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Metal flow tends to be complex and difficult to predict in the combined forward-backward extrusion (CFBE) process. Piercing and surface-crack defects are phenomenal in forming fasteners featuring a forward extruded pin and a backward extruded cup. In this work, a series of the CFBE tests with various combinations of the forward extrusion ratio (FER) and the backward extrusion ratio (BER) were conducted. A forming limit diagram, detailed with the piercing and surface-crack defects on the forward extruded pin or the backward extruded cup, was developed to provide a conception in choosing appropriate extrusion ratios in forming fasteners with such pin-and-cup features. With the aid of the forming load-stroke curves and the finite element analysis of fracture damage, the fracturing mechanism for the CFBE process was provided.

Purpose - The paper's aim is to investigate the effects of shot peening (SP) on the bending fatigue limit of high-strength steel containing an artificial semi-circular slit.Design methodology approach - SP and stress SP (SSP) were conducted on the specimens containing an artificial semi-circular slit with a depth of a=0.1, 0.2 and 0.3 mm. Then, bending fatigue tests were conducted on the specimens.Findings - The fatigue limit was improved by SP and SSP. In the case of SP and SSP specimens, the specimens with a semi-circular slit under a=0.2 mm fractured outside the slit, and they had considerably high fatigue limits. Therefore, a semi-circular slit with a depth of under a=0.2 mm could be rendered harmless by SP or SSP. It was found that the fatigue limit of specimens with a semi-circular slit that received SP or SSP was determined by the threshold condition for non-propagation of fatigue cracks that emanated from outside the slit. Whether the semi-circular slit is rendered harmless or not is decided by the relationship between the stress intensity factor range of semi-circular cracks and the threshold stress intensity factor range.Practical implications - The proposed method can be applied to mechanical parts used in vehicles, aircraft and trains.Originality value - There are very few examples of evaluations of fatigue limits after SP in materials containing crack-like surface defects. This study calcifies the effect of SP on the fatigue limit having crack-like surface defects.

In order to determine the causes of crack defects in Mn18Cr2 high-manganese wear-resistant steel plates, this paper conducted a systematic analysis of the steel plates’ microstructure, chemical composition, and hardness via metallographic microscopy, field-emission scanning electron microscopy, and Vickers hardness tester. The results indicated that there were folded cracks on the surface of the steel plate. The interior of the cracks was oxidized, and inclusions were observed in the crack gaps. A significant difference in the contents of Mn and Cr elements was detected at the defect locations, indicating that very obvious long-range diffusion of Mn and Cr elements had occurred during long-term high-temperature oxidation. The crack defects on the surface of the steel plate were related to the inheritance of the original cracks on the surface of the cast billet before rolling. There were cracks on the surface of the cast billet; the oxide scale and inclusions inside the cracks had not been completely removed. Multiple passes of rolling led to the cracks and oxide scale being pressed into the steel surface, thereby forming folding defects. The fine grain strengthening and deformation twinning generated by rolling deformation formed the hardened layer on the surface, resulting in higher surface hardness than core hardness. The austenite grain size inside the steel plate was in the range of 23–30 μm, and the hardness was around 275 HV. The grain size near the surface of the steel plate was around 10 μm. The surface hardness was 351 HV, which was higher than the core hardness of the steel plate.

Saw wood cracks are defects that affect the appearance and mechanical strength of sawn wood. Crack defects in the surface of sawn wood can be readily detected. Decisions regarding the presence and severity of such defects can affect the utilization rate of sawn timber. Due to the heavy workload, low efficiency, and low accuracy of manual inspection, traditional machine learning methods have strong specialization, complex methods, and high costs. By studying the semantic segmentation model of surface crack defects in sawn timber based on deep learning, the optimal model for segmentation and detection of surface cracks in sawn timber was established. The improved Attention U-Net model encoding stage was introduced into CBAM, and AdamW optimization was used instead of SGD and Adam to achieve better crack semantic segmentation results. The ECA module was introduced in the skip connection part, and the weighted fusion multi loss function was used instead of the original cross entropy loss function. The positions of the two modules were replaced to improve the accuracy of semantic segmentation of surface cracks in sawn timber. Through comparative experiments, the improved model also achieved higher scores in semantic segmentation indicators for surface cracks in sawn timber compared to other models.

Detecting metal surface crack defects is of great significance for the safe operation of industrial equipment. However, most existing mainstream deep-object detection models suffer from complex structures, large parameter sizes, and high training costs, which hinder their deployment and application in frontline construction sites. Therefore, this paper optimizes the existing YOLO series head structure and proposes a lightweight detection head structure, ZoomHead, with lower computational complexity and stronger detection performance. First, the GroupNorm2d module replaces the BatchNorm2d module to stabilize the model’s feature distribution and accelerate the training speed. Second, Detail Enhanced Convolution (DEConv) replaces traditional convolution kernels, and shared convolution is adopted to reduce redundant structures, which enhances the ability to capture details and improves the detection performance for small objects. Next, the Zoom scale factor is introduced to achieve proportional scaling of the convolution kernels in the regression branch, minimizing redundant computational complexity. Finally, using the YOLOv10 and YOLO11 series models as baseline models, ZoomHead was used to replace the head structure of the baseline models entirely, and a series of performance comparison experiments were conducted on the rail surface crack dataset and NEU surface defect database. The results showed that the integration of ZoomHead effectively improved the model’s detection accuracy, reduced the number of parameters and computations, and increased the FPS, achieving a good balance between detection accuracy and speed. In the comparative experiment of the SOTA model, the addition of ZoomHead resulted in the model having the smallest number of parameters and the highest FPS, while maintaining the same mAP value as the SOTA model, indicating that the ZoomHead structure proposed in this paper has better comprehensive detection performance.

Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, due to human physiology, the assessment limits the accuracy of image evaluation, making it more subjective rather than objective. Thus, in this study, a multivariant defect recognition technique was developed to efficiently assess the various structural health issues of concrete. The image dataset used was comprised of 3650 different types of concrete defects, including surface cracks, delamination, spalling, and non-crack concretes. The proposed scheme of this paper is the development of an automated image-based concrete condition recognition technique to categorize, not only non-defective concrete into defective concrete, but also multivariant defects such as surface cracks, delamination, and spalling. The developed convolution-based model multivariant defect recognition neural network can recognize different types of defects on concretes. The trained model observed a 98.8% defect detection accuracy. In addition, the proposed system can promote the development of various defect detection and recognition methods, which can accelerate the evaluation of the conditions of existing structures.

The threshold stress intensity factor ( Δ K th ) has dependent on the crack size during the propagation of microfatigue cracks. Ando et al. proposed a new surface crack nondamaging technology to evaluate this dependence. This technology has been validated in situations where the stress ratio is positive ( R  > 0). However, to expand the application range of this technology, it is necessary to verify its applicability in the case of stress ratio R  < 0. Therefore, this applicability was examined based on the results of the previous experiments. This study aims to investigate the effect of the residual stress distribution (RS1) of cavitation peening (CP) in the case of R  < 0 on the harmless crack size ( a hml ) of steel with crack-like surface defects. To compare with the residual stress distribution (RS1) of CP, two types (RS2 and RS3) with different residual stress distributions were used. The surface residual stresses of RS1, RS2, and RS3 were the same, but the distribution of depth from the surface was different. Compressive residual stress is the dominant factor that renders surface defects harmless. The depth of the residual stress significantly influenced a hml . Moreover, the trend of the defect size that can be rendered harmless, estimated based on fracture mechanics, was slightly larger than that of the experimental results. However, surface crack nondamaging technology can be applied even when the stress ratio is negative ( R  < 0).

Basin insulator is a critical component of gas-insulated transmission line (GIL) systems. Air gap defects and surface crack defects may form in basin insulators due to casting, installation, or transport processes. This phenomenon poses a significant threat to long-term safety and stability and may even lead to partial discharges. This study establishes a simulation model of a GIL system-incorporating insulator to systematically analyze the influence patterns of various defects on the insulation characteristics of the basin insulator. Meanwhile, an equation predicting the relationship between defect size and maximum electric field strength is derived. The research revealed the following: For short air gap defects near the conductor, increasing length reduces their impact on the surrounding electric field, with the radius having minimal effect; for long air gap defects near the conductor, increasing length amplifies their influence. Smooth air gap defects distant from the conductor show negligible variation in maximum electric field strength with increasing length, while unsmooth air gap defects exhibit more pronounced effects at shorter lengths. Under identical conditions, unsmooth air gap defects demonstrate greater influence on the electric field than smooth ones. For elliptical surface defects, variations in radius show the strongest distortion. The degree of influence from surface crack defects correlates directly with their proximity to the conductor. These findings provide critical diagnostic criteria for assessing the insulation performance of basin insulator under damaged conditions.

Under random wave loading, the crack growth rate exhibits jump-like cycle-to-cycle variations, which limit the direct use of efficient integration schemes such as the Euler method. In addition, the crack growth life is highly sensitive to the initial crack size and aspect ratio, while the initial defects are often difficult to determine accurately in practice, leading to increased uncertainty in life assessment. To address these issues, a cycle-scaling-based crack size accumulation method for random loading is proposed. A predictor–corrector improved Euler method is then established, and a fourth-order Runge–Kutta scheme incorporating the cycle-scaling transformation is derived. Furthermore, based on spectral analysis theory, a mapping between the wave spectrum and the crack-tip stress intensity factor response spectrum is developed. A stress intensity factor range sequence is generated by concatenating short-term sea states, thereby providing a random loading input that preserves the required statistical characteristics. Finally, a 21,000-TEU container ship is analyzed as a case study to investigate crack growth evolution for different initial aspect ratios. The results show that the crack aspect ratio gradually converges to a particular trend during propagation. A convergent aspect ratio curve is fitted. And a unified life assessment curve is constructed. An equivalent transformation is used to map an arbitrary initial crack shape and size to an equivalent convergent aspect ratio crack. As a result, fatigue life can be rapidly estimated using a single “initial crack size–fatigue life” curve, providing support for crack growth life assessment and the definition of defect acceptance limits for ship hull structures.

Fatigue lifetime of offshore pipelines with semi-elliptical circumferential surface cracks is often underestimated. An accurate prediction of the pipeline structural integrity is nevertheless important in order to prevent unnecessary and expensive downtime, failures leading to leakage or spillage of pipeline contents to the surrounding environment, and ultimately improve the reliability of the pipeline. The estimation of crack growth in pipelines under varying loads is highly dependent on the calculation of crack driving parameters, such as the stress intensity factor and the crack tip opening displacement (CTOD) using the 3D J-integral or its equivalent. This paper presents a numerical study to predict the fatigue lifetime of cracks in pipes, determining the J-integral that includes first and second derivatives of the displacement field for pipes containing a range of circumferential surface cracks. A pipe segment is structurally loaded and stress intensity factors (SIF) evaluated using the finite element method (FEM). Based on the results, a number-of-cycles to failure curve shows a longer lifetime than previously predicted by about 5% for a pipe with semi-elliptical external surface cracks. In addition, they indicate that the external short cracks are more dangerous than the internal long surface crack hereby requiring earlier assessment.