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This paper presents Fix-YOLOX (Fixmatch-You Only Look Once X), a semisupervised target detection model that uses a small amount of annotated data for fully supervised training, and adds a semisupervised training module using both pseudolabelling and consistent regularization to prevent overfitting in fully supervised training by using unlabelled data. Additionally, the generalization of the model and its fault tolerance to labelled data are improved. The experimental results show that the proposed semisupervised visual detection algorithm, Fix-YOLOX, can substantially reduce the amount of data annotation required for the target detection task while effectively overcoming the problem caused by annotated data with uneven quality. The YOLOX model achieves 91.95% accuracy with 50% annotated data and an average detection time of 10.4 ms per image/frame, which is consistent with the detection time of original YOLOX. Therefore, the model has good real-time performance and generalizability. [ABSTRACT FROM AUTHOR]

Soft-brittle crystals such as KH2PO4 (KDP) pose significant challenges to traditional machining techniques due to their inherent brittleness and substantial sizes. To surmount these challenges, this paper presents the water dissolution combined continuous polishing (WDCCP) method based on the water dissolution principle for achieving high-efficiency and high-quality machining. The WDCCP process is a three-step methodology including initial precision polishing with an aqueous solution, subsequent ultra-precision polishing utilizing a water-in-oil microemulsion, and final cleaning with anhydrous ethanol. Comprehensive analyses of the slurry's microstructure and viscosity are conducted to reveal the surface planarization mechanism. Multiple slurry dispensers ensure a more uniform distribution of various slurries across the interface between the KDP crystal and the pad, thereby enhancing surface quality. Experimental trials conducted on a large numerical control polishing machine demonstrate that precision polishing can eliminate sawn surface defects within a mere 5 min. The ultra-precision polishing significantly reduces the surface roughness root mean square (rms) from 474.974 to 2.779 nm and the peak-to-valley (PV) from 29.859 to 48.949 nm. After WDCCP, the integral morphology of a 180 mm × 180 mm KDP crystal exhibits a PV of 1.938 waves and an rms of 0.264 waves. The WDCCP method emerges as a potent and efficient solution for the machining of large-sized soft-brittle crystals. [ABSTRACT FROM AUTHOR]

As the core component of sandwich‐like perovskite solar cells (PSCs), the quality of perovskite layer is a challenge for further progress in PSCs due to the unfavorable defects and uncontrollable crystallization. Here, a surface post‐treatment strategy employing ethyl thioglycolate (ET) as ligand molecule is developed for property manipulation of perovskite films. ET can lower surface energy of perovskite facets and induces secondary growth of grains, giving films with higher crystallinity and lower defect density. Meanwhile, both carbonyl and sulfhydryl in ET can bind to the Pb2+, thus forming bidentate anchoring on the surface for defect passivation. Besides, the perovskite/ET/C60 interface presents improved charge transfer owing to the well‐aligned energy levels. Consequently, the power‐conversion‐efficiency (PCE) is boosted to 22.42% and 23.56% (certified 23.29%) for the FA0.85Cs0.15Pb(I0.95Br0.05)3 and FA0.9MA0.05Cs0.05Pb(I0.95Br0.05)3 PSCs, respectively, and the FA0.85Cs0.15Pb(I0.95Br0.05)3‐based PSC with a larger area (1.03 cm2) delivers a PCE of 20.01%. Importantly, ET demonstrates effective management of I2 and PbI2, thereby preventing accelerated degradation and lead leakage of devices. Thanks to the multiple effects of ET, the resulting devices exhibit significantly enhanced ambient stability over a course of 800 h, and a thermal stability of over 1500 h while maintaining 80.4% of its original efficiency. [ABSTRACT FROM AUTHOR]

Photoconductors featuring an individual micro/nano‐crystal as the photoactive channel hold the great promise for next‐generation integrated optoelectronics. Meanwhile, the gain aiming for signal amplification without an extra amplifier, the fast response for fast data transfer, and low dark current for weak signal acquisition are highly desirable in integrated optoelectronics, but are inherent trade‐off in traditional photoconductors. Herein, the study demonstrates a single‐microplate MAPbI3 photoconductor based on polyvinyl alcohol‐filled planar electrodes that can simultaneously achieve excellent performance on these parameters. The superior performance can be attributed to the exquisite design of filling the device channel gap with polyvinyl alcohol. This deep‐work‐function transparent polyvinyl alcohol can not only passivate surface defects of the perovskite microplate by hydrogen bonding, but also form a heterojunction with top perovskites to induce a depleted channel. Resultant single‐microplate photodetectors exhibit exceptional performance characteristics including ultra‐low noise‐equivalent power of 0.19 fW Hz−1/2, high specific detectivity of 3.68 × 1014 Jones, fast response of 3.2 µs, high EQE‐bandwidth over 107 Hz, as well as excellent stability. More importantly, single‐microplate MAPbI3 photoconductors demonstrate ultra‐weak‐light imaging ability surpassing silicon counterparts. Furthermore, the successful integration of their microplate pixels showcases the promising prospect for integrated optoelectronics. [ABSTRACT FROM AUTHOR]

Laser metal deposition is a promising way to repair the surface defects of single-crystal components in turbo engines. Understanding the mechanisms and improving the efficiency of the repair have been long-standing problems. In this study, the influence of the substrate orientation on the laser metal deposition (LMD) was investigated and its effect on repair layer-step was examined. LMD experiments were conducted on single crystal superalloys with a normal substrate orientation (001)/[100] and with an optimized substrate orientation (101)/[10 1 ¯ ]. It reveals that the laser cladding with the optimized orientation leads to a larger height of the [001] dendrite region than that with the normal orientation. The calculated results of the growth velocity, thermal gradient, and susceptibility to CET in the dendrite-preferred growth direction indicate that, for the (101)/[10 1 ¯ ] orientation, the [001]/[100] boundary is located at relative high position in each layer, which not only decreases the formation ability of stray grain significantly, but also eliminates the appearance of the maximum susceptibility. This makes the necessary dilution position much higher, and thus, a large cladding step can be selected. Our findings could find potential applications in laser repair of single-crystal components. [ABSTRACT FROM AUTHOR]

The fatigue performance of laser powder bed fusion-fabricated Ti-6Al-4V alloy was investigated using four-point bending testing. Specifically, the effects of keyhole and lack-of-fusion porosities along with various surface roughness parameters, were evaluated in the context of pore circularity and size using 2D optical metallography. Surface roughness of Sa = 15 to 7 microns was examined by SEM, and the corresponding fatigue performance was found to vary by 102 cycles to failure. The S–N curves for the various defects were also correlated with process window examination in laser beam power–velocity (P–V) space. Basquin's stress-life relation was well fitted to the experimental S–N curves for various process parameters except keyhole porosity, indicating reduced importance for LPBF-fabricated Ti-6Al-4V alloy components. [ABSTRACT FROM AUTHOR]

This study investigates the effects of a back plate preheating assistance system and deep rolling (DR) on axial force and tunnel defects during friction stir welding (FSW). Different preheating configurations—advancing side (AS), retreating side (RS), and both sides—were examined to evaluate their impact on axial force reduction, temperature distribution, and defect minimization. Axial force measurements were taken using a dynamometer, and temperature histories were recorded with a thermal camera. The results demonstrate that a preheating temperature of 200 °C is optimal, reducing axial force by 30.24% and enhancing material flow. This temperature also facilitated deeper tool penetration, especially when preheating was applied to both sides. Preheating on the AS resulted in the smallest tunnel defects, reducing defect size by 80.15% on the RS and 96.91% on the AS compared to the non-preheated condition. While DR further reduced tunnel defects, its effectiveness was limited by the proximity of defects to the surface. These findings offer significant insights for improving the FSW process. [ABSTRACT FROM AUTHOR]

Because the fabrication of three-dimensional (3D) microelectrodes is quite challenging, microelectrodes with a simple cross-section are usually used in micro-electro-discharge machining (micro-EDM) to perform layer-by-layer scanning machining for machining 3D microstructures. In this study, we used pure copper powder as the raw material to prepare 3D queue microelectrodes through selective laser melting (SLM). The influences of laser power and laser scanning speed on the densification rate of the 3D microelectrodes were investigated. Moreover, the effects of the temperature and temperature holding time on the conductivity and EDM performance of the 3D microelectrodes during heat treatment were also analyzed. In order to improve the geometric accuracy of the 3D microstructure, 3D microstructures were subjected to micro-EDM with 3D queue microelectrodes: the first microelectrode was used for rough machining and the remaining microelectrodes were used for fine machining. In addition, to ensure a high surface quality of the 3D microstructure, multiple EDM were conducted to eliminate unmelted copper powder and layer defects on the surface of the 3D queue microelectrode. Finally, by applying the 3D queue microelectrodes in micro-EDM, a 3D microstructure with a height error of 1 µm and surface roughness of 1.685 µm was obtained after three repeated discharges. [ABSTRACT FROM AUTHOR]

Tin oxide (SnO2) has demonstrated significant potential as an electron transport layer (ETL) owing to its low‐temperature processing in perovskite solar cells (PSCs). However, the poor energy‐level alignment and the presence of interface defects between the SnO2 and perovskite layer aggravate the power conversion efficiency (PCE) of the PSCs. Herein, heterovalent samarium cation (Sm3+) is deliberately doped into SnO2, optimizing the energy‐level alignment between SnO2 and the perovskite layer, and effectively passivating the oxygen vacancy defects on the surface of SnO2. Experimental and theoretical conclusions reveal that Sm‐doping successfully passivates the defects in the ETL and improves the perovskite crystal quality, thereby reducing interface charge recombination, and enhancing electron extraction from perovskite to the SnO2 layer. Consequently, the optimized Sm‐doped SnO2‐based PSCs achieve a PCE of 24.10% with a VOC of 1.174 V, negligible hysteresis, and improved durability under ambient conditions. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep learning. The method encompasses the entire process from data preprocessing and model training to validation analysis and innovation optimization with the goal of deepening students' understanding of AI technology and enhancing their ability to apply it to real-world scenarios. We have designed an experimental framework that incorporates the Efficient Multi-Scale Attention (EMA) mechanism into the Backbone network. This approach helps students understand the principles of feature extraction and the core functions of attention mechanisms. Additionally, we introduced a novel architecture—Convolution 3 Dilated Convolution X (C3DX)—into the Neck network. This architecture effectively expands the network's receptive field, improves its ability to capture multi-scale information, and thus enhances defect detection accuracy. Furthermore, the implementation of the Efficient Intersection over Union (EIoU) loss function optimizes the bounding box predictions, further increasing the model's accuracy and robustness. Overall, the teaching design not only ensures that the content remains at the cutting edge of technology but also emphasizes its practicality and operability. This approach enables students to effectively apply theoretical knowledge to real-world engineering projects. [ABSTRACT FROM AUTHOR]

Strip steel serves as a crucial raw material in numerous industries, including aircraft and automobile manufacturing. Surface defects in strip steel can degrade the performance, quality, and appearance of industrial steel products. Detecting surface defects in steel strip products is challenging due to the low contrast between defects and background, small defect targets, as well as significant variations in defect sizes. To address these challenges, a two-stage attention-based feature-enhancement network (TAFENet) is proposed, wherein the first-stage feature-enhancement procedure utilizes an attentional convolutional fusion module with convolution to combine all four-level features and then strengthens the features of different levels via a residual spatial-channel attention connection module (RSC). The second-stage feature-enhancement procedure combines three-level features using an attentional self-attention fusion module and then strengthens the features using a RSC attention module. Experiments on the NEU-DET and GC10-DET datasets demonstrated that the proposed method significantly improved detection accuracy, thereby confirming the effectiveness and generalization capability of the proposed method. [ABSTRACT FROM AUTHOR]

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Developing biochar with large specific surface area (SSA), heteroatom doping, and porous structure is attracting substantial attention to absorb electromagnetic wave (EMW) in recent. Herein, a novel method of ethanol and KOH co‐treatment is used to produce the biomass carbon deriving from pitaya peels. The obtained carbon possesses the high SSA of 1580 m2/g, successful N/O atoms co‐doping, and massive pores with different size. The results of EMW absorption measurement show that the prepared biochar could achieve over 99 % absorpition to EMW, which the highest reflection loss is of ca. −45.25 dB at 7.54 GHz with an effective absorption bandwidth (EAB) of ca. 4.87 GHz. The execellent microwave absorption property is caused by the surface defects, dipole and interface polarizations of the synthesized biochar owning unique microstructure and N/O atoms co‐doping. Hence, this avenue provides a new reference for fabricating low‐cost and eco‐friendly biochar as a microwave absorber. [ABSTRACT FROM AUTHOR]

Cesium zirconium halide (e.g., Cs2ZrCl6:Te or CZCT) perovskites have garnered significant interest due to their unique optical properties. However, there have been no reports of CZCT perovskite nanoparticles (PNPs) that simultaneously offer high efficiency and thermal stability. Here, a comprehensive defect suppression strategy is reported to achieve CZCT PNPs with these attributes. First, the inner defects of CZCT perovskite microcrystals (PMCs) are minimized by controlling crystallization kinetics precisely. Second, the PNPs are obtained from PMCs via top‐down fabrication, and the surface defects of PNPs are passivated via the hydroxyl groups of alkyl‐terminated silica‐oligomer shell (ASO). The resulting CZCT@ASO PNPs show the highest photoluminescence quantum yield (PLQY) of 96% and high thermal stability among the reported conventional CZCT emitters. Finally, white light‐emitting diodes (WLEDs) are integrated by using CZCT@ASO PNPs as the down‐color converters, achieving a color coordinate of (0.31, 0.33) and a color rendering index of 86. These results demonstrate that core/shell CZCT@ASO PNPs have great potential as phosphors for lighting applications. [ABSTRACT FROM AUTHOR]

As an electron transport layer (ETL) widely used in organic solar cells (OSCs), ZnO has problems with energy level mismatch with the active layer and excessive defects on the ZnO surface, which can reduce the efficiency of OSCs. Here, ZnO/ZrSe2 composite is fabricated by modifying ZnO with 2D ZrSe2. The XPS and first‐principles calculation (FPC) show that ZnO obtains electrons from ZrSe2 and forms interfacial dipoles toward the active layer, which decreases the work function of ZnO, thus reducing the interface barrier and favoring the collection of electrons in OSCs. At the same time, after ZrSe2 modification, the oxygen vacancy density on the ZnO surface decreases, thus improving the conductivity of ZnO. More importantly, the femtosecond transient absorption (Fs‐TA) shows that ZrSe2 selectively traps holes from the active layer, which prevents the holes from entering the ZnO, thereby reducing the probability of electron recombination. Finally, ZnO/ZrSe2 composite is used as a novel ETL in OSCs with PBDB‐T: ITIC, PM6:Y6 and PM6: L8‐BO as active layers, and obtaining 12.09%, 16.34%, and 18.24% efficiency, respectively. This study provides a method for the interface modification of ZnO, and further investigates the role of 2D nanosheets in the interface modification. [ABSTRACT FROM AUTHOR]

Interface engineering has become the mainstream for improving the performance of perovskite solar cells (PSCs). Interfacial dipole (ID) molecules have emerged as a feasible and effective strategy to alleviate the charge carrier loss and energy loss in PSCs. Here, the three symmetrical donor–acceptor interfacial dipole molecules (named PzT, PzTE, and PzTN) are designed and synthesized with identical hole transport backbone and different anchoring groups. The ID molecule is introduced into the interface between the perovskite layer and the hole transport layer. The dipole moments of ID molecules regulate the surface work function and energy‐level alignment of perovskites, improve charge extraction, and reduce energy loss at the interface. Meanwhile, the anchoring groups of ID molecules coordinate with the defects on the surface of PVK and HTL, reduce interfacial trap state density and charge accumulation, and mitigate the carrier non‐radiative recombination losses. As a result, PzTN‐modified PSC achieved a champion power conversion efficiency of 25.34% with a photovoltage of 1.176 V and a fill factor (FF) of 83.27%, accompanied by almost undetectable hysteresis and excellent operating stability. This research demonstrates a feasible strategy for efficient and stable PSCs by interfacial dipole molecules. [ABSTRACT FROM AUTHOR]

The development of cadmium‐free blue quantum dots (QDs) is of paramount importance to the display industry. In this study, high‐quality ZnSeTe/ZnSe/ZnS blue QDs, followed by surface treatment with ZnI2 are initially synthesized. The introduction of ZnI2 passivated the surface defects, resulting in an increase in the fluorescence quantum yield. The time‐resolved photoluminescence (TRPL) demonstrates a significant inhibition of non‐radiative recombination associated with the surface defect state. The density functional theory (DFT) calculation reveals that the binding energy between iodine ions and zinc ions is higher than that between oleate ions and zinc ions, providing a theoretical basis for the effective passivation of the suspended bonds of zinc ions on QDs' surface by iodine ions. Moreover, quantum dot light‐emitting diodes (QLEDs) are fabricated and UV photoelectron spectra (UPS) indicate the hole injection barrier between the hole transport layer and QDs decreases 0.12 eV after QDs being treated by ZnI2, facilitating hole injection. Finally, The ZnI2‐treated QLED demonstrates a 1.57‐fold and 1.82‐fold improvement in Lmax and EQEmax, respectively, reaching 6370 cd m−2 and 9.1%, compared to the pristine QLED. The work serves as a valuable reference for enhancing the performance of cadmium‐free blue QLED. [ABSTRACT FROM AUTHOR]

Lead halide perovskite nanocrystals (LHP‐NCs) incorporated within polymer matrices have emerged as promising materials for various photonic applications. However, challenges persist in achieving high‐quality nanocomposites due to low monomer conversion yields, restricted LHP‐NCs loadings, and difficulty in maintaining NCs integrity post‐polymerization. A novel protocol for synthesizing LHP‐NCs/poly(methyl methacrylate) nanocomposites in a single step via the NC‐initiated photoinduced electron transfer‐reversible addition‐fragmentation chain transfer (PET‐RAFT) method is presented. Polymerization initiation mediated by NCs surfaces under blue light enables the fabrication of homogeneous nanocomposites with NCs loadings up to 7% w/w and ≈90% monomer conversion even in the presence of oxygen. This process preserves the optical quality of the NCs and passivates NCs surface defects, resulting in nanocomposites exhibiting near unity luminescence efficiencies. The potential of this approach for producing highly loaded nanocomposites for radiation detection is validated by radioluminescence measurements showing light yield values of 6000 ph MeV−1 and fast scintillation dynamics with effective lifetime of 490 ps, showing promise for time‐of‐flight radiometry. [ABSTRACT FROM AUTHOR]

High thermal conductivity graphite films (GFs) have a thermal conductivity, which have attracted much attention in recent years. As the surface of GFs is extremely smooth, the resin matrix and GFs are only connected by van der Waals forces, and it is difficult to produce chemical bonds between them, resulting in poor mechanical properties of the composites. In this study, the composites were prepared by etching defects on the surface of GFs by high‐temperature heat treatment. It was determined that the critical temperature range for effective etching was between 600 and 650°C. The GFs were subjected to high‐temperature heat treatment to create circular, hexagonal, and disordered groove‐like defects, and then combined with prepregs and bismaleimide resins in a single molding process. The composites exhibit a distinct layered "sandwich" structure, where surface‐induced defects enhance the interfacial adhesion between GFs and the resin matrix, creating a "nail‐punched" structure. The thermal conductivity of the S‐650 sample in the X‐direction was up to 1187.93 W/(m·K), and the flexural strength of the S‐800 sample was up to 116.18 MPa. The prepared composites are expected to be widely used in thermal interface fields such as heat exchangers, battery heat dissipation and aerospace thermal control systems. Highlights: High‐temperature treatment was used to successfully introduce defects into GFs.The formation mechanisms of the three types of defects were explained.The thermal conductivity of the composites was increased to over 1000 W/(m·K).Composites reinforced with modified GFs exhibit enhanced mechanical properties. [ABSTRACT FROM AUTHOR]

Introduction: Chemical special steels are widely used in chemical equipment manufacturing and other fields, and small defects on its surface (such as cracks and punches) are easy to cause serious accidents in harsh environments. Methods: In order to solve this problem, this paper proposes an improved defect detection algorithm for chemical special steel based on YOLOv8. Firstly, in order to effectively capture local and global information, a ParC2Net (Parallel-C2f) structure is proposed for feature extraction, which can accurately capture the subtle features of steel defects. Secondly, the loss function is adjusted to MPD-IOU, and its dynamic non-monotonic focusing characteristics are used to effectively solve the overfitting problem of the bounding box of low-quality targets. In addition, RepGFPN is used to fuse multi-scale features, deepen the interaction between semantics and spatial information, and significantly improve the efficiency of cross-layer information transmission. Finally, the RexSE-Head (ResNeXt-Squeeze-Excitation) design is adopted to enhance the positioning accuracy of small defect targets. Results and discussion: The experimental results show that the mAP@0.5 of the improved model reaches 93.5%, and the number of parameters is only 3.29M, which realizes the high precision and high response performance of the detection of small defects in chemical special steels, and highlights the practical application value of the model. The code is available at https://github.com/improvment/prs-yolo. [ABSTRACT FROM AUTHOR]

An advanced scanning probe microscopy system enhanced with artificial intelligence (AI‐SPM) designed for self‐driving atomic‐scale measurements is presented. This system expertly identifies and manipulates atomic positions with high precision, autonomously performing tasks such as spectroscopic data acquisition and atomic adjustment. An outstanding feature of AI‐SPM is its ability to detect and adapt to surface defects, targeting or avoiding them as necessary. It is also designed to overcome typical challenges such as positional drift and tip apex atomic variations due to the thermal effects, ensuring accurate, site‐specific surface analysis. The tests under the demanding conditions of room temperature have demonstrated the robustness of the system, successfully navigating thermal drift and tip fluctuations. During these tests on the Si(111)‐(7 × 7) surface, AI‐SPM autonomously identified defect‐free regions and performed a large number of current–voltage spectroscopy measurements at different adatom sites, while autonomously compensating for thermal drift and monitoring probe health. These experiments produce extensive data sets that are critical for reliable materials characterization and demonstrate the potential of AI‐SPM to significantly improve data acquisition. The integration of AI into SPM technologies represents a step toward more effective, precise and reliable atomic‐level surface analysis, revolutionizing materials characterization methods. [ABSTRACT FROM AUTHOR]

Monitoring the number of defects in constant‐size units is a well‐defined problem in the industrial domain and usually, the c$c$ control chart is used for monitoring the total number of defects in a product or a sample of products. The c‐chart tracks the total number of defects in each case by assuming that the underlying number of defects (single or several different types of defects) follows approximately the Poisson distribution. An interesting class of problems where the c$c$‐chart is used is when the number of defects in a surface is of interest. Although the number of defects on the surface of products characterizes the quality of the products, it is especially important how concentrated the defects are in specific parts of the product. In this paper, we introduce a scan‐based monitoring procedure, which simultaneously combines control charts for monitoring the evolvement of the number of defects (in general, events) through time and scan statistics for exploring the spatial distribution of defects. The numerical illustration showed that the new procedure has excellent performance under different scenarios. [ABSTRACT FROM AUTHOR]

The performance of perovskite solar cells (PSCs) is often constrained by significant open‐circuit voltage (VOC) losses attributed to non‐radiative recombination processes induced by detrimental trap states. Surface treatments using passivating ligands typically involve single active binding sites on perovskite, posing challenges for effective passivation. Here, an aromatic donor‐acceptor (D‐A) configured phthalocyanine treatment is proposed to aim at dual‐site passivation of uncoordinated lead ions and effective mitigation of shallow and deep‐level defects on the perovskite surface. The resulting benign p‐type surface facilitates a more favorable energy level alignment and reduces energetic mismatches at the perovskite/Spiro‐OMeTAD interface. Pc‐BTBC, with its aromatic D‐A configuration, demonstrated compatibility with various perovskite compositions. Optimized PSCs achieves a power conversion efficiency (PCE) of 25.15% and reduces the VOC deficit to 0.379 V. Furthermore, encapsulated devices exhibited enhanced stability under damp‐heat conditions (ISOS‐D‐2, 50% RH, 65°C) with a T92 of 1000 h and maintained maximum power point tracking under continuous light in ambient air at 65°C (ISOS‐L‐2). Notably, fabricated wide‐bandgap semitransparent PSCs (ST‐PSCs) achieved a PCE of 20.29%, while four‐terminal perovskite/silicon tandem solar cells (4T‐P/STSCs) demonstrated an efficiency of 29.38%. This study provides insights into minimizing VOC losses and represents significant progress toward commercializing perovskite photovoltaics. [ABSTRACT FROM AUTHOR]

In this work, we report a simple yet robust strategy for eliminating surface defects in red-emitting fluorides through solid–liquid interface friction under elevated temperature and high pressure. This method is minimally dependent on the solvent type and especially excels at stripping away the abundant surface defects caused by mechanical crushing. [ABSTRACT FROM AUTHOR]

ABSTRACT Regarding its wide range of applications in different industries, such as oil and gas, and for manufacturing equipment used to control pollution and recycle industrial wastes, Inconel 686 turning process is highly important. The alloy is highly resistant to high temperatures and corrosion, and thus it can preserve its properties at high temperatures. Due to its low heat transfer coefficient and work hardening during operation, Inconel 686 is considered a difficult‐to‐cut material, and hence, turning Inconel 686 is challenged with major limitations regarding input parameter level and cutting fluid and issues such as reduced surface quality. The input parameter level and cutting fluid limitations might severely harm the environment and humans, decrease the machining efficiency and keep cleaner production goals out of reach. Novel cooling methods such as cryo‐MQL can contribute to achieving cleaner production goals. Cooling methods improve the machining performance and prohibit any damage to the surface integrity. In this study, cryo‐MQL, along with carbide‐coated tools and biodegradable vegetable oil, was adopted. The efficiency and success rate of cryo‐MQL were evaluated by comparing the results with those of MQL and wet methods. A wide range of output parameters, such as residual stresses, cutting zone temperature, cutting forces, tool wear, surface smoothness, surface defects and micro‐hardness, were assessed by changing the cutting speed and feed rate. The results indicated that cryo‐MQL could reduce the cutting forces, tool wear rate, cutting zone temperature and residual stresses while improving the surface quality. Moreover, environmental concerns were completely dealt with. Due to the increased possibility of higher input parameter levels, the time and cost of the cutting process were significantly reduced. [ABSTRACT FROM AUTHOR]

The exceptional performance and potential of 2D/3D perovskites for enhancing the efficiency and stability of perovskite solar cells (PSCs) are extensively studied. However, selecting appropriate organic ligands is critical to this process. The incorporation of suitable ligands into the 2D layer can passivate surface defects, regulate energy level alignment, and significantly improve device stability. Conversely, using unsuitable ligands not only fails to achieve the desired functions, but also negatively impacts device efficiency by hindering charge carrier transport and causing energy level mismatch issues. Therefore, exploring the selection of organic ligands based on different requirements is essential. In this review, recent studies are classified based on the chemical structure of organic ligands, analyze their application requirements, and provide suggestions for choosing optimal organic ligands to construct 2D/3D perovskites. Additionally, the feasibility and necessity of utilizing more efficient organic ligands while providing guidance for future research on 2D/3D PSCs are emphasized. [ABSTRACT FROM AUTHOR]

Tailoring the surface structure of the mesoporous metal catalysts is pivotal for influencing both the catalytic activity and selectivity. Through the dealloying of bimetallic alloys, surface modification is achieved by dissolving unstable components, thereby forming a multitude of catalytically active sites. In this study, highly enhanced electrocatalysts are fabricated via chemical etching of mesoporous AuCu alloy nanoparticles prepared through the self‐assembly of micelles. The presence of large mesoporosity (>10 nm) with rich defect sites on the surface, along with the synergistic effect arising from the AuCu bimetallic alloy, effectively boosts electrocatalytic performance for the ethanol oxidation reaction. This strategy demonstrates a promising strategy to further enhance the catalytic performance of Au‐based mesoporous nanoparticles by eliminating the unstable metal component and refining the mesoporous structure with an abundance of various active sites. [ABSTRACT FROM AUTHOR]

Selective synthesis of furfuryl alcohol from furfural conversion via semiconductor photocatalytic route has appeared as a promising solution for transforming biomass into high‐value‐added products under mild temperature and pressure conditions. Titanium dioxide (TiO2) photocatalysts were prepared by a simple sol‐gel method followed by a calcination treatment ranging from 500–1000 °C, resulting in materials with distinct physicochemical properties. The photocatalytic efficiency of the TiO2 samples was examined in the selective production of furfuryl alcohol from furfural under UV‐LED irradiation. The influence of various organic solvents, including ethanol, methanol, 2‐propanol, and acetonitrile, was evaluated to optimise the selectivity towards furfuryl alcohol production. Photocatalysts with larger anatase to rutile ratio and increased density of oxygen vacancies (defects) exhibited superior performance for furfuryl alcohol production. The presence of these defects on the catalyst surface leads to a significant enhancement in the photocatalytic efficiency by acting as crucial active sites. Among the TiO2 samples, the highest conversion of furfural into furfuryl alcohol was observed with the TiO2 sample calcined under an air atmosphere at 600 °C (TiO2‐600), achieving 85 % yield and 100 % selectivity for furfural after 30 min reaction using ethanol as solvent. [ABSTRACT FROM AUTHOR]

The use of an integrated approach to determining the static strength of operated main pipelines with surface defects makes it possible to take into account the effect of residual weld stresses in combination with operational loads. In this case, it is important to determine the nature of the welding stress distribution for welded structures made of high-strength steels. Here, there is non-uniformity of stresses concerning magnitude and gradient, which is due to technological methods. A mathematical model of the experimental-calculation method is proposed. Such a model allows describing such stresses. A two-parameter criterion for brittle-tough fracture of a material which takes into account the influence of residual stresses is used. It is shown that the combination of residual stresses and operating loads has a significant effect on the safety factor. At the same time, conditions are possible under which compressive residual stresses have a favourable effect on the static strength of a pipeline with a defect. [ABSTRACT FROM AUTHOR]

Vision-assisted surface defect detection technology is shallowly applied in crack identification of assembly building components, for this reason, the study proposes a crack identification and defect detection method for assembly building components oriented to intelligent construction. An image preprocessing algorithm is designed by improving bilateral filtering, on the basis of which an image classification model is constructed using the GhostNet algorithm, and the cracks are localized and measured using the 2D pixel positioning technique. Algorithm validation showed that the processed image denoising is better, and the peak signal-tonoise ratio of the image of the proposed algorithm is improved by 15.701 % and 2.395 %, respectively, compared to other algorithms. The F1 value of the proposed model after 50 training rounds increased by 20.970 % on average compared to other models, and the detection accuracy was as high as 0.990. The actual measurements of cracks in concrete wall panels revealed that the research-proposed method has better results compared to the traditional manual measurements, and is not subject to the limitations and interferences of factors such as manual experience, and it is more effective in the recognition of crack images. Overall, the detection method proposed by the study has high accuracy and small error, can meet the needs and standards of crack detection in assembly building components, and can intelligently locate the maximum length and width coordinates of the cracks, which is of high value in the application of crack detection in assembly building components. [ABSTRACT FROM AUTHOR]

The surfaces of ceramic products are replete with numerous defects, such as those that appear during the diamond grinding of sintered SiAlON ceramics. The defective surface layer is the reason for the low effectiveness of TiZrN coatings under abrasive and fretting wear. An obvious solution is the removal of an up to 4-µm-thick surface layer containing the defects. It was proposed in the present study to etch the layer with fast argon atoms. At the atom energy of 5 keV and a 0.5 mA/cm2 current density, the ions were converted into fast atoms and the sputtering rate for the SiAlON samples reached 20 μm/h. No defects were observed in the microstructures of coatings deposited after beam treatment for half an hour. The treatment reduced the volumetric abrasive wear by five times. The fretting wear was reduced by three to four times. [ABSTRACT FROM AUTHOR]

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%. [ABSTRACT FROM AUTHOR]

The appearance quality of fair-faced concrete plays a crucial role in evaluating the engineering quality, as the abundance of small-area bubbles generated during construction diminishes the surface quality of concrete. However, existing methods are plagued by sluggish detection speed and inadequate accuracy. Therefore, this paper proposes an improved method based on YOLOv5 to rapidly and accurately detect small bubble defects on the surface of fair-faced concrete. Firstly, to address the issue of YOLOv5 in generating prior boxes for imbalanced samples, we divide the image preprocessing part into small-, medium-, and large-area intervals corresponding to the number of heads. Additionally, we propose an area-based k-means clustering approach specifically tailored for the anchor boxes within each of these intervals. Moreover, we adjust the number of prior boxes generated by k-means clustering according to the training loss function to adapt to bubbles of different sizes. Then, we introduce the ECA (Efficient Channel Attention) mechanism into the neck part of the model to effectively capture inter-channel interactions and enhance feature representation. Subsequently, we incorporate feature concatenation in the neck part to facilitate the fusion of low-level and high-level features, thereby improving the accuracy and generalization ability of the network. Finally, we construct our own dataset containing 980 images of two classes: cement and bubbles. Comparative experiments are conducted on our dataset using YOLOv5s, YOLOv6s, YOLOxs, and our method. Experimental results demonstrate that the proposed method achieves the highest detection accuracy in terms of mAP0.5, mAP0.75, and mAP0.5:0.95. Compared to YOLOv5s, our method achieves a 7.1% improvement in mAP0.5, a 3.7% improvement in mAP0.75, and a 4.5% improvement in mAP0.5:0.95. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) offers an innovative manufacturing approach compared to traditional methods. It has found diverse applications in various fields, ranging from medical devices to aerospace, using a variety of metals. However, products manufactured with the metal AlSi10Mg using the SLM technique have a significant surface roughness defect. Consequently, products produced through SLM necessitate post-processing. In this study, electro-polishing was conducted on AlSi10Mg specimens manufactured at four different inclinations. Surface roughness measurements and 3D scans were performed at various time intervals to observe changes in roughness. It was observed that an increase in electro-polishing time effectively reduced surface roughness. This research provides data on post-processing for three-dimensional structures manufactured using the SLM technique. [ABSTRACT FROM AUTHOR]

In this paper, we reported the fabrication of improved CH3NH3PbI3/Si heterojunction photodetectors (PDs) achieved by passivating the interfacial defects by a low-temperature atomic layer deposition-grown thin amorphous HfO2 layer. The results suggested that the HfO2 thin layer effectively passivated the surface defects of Si and slightly improved the qualities of CH3NH3PbI3 thin films in terms of increasing the grain sizes. Current–voltage measurements suggested that the HfO2 thin layer suppressed interfacial Shockley–Read–Hall recombination, which decreased the dark current and simultaneously increased the photocurrent. However, a thick HfO2 layer resulted in a decrease in the photocurrent because of the insulting nature of HfO2. A champion performance was obtained by employing a 5 nm HfO2 layer, where the responsivity and detectivity were 0.6 mA/W and 8.0 × 1010 Jones, respectively, which are two times and four times as high as those PDs without the HfO2 layer. The results will provide a simple strategy for improving the performance of perovskite/Si heterojunction PDs in the future. [ABSTRACT FROM AUTHOR]

Through Silicon Via (TSV), a kind of 3D packaging technique, can improve chip integration based on "More than Moore" scheme. The larger copper area ratio at the TSV barrier layer interface requires strong adjustability of the removal rate selection ratio of copper, tantalum and TEOS–SiO2, and stronger surface defect control and surface profile correction capabilities. In this paper, the effects of guanidine hydrochloride (GH) and 2,2′-[[(Methyl-1H-benzotriazol-1-yl)methyl]imino]bisethanol (TT-LYK) system on the tunability of Ta and Cu removal rate in chemical mechanical polishing (CMP) of TSV wafer barrier material were studied. GH can complex TaO 4 3 - , while TT-LYK can form a protective film on the surface of Cu, thereby achieving rate regulation. The results showed that when the concentration of GH increased from 0 to 2 wt%, the removal rate selectivity ratio of Ta and Cu raised from 1.02:1 to 1.26:1. When the concentration of TT-LYK increased from 2.5 to 10 mmol/L, the removal rate selectivity ratio of Ta and Cu increased from 1.33:1 to 1.79:1, which verified that the GH and TT-LYK systems can efficiently regulate the rate and rate selection ratio of Ta and Cu. Surface roughness for both metals decreased from pre-polishing values of 5.03 nm to post-polishing values of 0.37 nm for Ta, from pre-polishing values of 5.33 nm to post-polishing values of 1.26 nm for Cu indicating that GH and TT-LYK systems significantly improved the surface quality. [ABSTRACT FROM AUTHOR]

To address the challenge of high rejection rates due to surface quality defects that are hard to detect with the naked eye, a comprehensive model was developed for detecting such defects and implemented a full-fledged equipment and system for surface quality inspection. Specifically, a targeted comprehensive model was formulated to identify three types of plate and strip surface quality defects. Furthermore, the latest hardware equipment and an advanced unsupervised self-learning algorithm were integrated into the detection system to enhance the identification and classification of these surface quality defects. The results demonstrate an improvement in the comprehensive detection rate and classification accuracy of surface defects from 96 % and 91 % to 98 % and 95 %, respectively. Moreover, the qualified rate of finished products has increased from 94 % to 99 %, leading to improved accuracy in defect detection and a significant decrease in false alarm rates. These findings provide a solid foundation for significantly reducing material waste and enhancing the overall quality of the production process. [ABSTRACT FROM AUTHOR]

In this study, we propose a random cropping augmentation (RCA) based on an optimized YOLOv8 algorithm by introducing a tiny object detection layer to improve the accuracy of surface defect detection on the inner wall of micro-holes of cylindrical high-precision parts. The inner surfaces of cylindrical high-precision parts possess attributes like high smoothness, elongated channels, limited visibility, minute flaws, and challenges capturing image data. These characters impose significant difficulties in automating defect detection for this part type. Taking the automobile fuel injector valve seat as an example, industrial CCD cameras and lenses are combined with a circular light source to acquire magnified image data. The images are preprocessed using tile and adaptive histogram equalization algorithms. Then, the RCA algorithm expands and randomly combines the image data. We compare the proposed algorithm with the original faster RCNN, YOLOv5 and YOLOv8 algorithm for surface defect detection on the inner wall of the automotive fuel injector valve seat. The experimental results demonstrate that after extending the dataset with the RCA algorithm, there have been significant improvements in the mean average precision (mAP) for the faster RCNN, YOLOv5, and YOLOv8 algorithms. The mAP@0.5 for these algorithms has increased from 37.1%, 48.7%, and 62.2% to 60.9%, 72.6%, and 81.1%, respectively. Furthermore, the improved YOLOv8 algorithm, which incorporates tiny object detection module, achieved an mAP@0.5 of 87% on the dataset extended by the RCA algorithm, with precision and recall reaching 89.7% and 80.6%, respectively. The improved YOLOv8 + RCA algorithm performs better than mainstream algorithms in surface defect detection on the inner wall of the fuel injector valve seat while meeting the requirements of industrial production quality inspection for detection speed. [ABSTRACT FROM AUTHOR]

To address the issue of low detection accuracy of steel surface defects due to complex texture background interference and complex defect morphology, this paper proposes an improved YOLOv8 model based on MobileViTv2 and Cross-Local Connection for steel surface defect detection. Firstly, the lightweight MobileViTv2 network is introduced into the backbone network, which enhances the feature extraction capability of the model in complex defect shapes by combining the advantages of CNN and Transformer. Then, the designed CLC method is introduced into the neck network, which connects deep and shallow features through additional convolutional layers, further integrating defect features in the presence of complex texture background interference. Finally, the NET-DET dataset is augmented to improve the model's robustness. Experimental results show that the mAP of the improved model is 74.1%, with a detection speed of 86.2 FPS and model memory usage of 27.5 MB. Compared to YOLOv5 and YOLOv8, the mAP of the improved model is increased by 6.5% and 4%, respectively. Compared to existing object detection models, the improved model has the characteristics of high detection accuracy and fast detection speed, meeting the requirements of industrial production for steel surface defect detection. [ABSTRACT FROM AUTHOR]

Selective catalytic NH 3 -to-N 2 oxidation (NH 3 -SCO) is highly promising for abating NH 3 emissions slipped from stationary flue gas after-treatment devices. Its practical application, however, is limited by the non-availability of low-cost catalysts with high activity and N 2 selectivity. Here, using defect-rich nitrogen-doped carbon nanotubes (NCNT-AW) as the support, we developed a highly active and durable copper-based NH 3 -SCO catalyst with a high abundance of cuprous (Cu+) sites. The obtained Cu/NCNT-AW catalyst demonstrated outstanding activity with a T 50 (i.e. the temperature to reach 50% NH 3 conversion) of 174°C in the NH 3 -SCO reaction, which outperformed not only the Cu catalyst supported on N-free O-functionalized CNTs (OCNTs) or NCNT with less surface defects, but also those most active Cu catalysts in open literature. Reaction kinetics measurements and temperature-programmed surface reactions using NH 3 as a probe molecule revealed that the NH 3 -SCO reaction on Cu/NCNT-AW follows an internal selective catalytic reaction (i -SCR) route involving nitric oxide (NO) as a key intermediate. According to mechanistic investigations by X-ray photoelectron spectroscopy, Raman spectroscopy, and X-ray absorption spectroscopy, the superior NH 3 -SCO performance of Cu/NCNT-AW originated from a synergy of surface defects and N-dopants. Specifically, surface defects promoted the anchoring of CuO nanoparticles on N-containing sites and, thereby, enabled efficient electron transfer from N to CuO, increasing significantly the fraction of SCR-active Cu+ sites in the catalyst. This study puts forward a new idea for manipulating and utilizing the interplay of defects and N-dopants on carbon surfaces to fabricate Cu+-rich Cu catalysts for efficient abatement of slip NH 3 emissions via selective oxidation. [ABSTRACT FROM AUTHOR]

Inverted NiOx‐based perovskite solar cells (PSCs) exhibit considerable potential because of their low‐temperature processing and outstanding excellent stability, while is challenged by the carriers transfer at buried interface owing to the inherent low carrier mobility and abundant surface defects that directly deteriorates the overall device fill factor. Present work demonstrates a chemical linker with the capability of simultaneously grasping NiOx and perovskite crystals by forming a Ni−S−Pb bridge at buried interface to significantly boost the carriers transfer, based on a rationally selected molecule of 1,3‐dimethyl‐benzoimidazol‐2‐thione (NCS). The constructed buried interface not only reduces the pinholes and needle‐like residual PbI2 at the buried interface, but also deepens the work function and valence band maximum positions of NiOx, resulting in a smaller VBM offset between NiOx and perovskite film. Consequently, the modulated PSCs achieved a high fill factor up to 86.24 %, which is as far as we know the highest value in records of NiOx‐based inverted PSCs. The NCS custom‐tailored PSCs and minimodules (active area of 18 cm2) exhibited a champion efficiency of 25.05 % and 21.16 %, respectively. The unencapsulated devices remains over 90 % of their initial efficiency at maximum power point under continuous illumination for 1700 hours. [ABSTRACT FROM AUTHOR]

The detection of steel surface defects is of great significance to steel production. In order to better meet the requirements of accuracy, real-time, and lightweight model, this paper proposes a highly efficient and lightweight steel surface defect detection method based on YOLOv5n. Firstly, ODMobileNetV2 composed of MobileNetV2 and ODConv is used as the backbone to improve the defect feature extraction capability. Secondly, GSConv is utilized in the neck to achieve deep information fusion through channel concatenation and shuffling, enhancing the ability of feature fusion. Finally, this paper proposes a spatial-channel reconstruction block (SCRB) designed to suppress redundant features and improve the representation ability of defect features through feature separation and reconstruction. Experimental results show that this method achieves 84.1% mAP and 109 FPS on the NEU-DET dataset, and 72.9% mAP and 110.1 FPS on the GC10-DET dataset, enabling accurate and efficient detection. Furthermore, the number of parameters is only 5.04M, which has a significant lightweight advantage. [ABSTRACT FROM AUTHOR]

Leaf springs, as vital suspension components in trucks, endure various forces such as torsion, compression, tension and impact, rendering them highly susceptible to fatigue and fracture risks, thereby posing a significant threat to vehicle safety. During the production process of leaf springs, surface decarburisation can easily occur, leading to the formation of numerous surface rolling defects. These defects exert varying influences on the initiation of fatigue crack. This study investigates the influence of various forms of rolling defects caused by decarburisation on fatigue crack initiation in 50CrMnMoNbAT leaf spring through experimental and numerical analysis of fatigue fracture behaviour. The results indicate that fracture locations in the leaf springs are predominantly concentrated approximately 297 mm and 698 mm from the ends. Furthermore, the occurrence of surface decarburisation during the rolling process engenders a significant number of surface rolling defects. Surface defects in leaf springs can be categorised into three forms: Parallel defect, Parcel defect and Occlusal defect. In terms of defect propagation rate, rolling defects on the concave surface are most prone to initiate fatigue cracks, with Occlusal defects exhibiting the highest likelihood of developing internal cracks and the fastest crack propagation rate. [ABSTRACT FROM AUTHOR]

ABSTRACT The high tensile strength and high resistance of nickel‐based superalloy 686 against high temperatures and corrosion rates have made it a widely used in important applications such as the aerospace industry, high pollution‐ and corrosion‐resistance equipment manufacturing and petrochemical industry. Therefore, the machining of this advanced alloy with its unique properties is extremely important and can be challenging. Significant increase in input parameters levels, reduction of machining costs, improvement of surface and subsurface properties and clean production are among the issues that should be considered in dealing with Inconel 686 turning operations. Simultaneous application of advanced tools such as polycrystalline diamond (PCD) and polycrystalline cubic boron nitride (PCBN) and optimised minimum quantity lubrication (MQL) method and evaluating the results obtained with a wide range of output parameters related to machining process performance and tribological properties can be proposed as an innovation and a solution to this problem in this article. This study analyses several output parameters with different speeds and feeds to evaluate the effect of cutting insert type on machining process performance and tribological properties. The output parameters include tool wear, residual stress, cutting zone temperature, surface smoothness, machining forces and workpiece surface defects. The results indicated that using the optimised MQL method reduces the size of lubricant droplets and increases the surface covered by cooling. With these changes, the performance of the machining process and the parameters related to the surface integrity increase significantly. Among the parameters associated with the performance of the machining process, the PCD tool reduces the cutting zone temperature by 23%, the tool wear by 19% and the machining forces by 18% compared to the PCBN tool. In the parameters related to surface integrity, this method reduces the residual stress by 19% and the surface roughness by 9% compared to the PCBN tool. From the production index perspective, the PCD tool can significantly increase the cutting speed and feed rate, reducing production time and costs. [ABSTRACT FROM AUTHOR]

Achieving an optimal balance between mechanical properties, dielectric properties, and thermal shock resistance presents a significant challenge in the development of advanced ceramics. This study investigates Si 3 N 4 -BN composite materials, prepared by hot-press sintering with magnesium aluminum silicate (MAS) as the sintering additive, to assess the impact of varying hexagonal boron nitride (h -BN) content on their mechanical and dielectric properties, as well as their thermal shock resistance. At an h -BN content of 20 wt%, the BN layers act as a toughening phase through a pull-out mechanism, minimally impacting the composite's density. This composition results in superior fracture toughness (7.4 MPa m1/2), high strength (598 MPa), low dielectric constants (∼6.8), and dielectric loss (∼7.4 × 10−3). Notably, after a thermal shock at 1000 °C, the composite exhibits a peak residual strength of 621 MPa, surpassing its initial strength. This enhanced performance is attributed to the formation of a dense Mg–Al–Si–B–O oxide glass layer on the surface during thermal shock, which not only repairs surface defects but also induces compressive stress. The development of the Si 3 N 4 -BN-MAS composite, with its outstanding comprehensive performance, marks a significant advancement for materials used in high-temperature wave transmission applications. [ABSTRACT FROM AUTHOR]

This research aims to detect and analyze critical internal and surface defects in metal components manufactured by powder bed fusion of metals using a laser beam (PBF‐LB/M). The aim is to assess their impact on the fatigue behavior. Therefore, a combination of methods, including image processing of micro‐computed tomography (μCT) scans, fatigue testing, and machine learning, was applied. A workflow was established to contribute to the nondestructive assessment of component quality and mechanical properties. Additionally, this study illustrates the application of machine learning to address a classification problem, specifically the categorization of pores into gas pores and lack of fusion pores. Although it was shown that internal defects exhibited a reduced impact on fatigue behavior compared with surface defects, it was noted that surface defects exert a higher influence on fatigue behavior. A machine learning algorithm was developed to predict the fatigue life using surface defect features as input parameters. Highlights: Established workflow for extracting surface and internal features.Developed pore classification ML algorithm with 92.4% accuracy.Effectively developed ML algorithm for predicting fatigue life. [ABSTRACT FROM AUTHOR]

Ball burnishing (BB) is a surface finishing process that involves moving the tool while pressing it against the workpiece under rolling contact. We investigated the effects of BB on the fatigue strength of an aluminum alloy with a semicircular slit simulating an initial surface defect. After performing BB under a typical condition, compressive residual stress was induced to a depth of 0.4 mm, with a reduction in surface roughness by 81%. Consequently, the fatigue strength increased by 22%. BB prevented the fatigue strength reduction caused by the 0.1 mm deep slit. The result indicated that BB could render the below 0.1 mm deep surface defect harmless in terms of fatigue strength. The maximum depth of surface defect that can be rendered harmless is 0.18 mm according to fracture mechanics, which is in agreement with the experimental results. Highlights: Ball burnishing (BB) can introduce large compressive residual stress along with smoothing.BB can render a 0.1 mm deep slit harmless in terms of fatigue strength.Defect size rendered harmless can be estimated based on fracture mechanics.Compressive residual stress is the main factor rendering the surface defect harmless. [ABSTRACT FROM AUTHOR]

Steel surface defect detection poses a significant challenge in the steel industry, aiming to enhance product quality and production efficiency. Traditional mechanical and optical detection methods exhibit relatively low efficiency and poor real-time performance in detecting acceptable defects on the surface of steel strips. This paper proposes a new model named Hyper-YOLO for steel surface defect detection in steel strips. Firstly, the CSP module in the conventional YOLO backbone network is replaced with the Ghost module. The Ghost module, a lightweight convolutional module, enhances model efficiency by reducing parameter count and computational load while maintaining satisfactory performance. Secondly, researchers replace the PAFPN module in YOLO V5 in the bottleneck section with the Hyper FPN module. Hyper FPN, an improved feature pyramid network module, leverages features at different scales for multi-level feature fusion, enhancing the model's capability to detect targets at various scales. Lastly, improvements are made in the loss functions for both training and prediction stages. The α-CIoU loss function is introduced during training to substitute the original CIoU loss function, and the α-DIoU loss function is utilized during prediction instead of the original DIoU loss function. These enhanced loss functions effectively measure the accuracy and position precision of target boxes, thereby improving the detection performance of the model. Through these enhancements, the Hyper YOLO model achieves an overall performance improvement of 4.58% over the baseline model. This indicates that Hyper YOLO performs outstanding surface defect detection in steel strips, providing innovative insights for the YOLO V5 model. These improvements not only elevate the accuracy and efficiency of the model but also hold significant guidance for similar research and applications. [ABSTRACT FROM AUTHOR]

As deep learning advances, neural network technologies are increasingly penetrating the field of steel surface defect detection. To tackle the challenges of low accuracy and inadequate quality, we introduce CMS-YOLOv8s, a defect detection algorithm engineered for superior precision and efficiency. Initially, integrate the CBAM to enhance the network's focus on relevant information, allowing it to leverage multi-dimensional data like spatial and channel dimensions fully. Next, introduce the SPPFCSPC module to fuse feature information from different scales, expanding the model's receptive field and enhancing its feature extraction abilities. Finally, incorporate a small object detection head to boost the model's capability in identifying tiny targets, strengthening its ability to detect targets across different scales. Experimental findings show that CMS-YOLOv8s attains a detection accuracy of 70.4%, registering a significant 3.3% enhancement compared to the original YOLOv8s. This advancement distinctly improves defect detection accuracy across all categories. [ABSTRACT FROM AUTHOR]

Hydrogen permeation resistance in the welded region of 06Cr18Ni11Ti steel is relatively weak due to surface defects, which need high integrity surface machining. The parameters of the welding material for 06Cr18Ni11Ti steel are currently unavailable, which causes some inconvenience for simulation studies. To fill the lack of 06Cr18Ni11Ti steel weld material parameters in the relevant literature at the present stage, the quasi-static tensile test at different strain rates and notch specimen tensile tests were conducted in this paper and determined the Johnson–Cook (J-C) constitutive model parameters and Johnson–Cook failure model parameters. Subsequently, a multi-grain grinding simulation model was built based on W-M fractal dimension theory by using the determined material parameters. The influence of processing parameters on grinding heat was analyzed. Grinding experiments were conducted to analyze the influence of processing parameters on grinding heat and grinding force. By comparing the simulation and experimental results, it is revealed that the average error is 9.37%, indicating relatively small discrepancy. It is demonstrated that the grinding simulation model built in this paper could efficiently simulate the grinding process, and the determined weld material parameters of 06Cr18Ni11Ti steel have been verified to possess high accuracy and reliability. [ABSTRACT FROM AUTHOR]