measurement and the difficulty of pose-parameter decoupling in existing methods, this paper proposes an error-modeling and crosstalk-compensation method for 6-DOF relative pose measurement based on the fusion of fiber-coupled heterodyne interferometry and laser collimation. First, a relative pose measurement model was established using homogeneous coordinate transformations and ray-tracing methods, wherein physically reasonable constraints on structural deformation were introduced to compensate for crosstalk caused by the coupling between angular and displacement errors based on COMSOL simulation, and this model was later validated via Zemax. Second, a measurement system was constructed, followed by calibration experiments and a 35 h stability test to characterize its measurement accuracy and long-term stability. Finally, three repeated plate loading and unloading cycles were conducted under structural deformation conditions. The comparative results show the maximum residuals of 0.92 μm, 0.79 μm, 1.00 μm, 0.54″, 2.53″, and 1.53″ for Δx, Δy, Δz, Δα, Δβ, and Δγ compared to reference instruments, while it drops from 61.13 μm to 1.00 μm for Δz after model compensation, reducing error by 96.79%. The proposed method provides an effective approach for high-precision measurement and decoupling of the 6-DOF relative pose of structural components under complex operating conditions.

To address metastability, phase misalignment, and wavelength jumps in the high-frequency heterodyne interferometric measurement of computer numerical control (CNC) machine tool positioning errors, a robust all-hardware field-programmable gate array (FPGA) phase demodulation system is developed. The architecture adopts an all-hardware design covering the complete process from signal preprocessing to synchronous data alignment. The front-end integrates time-domain constraints and multi-stage convergence to filter noise and suppress metastability. The core layer utilizes a wide-threshold hysteresis algorithm with a quarter-cycle buffer to eliminate counting errors at the fundamental level. The back-end employs synchronous latching and handshake protocols for precise data alignment. Experiments conducted on a 3.2 m guide rail demonstrate that the average repeatability of the proposed system reaches 0.59µm, outperforming the Renishaw XL-80 (0.88µm), and the nonlinear residuals of both systems exhibit high consistency. Stability tests over a 1.5 m long-distance open path further confirm that the raw fluctuations of the system remain at the sub-micrometer level, with no phase-locking failures or jumps. This timing-deterministic, jump-free hardware solution offers significant potential for precision measurement in complex industrial environments.

With increasing rail traffic intensity, reliable detection of shallow subsurface rail damage is essential for operational safety. This critical narrative review evaluates non-destructive testing technologies relevant to defects whose active crack front or principal scattering zone lies within the upper approximately 0.5–10 mm of the rail, while treating the 10–15 mm range as a transition to deeper-defect verification. Magnetic flux leakage, magnetic particle inspection, visual inspection, eddy current testing, and conventional ultrasonic testing are first examined as screening or confirmatory comparators. The review then focuses on four ultrasonic surface-wave excitation routes—contact piezoelectric, active air-coupled, electromagnetic acoustic, and laser ultrasonic—and distinguishes source-specific laboratory capability from demonstrated field evidence. Because the cited studies use different defect geometries, rail conditions, sensor configurations, speeds, and decision criteria, their numerical values are reported as source-conditioned evidence rather than as a normalized ranking. An engineering decision matrix links defect depth and size, inspection speed, surface condition, and noise environment to a recommended screening–confirmation workflow. The synthesis identifies contact piezoelectric UT/PAUT as the most mature quantitative confirmation route, while EMAT, air-coupled UT, and laser UT retain method-specific advantages but require stronger natural-defect and in-service validation.

Although laser heterodyne interferometric sensing systems offer exceptional theoretical resolution, practical precision is constrained by intractable nonlinear errors stemming from optical imperfections. Conventional compensation methods suffer from hardware dependency, complexity, and performance degradation under low signal-to-noise ratios (SNR). To address this, we propose a precision calibration method using a self-supervised Physics-Informed Neural Network (PINN) guided by frequency-domain priors with harmonic distribution characteristics. This approach establishes a robust compensation model by inverting equivalent parameter sets and error curves in a single step. Specifically, leveraging high-precision displacement references, the method extracts measurement residuals containing periodic physical features. Subsequently, it integrates frequency-domain priors into a physically constrained network architecture: theoretical frequency characteristics construct masks to generate high-confidence pseudo-labels, while the error equation is recast as a differentiable physical layer imposing explicit hard constraints during forward propagation. This mechanism enables precise identification of the system’s nonlinear physical properties against high background noise. Experimental results show that the root-mean-square (RMS) value of the nonlinear error was reduced from 1.90 nm to 0.23 nm, with a compensation rate reaching up to 88.13%. This method provides a reliable framework for the intelligent calibration and error self-characterization of heterodyne interferometric industrial sensors in the field of precision metrology sensors.

Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods. To address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A multi-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thickness. Based on the measured data, a matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the requirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually deteriorates with increasing service mileage, and that flange height wear is the dominant factor affecting the wear grade. These findings are consistent with actual operating conditions. The proposed method integrates high-precision multi-parameter measurements with wear condition evaluation, providing a reliable technical basis for wheel condition monitoring and predictive maintenance in rail transit.

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes.

Train wheel wear is a critical factor affecting train operational safety, making the accurate and objective evaluation of wheel wear condition essential. However, current approaches are still constrained by inadequate measurement accuracy and incomplete evaluation methods, to address this issue, this study proposes an integrated method for the high-precision measurement and wear condition evaluation of train wheels. A mul-ti-sensor data fusion-based measurement method is developed to synchronously acquire key wear-related parameters, including wheel diameter, flange height, and flange thick-ness. Based on the measured data, an improved matter-element model combined with game-theoretic weighting is established to evaluate wheel wear condition. Experimental results show that the proposed online measurement method for in-service wheels achieves standard deviations below 0.15 mm, and the measurement errors satisfy the re-quirements of Chinese railway industry standards. The evaluation results derived from the high-precision measurement data indicate that wheel wear condition gradually dete-riorates with increasing service mileage, and that flange height wear is the dominant fac-tor affecting the wear grade. These findings are consistent with actual operating condi-tions. The proposed method integrates high-precision multi-parameter measurement with wear condition evaluation, providing a reliable technical basis for wheel condition moni-toring and predictive maintenance in rail transit.

The extraction of the light stripe center is a pivotal step in line-structured light vision measurement. This paper addresses a key challenge in the online measurement of train wheel treads, where the diverse and complex profile characteristics of the tread surface lead to uneven gray-level distribution and varying width features in the stripe image, ultimately degrading the accuracy of center extraction. To solve this problem, a region-adaptive multiscale method for light stripe center extraction is proposed. First, potential light stripe regions are identified and enhanced based on the gray-gradient features of the image, enabling precise segmentation. Subsequently, by normalizing the feature responses under Gaussian kernels with different scales, the locally optimal scale parameter (σ) is determined adaptively for each stripe region. Sub-pixel center extraction is then performed using the Hessian matrix corresponding to this optimal σ. Experimental results demonstrate that under on-site conditions featuring uneven wheel surface reflectivity, the proposed method can reliably extract light stripe centers with high stability. It achieves a repeatability of 0.10 mm, with mean measurement errors of 0.12 mm for flange height and 0.10 mm for flange thickness, thereby enhancing both stability and accuracy in industrial measurement environments. The repeatability and reproducibility of the method were further validated through repeated testing of multiple wheels.