The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu’s method was used for interval thresholding of the first relevant mode, and a window was used to separate the flaw echoes in the relevant modes. The flaw echo was reconstructed by adding the residue and the separated flaw echoes. The established methodology was successfully employed for simulated signal and experimental signal processing. For the simulated signals, an improvement of 9.42 dB in the signal-to-noise ratio (SNR) and an improvement of 0.0099 in the modified correlation coefficient (MCC) were achieved. For experimental signals obtained from two cracks at different depths, the flaw echoes were also significantly enhanced.

A preliminary backscattered signal model of carbon-fiber-reinforced plastic (CFRP) laminate was established. The backscattered signal model was composed of three sub models, which were concerned with structural signal, scattering signal, and non-acoustic noise. Resonance in structural signal and echoes excited by defects (porosity and rich-resin) were studied. The results showed that: resonance would occur when there was sufficient bandwidth; when the CFRP laminate contained voids, the center frequency of the backscattered signal decreased; and the localized defects, including rich-resin and localized porosity, tended to generate apparent echoes where they located. A simplified backscattered signal model was subsequently put forward, showing certain potential in revealing time-frequency properties of backscattered signals. The newly proposed variational mode decomposition was used for defect modes extraction, successfully avoiding the mode mixing and false modes which easily exist in empirical mode decomposition. Subsequently, the generalized Stockwell transform was adopted for the defects localization. The simulation and experiment denoted the coincidence between the backscattered signal model and the experimental signal, and showed the effectiveness of variational mode decomposition and generalized Stockwell transform in localized defects detection.

In ultrasonic inspection of internal defects within multilayer media, traditional amplitude-based imaging methods frequently suffer from low signal-to-noise ratio (SNR) and inconsistent defect characterization, particularly for identically sized flaws at varying depths, primarily due to interface reflections and material attenuation. To address these challenges, a phase-coherent far-focused pixel-based (PC-FPB) imaging algorithm is proposed in this paper. This method constructs coherent imaging signals using a vector coherence factor and introduces a novel scan-line-based coherence modulation strategy. A dynamic threshold is statistically determined from the distribution of maximum coherence factors across scan lines and applied through a multiplicative fusion mechanism to selectively enhance coherent defect signals and suppress background noise. Experimental validation on multilayer structures demonstrates that the imaging intensity and SNR for deep-seated defects are significantly enhanced by the PC-FPB imaging algorithm. Comparative results further indicate that it outperforms conventional amplitude-based methods in the detection of deep defects. For single-hole imaging, the defect amplitude was improved by 9.8 dB and the image SNR increased by 7.7 dB. In multi-hole imaging, the maximum quantification error for defects with a diameter of 1 mm was recorded at 0.245 mm2, demonstrating a high degree of consistency in the imaging of identically sized defects at varying depths. Furthermore, the average SNR of defects at various depths increased by 15 dB and the average lateral resolution improved by 53.4%. The findings confirm that PC-FPB effectively overcomes the limitations of traditional methods and enhances defect detection in multilayer structures.

In order to measure the depth profile of the heat-treated case-hardened layer of linear guides, this paper proposes a B-scan imaging and 3D visualization method for detecting the depth profile of the case-hardened layer of linear guides based on the ultrasonic transverse wave backscattering technology. Firstly, by analyzing the generation mechanism of ultrasonic transverse waves and their advantages in material detection, and combining the differences in metallographic structure and hardness properties between the case-hardened layer and the base material, an ultrasonic transverse wave backscattering model for the case-hardened layer of linear guides was established. Then, an ultrasonic transverse wave detection experiment for the GH20 linear guide was designed and carried out to obtain the A-scan signals of the case-hardened layer depth at different positions on the cross-section of the linear guide. Finally, the A-scan signals obtained from the detection were used to generate the B-scan image of the case-hardened layer depth profile, and the 3D visualization of the case-hardened layer of the linear guide was achieved using Python and VTK tools. The experimental results show that the error between the measurement results of ultrasonic transverse waves and those of the metallographic method is 0.063 mm, and the detection results are within the allowable error range. This research provides an efficient, intuitive, and reliable technical method for detecting the depth of the case-hardened layer of linear guides in the industrial field.

In recent years, defect detection based on ultrasound B-scan images has been widely utilized in industry to detect the quality and presence of defects in products. However, there are still some difficulties in the process of processing B-scan images, such as sampling noise, the large amount of data, and so on. In this paper, we analyze the acoustic characteristics of an ultrasound B-scan image time series, design an image preprocessing method to make the image information gray-scale lossless, and propose a screening method for ultrasound B-scan image segments containing defects based on the theory of image entropy and a recurrence diagram. Comparison experiments between this method and the traditional image entropy screening algorithm show that this method can solve the above difficulties to a certain extent and has its own superiority. The method proposed in this paper provides a new idea for processing ultrasound B-scan image sequences and presents a new choice when the traditional method is limited.

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers.