Search Results (675)

Laser surface hardening is a technique that improves various mechanical characteristics of different materials. The methods are being extensively used in the automobile, aerospace, tool manufacturing, and construction industries for various components. The present review highlights the hardness and hardened surface depth improvement of different steels and non-ferrous alloys in as-bought and pre-heat treatment conditions. Diode and fibre lasers have rendered higher surface hardness and hardened depth, while consuming higher power. Nd:YAG lasers have resulted in a precise increase in hardness and a very minimal 0.8 in ferrous and 2 mm in surface-hardened depth of non-ferrous alloys, proving a better efficiency. The pre-heat treatments are selected to enhance mechanical properties and reduce the deformations and defects. An increase of 300.43 and 282.38% of surface hardness due to laser hardening as compared to the core material of AISI 420 was observed using a high-power diode laser. A huge 281.41% of increase in surface hardness was observed for ICD-5 tool steel using Nd:YAG lasers. The annealing pre-heat treatment has also affected the hardenability, resulting in high hardness. Non-ferrous alloys such as titanium and A356 alloys have recorded 200 and 125% increase in surface hardness compared to their core using Nd:YAG lasers. Full article

The article presents the calculation of temperature fields for a casting (a cylinder 20 mm in diameter) made of Al–5%wt.Cu alloy, poured into sand (sand–clay) and metal (steel) molds at a temperature of 1123 K (with a metal mold temperature of 523 K). Many existing analytical approaches do not explicitly account for key features such as the time-dependent temperature evolution at the casting surface and center, as well as the variable temperature gradient within the casting. In this paper, the parameters calculated for the sand mold include the surface temperature change over time, as do the dynamics of the solidification front progression, and ultimately, the overall thermal field of the casting. For the metal mold, the process first determines the change in the center temperature over time, followed by the surface temperature dynamics, and finally, the complete thermal field of the casting. Particular attention is paid to determining the position of the mushy zone, namely the zero fluidity and feeding temperatures (the point at which the liquid phase loses mobility upon cooling). These temperatures are critical for casting structure formation and the initiation of shrinkage defects. To perform the calculations, the authors developed original mathematical models and provided solutions to the resulting differential equations. The study demonstrates the differences between the thermal fields in sand and metal molds: the maximum temperature difference is 195 K in the sand mold, compared to 90 K in the metal mold. Therefore, the solidification conditions for this casting in the metal mold are more favorable. The metal mold provides more favorable thermal conditions and a lower analytically predicted tendency toward shrinkage defects, but it does not guarantee their complete absence. Full article

Accurate thermal analysis of steel solidification and heat transfer in the continuous casting mold is essential for understanding and controlling solidification, shell thickness uniformity, interfacial gap phenomena, and defects such as cracks and breakouts. This study investigates heat transfer in a funnel mold slab caster using the in-house thermal model, Con1D. A new methodology is introduced to predict the slag layer roughness, and its effect on interface resistance. To account for the multidimensional thermal behavior near water channels and thermocouples, finite-element models are developed in Abaqus to calibrate Con1D to match three-dimensional calculations of mold heat transfer. After calibration to match plant measurements for one set of casting conditions, Con1D predictions are validated with plant measurements at different casting speeds and mold plate thicknesses. Key outputs analyzed include the heat flux profile, mold and shell temperatures, shell thickness, shell shrinkage, and interfacial parameters such as slag layer thickness. Increasing casting speed causes higher heat flux, higher shell surface and mold temperatures, and decreased shell and slag layer thicknesses. Decreasing mold plate thickness increases heat flux slightly due to reduced thermal resistance of both the mold and interfacial gap. The modeling approach presented here is a powerful methodology to gain quantitative fundamental understanding of mold heat transfer in continuous casting, especially including phenomena in the interfacial gap. Full article

In this paper, we investigate the application of the relative entropy framework for safety assessments of steel elements with structural defects at the micro- and macro-scales. Mathematical theories developed by Bhattacharyya and by Kullback and Leibler (K-L) have been used for this purpose. This approach uses both expectations and variations, similar to the First-Order Reliability Method (FORM), but is extended to include 3rd- and 4th-order central probabilistic moments. It is necessary to use a hybrid computational technique that combines the Finite Element Method (FEM) software ABAQUS CAE 2017 with the implemented Gurson–Tvergaard–Needleman (GTN) damage model and the computer algebra system MAPLE. The iterative generalized stochastic perturbation technique has been used to determine the probabilistic moments of structural response, to utilize the Weighted Least Squares Method to approximate the structural response function, and to determine uncertainty in the stress, strain, and displacement state functions. This approach is based on relative entropy because of its universality. There is no need to assume a type of distribution of the state functions, in contrast to FORM, where a Gaussian distribution is required. This paper verifies whether relative entropy can serve as an alternative to FORM for determining reliability. The yield surface of the porous material with a random values of the void volume fraction f are also presented. Full article

The effect of nanosecond laser modification on X165CrMoV12 tool steel before and after heat treatment was investigated. Three laser texturing modes were applied to the studied material, with the variables being the frequency used and the pulse energy: 50 kHz/pulse energy 0.9 mJ, 100 kHz/pulse energy 0.45 mJ, and 150 kHz/pulse energy 0.3 mJ. The other parameters of laser texturing were power—90%; speed—500 mm/s; hatching angle—0° (horizontal), +60°/−60° (or equivalent 120°), and +30°/−30° (or equivalent 150°); and Hatching Distance—0.02 mm. The surface laser modification process aims to obtain a homogeneous and adaptive surface relief optimizing the operational properties of the working surfaces of the parts under dry contact friction conditions. The influence of the used laser modification modes on the roughness class of the obtained surfaces, the structure of the formed modified surface and the friction coefficient was studied. The comparative analysis showed that the lowest roughness class (Ra—4.123 µm) was obtained when using an operating frequency of 50 kHz. The obtained friction coefficient values were lowest in the following sequence of processes: laser texturing and subsequent thermal treatment. The lowest friction coefficient (µ = 0.0041) was registered in the test bodies processed with a mode in which the operating frequency was 50 kHz and the pulse energy was 0.9 mJ, after which they were subjected to thermal treatment according to the used cycle. In this processing sequence, no diffusion-related defects (decarburization) were observed on the surface layer of the tested steel. Full article

To address the issues of low accuracy, high false positives, and missed detections in hot-rolled strip steel surface defect inspection, this paper proposes an improved detection model named DFEM-NET based on YOLOv8n. First, an efficient feature extraction module (DSC2f) based on Dynamic Snake Convolution is designed to enhance the model’s capability in capturing features of irregular and elongated defects. Second, a Feature Pyramid Shared Convolution module (FPSC) is constructed to expand the model’s receptive field and effectively suppress interference from complex backgrounds. Third, an Enhanced Feature Correction (EFC) strategy is adopted during the feature fusion stage to help the model better learn the detailed features of small defect targets. Finally, a Multi-Scale Attention Aggregation module (MSAA) is introduced before the detection head, enabling the network to focus on critical feature information and thereby comprehensively improve detection accuracy for target defects. Experimental results demonstrate that, compared to the baseline model YOLOv8n, DFEM-NET achieves a detection accuracy (mAP@0.5) of 83.5%, representing an increase of 4.8%; a recall rate of 76.4%, an increase of 3.3%; and a precision of 84.7%, an increase of 3.1%, without a significant increase in model complexity. Furthermore, generalization experiments conducted on the GC10-DET dataset confirm that the proposed algorithm exhibits exceptional generalization capability. Full article

Laser-based Powder Bed Fusion (LPBF) enables the fabrication of complex metal components but often results in high porosity and microdefect densities, compromising fatigue performance despite acceptable static properties. Standard fatigue characterisation methods are time-consuming and costly and yield scattered results due to defect-induced brittleness and residual stresses. This study investigates the application of thermographic techniques as a rapid alternative for evaluating the intrinsic fatigue behaviour of tensile coupons fabricated by LPBF employing AISI 316L steel. By monitoring surface temperature during stepwise static monotone and fatigue loading, thermographic methods aim to detect early hints of heat dissipation associated with microdamage initiation. Approaches based on temperature harmonic analysis have been implemented, allowing near-real-time and full-field mapping of stress distribution and damage development. Results show that harmonic metrics correlate with the material state and effectively track the thermoelastic effect-induced temperature changes. Some evidence is found regarding the onset of intrinsic heat dissipation, which needs to be confirmed by more focused and extensive experimental tests. Full article

This study investigates the causes and mechanisms of a corrosion-induced perforation failure in P110 tubing from a nitrogen injection well in the Tahe Oilfield. A comprehensive analysis was performed using macroscopic examination, mechanical and chemical property testing, characterization of corrosion product morphology and composition, and electrochemical measurements. The results show that the tubing material met all relevant standard requirements, ruling out intrinsic material defects as a contributing factor. The primary cause of failure was the breakdown of the internal coating. Poor coating adhesion in the older tubing from the shallow section, combined with the tensile stress from the tubing’s suspended weight and the acidic service environment, led to coating blistering and disbondment, thereby exposing the underlying steel. In the presence of H₂S, CO₂, and O₂, severe electrochemical corrosion developed on the exposed metal surface. The process was further accelerated by a high concentration of Cl⁻, which promoted rapid localized corrosion and ultimately resulted in perforation. Based on these findings, several targeted mitigation strategies are proposed. These include optimizing the coating process to improve adhesion and modifying the corrosive environment. The recommendations provide practical guidance for corrosion control in similar oil and gas well applications. Full article

Surface defects in high-temperature continuous cast billets are critical factors affecting the quality of steel products. Owing to high-temperature radiation, heavy dust contamination, varying billet specifications, and background interference from oxide scales and water stains, existing online surface defect detection technologies for high-temperature continuous cast billets still suffer from limitations including high false-positive rates, inefficient identification of pseudo-defects, and the inability to simultaneously detect three-dimensional (3D) depth information alongside two-dimensional (2D) features. To solve these problems, this paper proposes a multi-dimensional online detection technology for surface defects in high-temperature continuous cast billets based on multi-information fusion. A four-channel multispectral image sensor and a corresponding three-light-source imaging system were developed. Furthermore, a defect sample augmentation method, a deep learning-based 2D recognition method, and a photometric stereo-based 3D reconstruction method were designed to mitigate problems of low detection accuracy and poor robustness caused by sample imbalance among different defect types. Finally, industrial applications were conducted on large-section continuous cast billets, beam blanks, and billets during the grinding process. According to the surface defect detection requirements of different continuous cast billets, multispectral multi-information fusion and traditional 2D defect imaging methods were adopted respectively. The results demonstrate high-precision online detection of surface defects in continuous cast billets, with favorable practical application effects. Full article

Quench–polish–quench (QPQ) of 40Cr steel was performed to improve its tribological properties and corrosion resistance, thereby enhancing the service performance of components such as gears and bearings. The 40Cr steel was treated by QPQ at 580 °C and 620 °C for 90 or 120 min. Optical microscopy (OM, Sunny Group, Ningbo, China), scanning electron microscopy (SEM, Hitachi, Tokyo, Japan), and X-ray diffraction (XRD Rigaku Corporation, Tokyo, Japan) were used to characterise the microstructure and phase constitution. Ball-on-disk tribometry, electrochemical tests, and salt spray tests in 3.5 wt.% NaCl evaluated surface performance. At 580 °C, a composite structure of Fe₃O₄ and ε-Fe₂₋₃N formed on the surface. When the temperature rose to 620 °C, ε-Fe_2–3N gradually transformed into γ′-Fe₄N. Within the scope of this study, the diffusion layer depth exhibits an approximately linear relationship with increasing processing temperature and holding time, and the surface hardness is 67–112% higher than that of the untreated sample. After QPQ treatment, the wear mechanism changed from adhesive wear to abrasive wear. However, under the treatment conditions of 620 °C × 120 min, brittle surface spalling increased roughness, thereby increasing the coefficient of friction. As treatment time increases, nitrogen atoms continue to diffuse outward as Fe₂N transforms to the γ′ phase. This increases the composite layer’s porosity and decreases its corrosion resistance. The best corrosion resistance was observed at 580 °C for 120 min, with a corrosion potential of −0.4325 V, corrosion current density of 1.80 × 10⁻⁶ A·cm⁻², and polarisation resistance of 24,500 Ω. Corrosion performance depends on overall surface integrity. Porosity morphology strongly influences this property. For 40Cr steel, the results show that surface properties are primarily determined by the quality of the compound layer’s microstructure. Specifically, density, phase-composition stability, and defect control are more important than the commonly held view of layer thickness. Full article

Accurate surface defect detection is essential for ensuring the measurement accuracy and assembly reliability of aero-engine components during manufacturing and assembly processes. Bearings, as critical rotating components in aero-engines, are highly sensitive to surface defects that may lead to stress concentration and premature failure. However, complex defect types, low-contrast textures, and multi-scale characteristics pose significant challenges for existing lightweight visual inspection models. To address these issues, this paper proposes an improved lightweight detection model, termed DMR-YOLO, based on YOLOv8n. A Diverse Branch Block (DBB) is introduced to enhance multi-scale feature extraction and improve the representation of complex defect patterns. A Multi-Level Channel Attention (MLCA) mechanism is embedded to strengthen discriminative feature channels and suppress background interference caused by low-contrast textures. In addition, a ResidualADown module is designed to preserve critical feature information during downsampling, improving the detection of subtle defects. Experimental results on a bearing surface defect dataset show that the proposed model achieves an mAP of 89.3%, representing a 2.8% improvement over YOLOv8n while maintaining real-time inference at 138.6 FPS. Moreover, generalization tests conducted on a steel surface defect dataset demonstrate the robustness and transferability of the proposed method across different datasets. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, and simplistic feature fusion strategies. In response to the above challenges, this paper proposed the Multi-Scale Frequency-Enhanced YOLO (MSFE-YOLO) algorithm that integrates multi-scale frequency domain enhancement with defect-aware attention mechanisms. First, a Multi-Scale Frequency-Enhanced Convolution (MSFC) module was constructed, which extracted multi-scale spatial features in parallel through depth-adaptive dilated convolutions, explicitly modeled high-frequency edge information using the Laplacian operator, and achieved adaptive fusion of multi-branch features via learnable weights. Second, a Cross-Stage Partial with Multi-Scale Defect-Aware Attention (C2MSDA) module was designed, integrating Sobel operator-based edge perception, multi-scale spatial attention, and adaptive channel attention to collaboratively enhance features across spatial, channel, and edge domains through a gated fusion strategy. Finally, an Adaptive Feature Fusion Enhancement (AFFE) module was proposed to achieve adaptive aggregation of multi-level features through a data-driven weight generation network and cross-scale feature interaction mechanism. Experimental results on the NEU-DET and GC10-DET datasets demonstrated that MSFE-YOLO achieved the mAP@0.5 of 79.8% and 66.7%, respectively, which were 1.7% and 2.1% higher than the benchmark model YOLOv11s respectively, while maintaining an inference speed of 89.3 FPS, which satisfied the real-time detection requirements in industrial scenarios. Full article

Although 45 steel is widely used in the manufacture of mechanical parts, its application in harsh working conditions is limited owing to its low hardness, poor wear resistance, and corrosion resistance. Laser cladding can enhance the performance of the working surface without sacrificing substrate toughness. CoCuNiTi HEACs with different cBN additions were successfully prepared on a 45-steel substrate. The phase structure, microstructure, elemental composition, wear, and corrosion behavior of CoCuNiTi + x cBN (x = 0.0, 0.5, and 1.0 wt.%) HEACs were investigated using XRD, OM, SEM, EDS, friction and wear tester, and electrochemical workstation, respectively. The results show that all three coatings exhibit a dual-phase structure composed of FCC and BCC phases. The addition of cBN transforms the alloy phase structure from the original FCC main phase to the BCC main phase. The incorporation of cBN significantly reduces the lattice constant and cell volume of the alloy phase. The change in the alloy phase density is negatively correlated with the cell volume. CoCuNiTi + x cBN (x = 0.0, 0.5, and 1.0 wt.%) alloys have a dendritic structure. No pores were observed in the cBN-containing sample. The content of Ti in the primary phase is the highest. Co is enriched in the dendrite region, and Cu is enriched in the interdendrite region. The significant reduction in the average segregation coefficient for cBN-containing samples is attributed to the heterogeneous nucleation of the alloy melt at lower undercooling levels and the significant increase in the diffusion rate. The friction coefficient of the alloy decreases significantly with increasing cBN content. The sample with 1.0 wt.% cBN shows the best wear resistance, mainly due to the combined effects of hard particle support, solid solution strengthening, phase interface reduction, and high thermal conductivity of cBN. The sample with 1.0 wt.% cBN has the largest capacitive arc radius and charge-transfer resistance, along with the lowest annual corrosion rate, indicating optimal corrosion resistance. This is primarily related to the reduction in pore defects caused by cBN addition, hindrance of uniform penetration of the corrosive medium by dispersed cBN particles, and increased complexity of the anodic dissolution process. CoCuNiTi HEACs reinforced by cBN can simultaneously improve the wear and corrosion resistance of the surface of the 45-steel substrate, providing a feasible strategy for the design of high-performance protective coatings. Full article

To address the challenges of steel surface defect detection—characterized by fine-grained textures, substantial scale variations, and complex background interference—conventional lightweight detectors often struggle to balance real-time navigation requirements with high-precision spatial localization on mobile inspection platforms. In this work, we propose KDM-YOLO, a lightweight visual localization and detection method built upon YOLOv10n, designed to provide an efficient perception engine for autonomous inspection robots. The proposed approach enhances the baseline through three key perspectives: feature extraction, context modeling, and multi-scale fusion. Specifically, KWConv is introduced to strengthen the representation of fine-grained texture and edge cues; C2f-DRB is employed to enlarge the effective receptive field and improve long-range dependency perception to reduce missed detections; and a multi-scale attention fusion (MSAF) module is inserted before the detection head to adaptively integrate spatial details with semantic context while suppressing redundant background responses. Ablation studies confirm that each module contributes to performance gains, and their combination yields the best overall results. Comparative experiments further demonstrate that KDM-YOLO significantly improves detection performance while retaining a compact model size and high inference speed. Compared with the YOLOv10n baseline, Precision, Recall and mAP@50 are increased to 91.0%, 93.9%, and 95.4%, respectively, with a parameter count of 3.29 M and an inference speed of 155.6 f/s. These results indicate that KDM-YOLO achieves an ideal balance between the accuracy and computational efficiency required for embedded navigation platforms, providing an effective solution for online autonomous inspection and real-time localization of steel surface defects. Full article