Surface Imperfections Research Articles

Detecting microscale surface imperfections in powder bed fusion through light scattering and machine learning – validation of inspection principles

Detecting microscale surface imperfections in powder bed fusion through light scattering and machine learning – validation of inspection principles

Assembly tolerance analysis for spacecraft cabin considering non-ideal surface

Due to manufacturing and assembly errors, the assembly accuracy of the spacecraft cabin is inevitably affected. To minimize these errors and improve operational performance, we apply a comprehensive assembly accuracy analysis method that accounts for non-ideal surfaces. First, a 3D model of the cabin’s non-ideal surfaces is developed using skin model shapes and small displacement torsor theory to represent form and positional errors, respectively. However, previous studies have often overlooked the impact of form errors. Next, an improved deviation propagation model is introduced to analyze the assembly of multi-segment cabins while considering surface imperfections. Finally, a simulation study is conducted using a cabin simulation component as the research object. The results confirm the method’s effectiveness in predicting the assembly accuracy of cabins with non-ideal surfaces.

An injection filling method for packing chromatography devices.

An injection filling method for packing chromatography devices.

LiC6 Phase Mobility in Highly Oriented Pyrolytic Graphite

Determining the solid diffusion coefficient of lithium in graphite anode active materials for lithium-ion batteries is challenging due to the complex intercalation dynamics, the structural complexity, particle size/shape distribution, and the superposition of liquid electrolyte transport processes in the pores of an electrode. To minimize these influences, we examined the lithiation of highly oriented pyrolytic graphite (HOPG) disk electrodes, with the basal planes stacked normal to the disk thickness (0.5 mm). At first, the radially progressing lithiation of the HOPG disks was followed in situ by top-view optical monitoring of the golden LiC6 phase. However, post-mortem analysis of split HOPG disks revealed that HOPG crystal surface imperfections lead to the artefact that the apparent LiC6 phase progression determined from top-view images does not correspond to that in the bulk of the disks. Thus, the LiC6 phase front position can only be quantified through post-mortem analysis of split HOPG disks. The intercalation time and temperature dependence of the LiC6 phase progression can be reasonably well described by a Fickian diffusion model in cylindrical geometry, yielding an apparent diffusion coefficient of the LiC6 phase front of D 0 = 0.6–1.0 × 10−13 m2 s−1 at 25 °C, with an activation energy of E a = 35.4–39.0 kJ mol−1 (between 10–55 °C).

On the Friction and Lubrication of 3D Printed Ti6Al4V Hip Joint Replacement

The present study investigates the tribological performance of 3D printed Ti6Al4V total hip replacements (THR) compared to conventionally produced THRs from CoCrMo and FeNiCr alloys. The objective was to evaluate the suitability of 3D printed titanium alloy, with and without DLC coating, for THR rubbing surfaces and to investigate the potential benefits of 3D printing technology for friction and lubrication. A pendulum hip joint simulator was employed to replicate the swinging motion of a hip joint, thereby enabling the measurements of coefficient of friction (COF) and the observation of lubricant film formation under realistic conditions between the metal femoral head and acetabular cup. The experiments demonstrated that additive manufacturing enables the creation of specific surface topographies that can enhance protein adsorption, but also introduce surface imperfections negatively affecting tribological properties. The elevated surface roughness of additively manufactured femoral heads did not inevitably result in an increase in COF and was comparable to that of conventionally manufactured femoral heads. The additively manufactured Ti6Al4V head without DLC coating also exhibited a more rapid increase in lubricant film thickness during dynamic motion. In conclusion, the findings indicate that while 3D printing offers promising advancements in implant customization and material properties, its application requires careful consideration of surface finishing and coating methods to achieve optimal tribological performance.Graphical abstract

Impact of Substrate Type on the Properties of Cast Biodegradable Starch-Based Films

Biodegradable films are a viable alternative to conventional plastics, thereby contributing to environmental pollution reduction. This study investigates the impact of substrate type on the properties of starch-based films produced using a plasticizer-assisted casting method. Four different substrates, namely, glass, copper, copper-free laminate, and Teflon®, were evaluated, addressing a research gap in which previous studies primarily focused on film composition. The films were analyzed for color, tensile strength, surface free energy, and surface morphology using optical and electron microscopy. The results demonstrated a substrate-dependent impact on surface properties, particularly optical transparency, surface roughness, and adhesion. The films cast on glass and laminate exhibited higher transparency and lower roughness, while copper substrate induced micro-striations and strong adhesion. Teflon® substrates replicated surface imperfections, which may be advantageous for optical applications, but caused film delamination. Tensile strength did not show statistically significant differences across substrates, although reduced elongation was observed for the films cast on Teflon®. Water vapor permeability was also not significantly affected, indicating a dominant role of bulk material properties. It averaged 25 kg per day per square meter, which means high vapor permeability. Surface free energy analysis revealed marked variations between top and bottom layers, with values ranging from 35 to 70 mJ·m⁻2 depending on the substrate. These findings confirm that the type of casting substrate plays a critical role in determining the surface and optical properties of starch-based films, even at the laboratory scale. This study provides new insights into substrate–film interactions and establishes a foundation for optimizing biodegradable film fabrication for industrial and application-specific needs.

Improved structural configuration on the MAPbI3 perovskite surface through the integration of 2-amino benzothiazole for durable and effective photovoltaic device

Abstract The organic-inorganic hybrid MAPbI3 perovskite material has remarkable optoelectronic properties for excellent device performance. However, the poor long-term stability of MAPbI3 perovskite in light, heat and humid environments is a major obstacle to commercialization. Degradation of MAPbI3 and an impairment in non-radiative charge recombination, a major impediment to increasing the stability and efficiency of photovoltaic devices, are made possible by the trap state and surface imperfections between the perovskite and ETL interfaces. Here, the surface defect was healed 2-Amino benzothiazole (Lewis’s base) by activating the heteroatoms of S and N and the amine functional group for defect passivation. In the context of defect passivation, systematic investigation is conducted on the formation of hydrogen bonds between N—H—I or the coordination in between uncoordinated Pb2+ and heteroatoms of S and N. The investigations have been done into the optoelectronic properties, chemical interaction, structure, crystal orientation, morphology, and photovoltaic performance characteristics of MAPbI3 in association with 2-amino benzothiazole (2-ABT) surface integration. A high photovoltaic efficiency of 21.5% with improved open circuit voltage (Voc) and fill factor was demonstrated with the optimum concentration of passivating material (2 mg/ml IPA) than the pristine MAPbI3 PCE of 19.4%. As demonstrated by stability studies, the 2-ABT integrated device outscored than the pristine MAPbI3 device, retaining 87% of its PCE after 500 hours in N2.

Influence of metal nanocoatings on the corrosion resistance of welded joints of wind power facilities

The construction of numerous offshore wind power plants across the European Union and globally has exposed these structures to corrosive marine environments, leading to significant corrosion damage, particularly in welded joints. This study examines the effect of nickel and nickel-copper nanolaminates on corrosion resistance. Electrochemical methods, including polarization measurements and electrochemical impedance spectroscopy (EIS), were used to evaluate corrosion behavior in tap water and 3.5% NaCl solution. Visual inspection by optical microscopy was performed to analyze localized corrosion defects. BM-Ni and BM-Ni-Cu specimens exhibited galvanic corrosion, more intense in NaCl solution. BM-Ni-Cu samples showed significant potential differences, indicating electrochemical interactions between nanolayers. Corrosion defects initiated at surface imperfections, potentially affecting the integrity of welded joints over time. Surface defects in nanolaminates may serve as corrosion initiation points. The results showed the promising potential of metal multilayer nanocoatings for improving the corrosion resistance of welded joints. Future research should optimize layer thickness and other parameters to improve corrosion resistance and enhance the durability of offshore wind turbines.

Identifying defective casting products using hierarchical defect recognition architecture: A computer vision approach

This paper proposes a novel approach for identifying defective casting products using a custom convolutional neural network architecture named Hierarchical Defect Recognition Architecture (HiDraNet). The HiDraNet model is designed to classify submersible pump impeller casting products into Normal and Defective categories by learning and extracting hierarchical features from a comprehensive dataset of 7348 casting product images, which includes various defect types such as fins, porosity, surface imperfections, and multiple defects. Experimental results demonstrate the superior performance of the HiDraNet model compared to several well-known deep learning models, such as AlexNet, MobileNetv2, ResNet18, GoogLeNet, ShuffleNet, and SqueezeNet, achieving the highest classification accuracy of 99.8% while exhibiting faster computation times. The proposed approach has significant implications for the manufacturing industry, as it can reduce the reliance on manual inspection methods, improve overall product quality, and minimize production costs, contributing to the broader adoption of Industry 4.0 technologies in the manufacturing sector.

Remaining Useful Life Prediction of Rolling Bearings Based on Deep Time–Frequency Synergistic Memory Neural Network

Rolling bearings are essential components of a rotating machinery system. Surface imperfections on bearings can alter vibration patterns, and monitoring these changes allows for the precise prediction of the bearing’s remaining useful life (RUL). To address the limitations, such as inadequate sensitivity to features and constrained time–frequency feature extraction capabilities, in conventional methods for predicting the RUL of rolling bearings in the early stages of degradation, this paper introduces a novel predictive framework that combines dynamic weighting mechanisms with hybrid deep learning. This framework incorporates a continuous wavelet transform to generate two-dimensional time–frequency feature maps as degradation indicators, employs CNN for extracting local detailed features, integrates iTransformer modules with dynamic weighting mechanisms to enhance the focus on early subtle features, and leverages the time-dependent modeling capabilities of BiLSTM. The experimental findings using truncated samples from IEEE-PHM2012 datasets show a 71.82% reduction in errors compared with traditional CNN in the early prediction stages, where it effectively mitigated the challenge of early degradation features being overshadowed by noise. Ablation experiments on model components further validated the effectiveness of the model architecture design, where the dynamic weighting mechanism contributed significantly (29.92%) to improving the mean absolute error (MAE).

Wet‐Transferred MoS2 on Passivated InP: A Van der Waals Heterostructure for Advanced Optoelectronic Applications

III–V semiconductors are considered ideal materials for optoelectronic applications due to their direct bandgap and wide tunable range of bandgap energy. However, optoelectronic devices based on III–V have been plagued by significant surface recombination due to imperfect surface bonding. Meanwhile, 2D transition metal dichalcogenides (TMDs) exhibit unique electrical properties, including a dangling bond‐free surface, which has led to extensive research into their potential for electronic applications. However, optical devices such as photodetectors utilizing 2D‐TMDs have received relatively little attention, primarily because they are inefficient at absorbing photons independently. In this study, a photodetector employing atomically thin layers of MoS2 on InP substrate is demonstrated. The n‐MoS2/p‐InP device exhibits excellent rectifying properties with an ideality factor of 1.57, indicating the formation of a type‐II staggered heterojunction. The photoresponsivity of the heterojunction device is measured across a wavelength range of 300–1000 nm, with a maximum value of 960 mA W−1. Notably, the MoS2 layers provide a stable passivation effect on the imperfect InP crystal surface.

Effects of Heat Treatment on Microstructure Change and Mechanical Performance of Additively Manufactured 316L Stainless Steel Stents.

Currently, percutaneous coronary intervention, based on stenting, is employed to provide scaffolding support to correct occlusion and diminished blood supply caused by atherosclerosis. To guarantee procedural efficacy and enhanced structural integrity of stents, further developments of stent materials and manufacturing methods are particularly required. In this paper, 316L stainless steel stents fabricated by additive manufacturing are studied through heat treatment, microstructural characterization, and mechanical deformation invitro. After solution heat treatment conducted at 1200°C for durations ranging from 1 to 4 h, coarsening of columnar grains and changes in the grain boundary characters were observed, indicating the potential of microstructure modification through heat treatment. Electrochemical polishing can effectively improve surface quality by dissolving surface imperfections caused by partially sintered powders and uneven solidification processes, characteristics of additively manufactured parts. Mechanical deformation behaviors are evaluated by expansion tests before and after heat treatment. Specifically, free expansion tests are carried out to assess the mechanical performance of the stent alone, while invitro mechanical performances are evaluated using silicone arteries filled with silicone plaques, corresponding to a stenosis rate of 70%. Coarsened grain microstructures in heat-treated stents lead to improved expansion flexibility, reduced dog-boning ratio, and slightly increased recoil, as compared to the as-printed stents. Results demonstrate the viability of improving the mechanical performance of additively manufactured 316L stainless steel stents through heat treatment process.

Machine Learning-Based Prediction and Multi-Algorithm Optimization for Reducing Vertical Acceleration and Enhancing Two-Wheeler Ride Comfort

The suspension system has a major impact on the rider's comfort and stability and is essential in minimizing vibrations that are conveyed to them. Uncomfortable, exhausting, and perhaps dangerous situations can result from excessive vertical acceleration brought on by uneven roads. In order to analyze and optimize the impact of road surface, speed, and suspension stiffness on vertical acceleration, this study used machine learning regression models and optimization approaches in an experimental examination. In a total of twenty-seven trials, three separate suspension springs were put through their paces on three separate road surfaces. To ensure precise data collection, a sitting pad accelerometer and a Svantek FFT analyzer were used to determine the maximum vertical acceleration and RMS acceleration. Maximum vertical acceleration was predicted using three regression models: Support Vector Regression (SVR), Random Forest Regression (RFR), and Response Surface Methodology (RSM). In order to find the sweet spot for suspension stiffness, road surface type, and vehicle speed that would minimize maximum vertical acceleration, optimization methods like Genetic Algorithm (GA) and the Nelder-Mead approach were employed. The findings demonstrate a robust link among suspension stiffness, road surface imperfections, and speed in affecting vertical acceleration. The Random Forest Regression model attained a R² score of 0.30 and a root mean square error (RMSE) of 0.475, whereas the RSM exhibited superior performance with a R² score of 0.66 and an RMSE of 0.563. Nonetheless, SVR demonstrated subpar performance, evidenced by a R² score of -0.61 and an RMSE of 0.722. The refined suspension configuration resulting from these models decreased the maximum vertical acceleration from 1.72 m/s² to 1.656 m/s², enhancing ride comfort by about 3.68%. This work systematically improves two-wheeler suspension systems for greater ride quality, fatigue reduction, and safety. Automotive engineers, manufacturers, and researchers enhancing car suspension performance in real-world driving situations will benefit from the findings.

Optimization methods of carbon fiber composite antenna surface precision based on genetic algorithms

AbstractCarbon fiber composite materials are renowned for their exceptional properties, which meet the requirements for high‐precision antennas. Composite curing can cause deformation, resulting in antenna surface imperfections and reduced precision. As a result, most antennas are usually splice‐molded, and integral molding remains a challenge. This study aims to address the issue by optimizing the layup angle for antennas and proposing a new method for surface compensation. First, standard mathematical methods are used to determine the local fiber angle distribution within the unidirectional composite prepreg antenna, based on its geometric design. Next, the layup angle is optimized using a revised genetic algorithm for 10 different layups. Finally, a method for surface compensation is proposed to reduce surface errors. Reduction of up to 91.50% in surface error compared to the original surface. This study is expected to serve as a guide for the thin‐wall manufacturing of composite materials.Highlights Optimized layup angle for carbon fiber composite antenna manufacturing Genetic algorithm used to refine 10 different antenna layup configurations Proposed surface compensation method reduces errors by up to 91.50% Study provides a guide for thin‐wall composite material manufacturing.

Fatigue Assessment of Precorroded AlSi9Cu3 Specimens Incorporating Short Crack Propagation

ABSTRACTThis study investigates the fatigue behavior of AlSi9Cu3 aluminum die‐cast alloy after exposure to accelerated corrosive conditions. It underscores the significant impact of minor localized corrosion imperfections on fatigue strength and presents a numerically efficient method for fatigue assessment based on fracture mechanics principles. Fatigue tests under rotary bending load showed a 34% reduction in long‐life fatigue strength for precorroded surfaces compared to polished ones. Numerical simulations, which treated the precorrosion as a single crack‐like surface imperfections, provided good estimates of fatigue life, when using the mean corrosion depth as initial flaw depth. Variations in the assessed fatigue strengths for different flaw geometries and sizes ranged from 13.7% above to 22.2% below the experimental values. The findings underscore the importance of incorporating short crack propagation, particularly for small corrosive defects, in fatigue assessments to enhance accuracy and ensure component reliability and safety when corrosion‐induced imperfections are present.

Aerodynamic Impact of Repair Patches on NACA 0012 and NACA 2412 Airfoils: A Computational Study

Aircraft structures are frequently exposed to damage from environmental factors and operational wear, leading to the development of surface imperfections such as cracks. These imperfections can increase aerodynamic drag, a phenomenon known as excrescence drag. To mitigate these effects and quickly restore structural integrity, repair patches, often made from aluminium alloys and tailored to the damage, are applied following guidelines set out in the Aircraft Battle Damage Repair (ABDR) manuals. This paper investigates the aerodynamic performance impacts of applying such patches to two prominent airfoil models: the symmetric NACA 0012 and the cambered NACA 2412. Utilizing advanced computational tools including CATIA V5 for 3D modelling and CFD++ for fluid dynamics simulation, this study examines changes in aerodynamic characteristics such as lift, drag, and pressure distribution under various flow conditions. The research particularly focuses on quantifying the aerodynamic changes introduced by the patches through detailed analyses of lift curves, drag polars, and pressure coefficient (Cp) plots. Our findings aim to provide insights into the trade-offs involved in patch repairs, exploring how different patch configurations influence airfoil performance. This contributes to the development of optimized repair strategies that ensure minimal aerodynamic disruption while extending the operational lifespan of aircraft components.

Buried Interface Smoothing Boosts the Mechanical Durability and Efficiency of Flexible Perovskite Solar Cells

Flexible perovskite solar cells (F-PSCs) have the advantages of high power-per-weight, solution processability, and bending durability and have emerged as a competitive photovoltaic technology for various applications. As the core electron transport layer (ETL) in n-i-p-type device configurations, the solution-processed SnO2 generally suffers from serious defect stacking on films, compromising the charge transport properties and the performance of resulting devices. Herein, we proposed a media-filling strategy to optimize the contact quality at the buried interface by introducing Al2O3 nanoparticles on the SnO2 surface. Rather than forming a compact insulating layer, the Al2O3 can fill the grain boundaries of SnO2 and smooth the substrate surface. Optimized interfacial contact under careful concentration control can rationally minimize the contact area of the perovskite with the surface imperfections of SnO2 to mitigate trap-assisted charge recombination. Furthermore, the reduced surface roughness of SnO2 facilitates the uniform deposition and oriented growth of upper perovskite film. As a result, the target F-PSCs achieved an impressive efficiency of 23.83% and retained 80% of the initial performance after 5000 bending cycles at a radius of four mm.

Thickness-dependent surface reconstructions in non-van der Waals two-dimensional materials.

Bismuth oxychalcogenides (Bi2O2X, X = S, Se, Te), a family of non-van der Waals (non-vdW) two-dimensional (2D) semiconductors, are attracting significant attention due to their outstanding semiconducting properties and huge potential in various applications of electronic and optoelectronic devices. Surface imperfections (e.g., surface vacancies) and surface reconstructions are more likely to appear and may cause intriguing physical properties and novel phenomena in the non-vdW 2D materials than the vdW cases. Here, we explore the impacts of surface vacancies and surface reconstructions on the properties of the surfaces and 2D structures of Bi2O2X by using the first-principles method. We find that the dimerization of surface X-vacancies occurs in Bi2O2S and Bi2O2Te (001) surfaces, like that happening in Bi2O2Se. Unexpectedly, the electronic structures of Bi2O2X (001) surfaces show strong tolerance to the order of surface X-vacancies. Furthermore, we find a phenomenon of thickness-dependent surface reconstructions for non-vdW Bi2O2X ultrathin films. For a monolayer, the zipper-surface is more stable, while the dimer-surface is generally more stable for thicker films. Calculated exfoliation energies of the Bi2O2X monolayer and multi-layers are close to those of common vdW 2D materials, indicating that 2D Bi2O2X belong to easily fabricated 2D materials, even though the inter-layer binding interaction is of the non-vdW type. Our results suggest that non-vdW 2D materials can possess intriguing properties because of surface imperfections and reconstructions in comparison with vdW 2D materials.

Automated inspection of the tip condition of a welding tungsten electrode

This article introduces an innovative inspection system designed to check the condition of the welding electrode tip. The system combines 2D laser scanning with advanced analytical algorithms, enabling fast, accurate, and automated inspection. Developed PC software controls the synchronized movement of welding electrodes and the scanner, allowing the creation of 3D point clouds representing the surface of the electrodes. These data are analysed to identify deformations, contamination, and surface imperfections. The inspection results are communicated with the welding control system, contributing to improved weld quality and efficiency in industrial production processes. This innovation has the potential to change how the condition of welding electrodes is monitored in industrial sectors.

Enhancement of all-inorganic perovskite solar cell performance through the addition of CuI as a hole transport layer additive

Abstract The transport layer is one of the main factors affecting the stability and efficiency of all-inorganic perovskite solar cells (PSCs). It is still difficult to produce an HTL with the required properties using the present production methods. Based on the solubility, a new porous transport layer of CuI doped on the surface of inorganic perovskite is proposed. CuI inclusion promotes an energy level alignment that reduces ionic loss, inhibits charge carrier recombination, and improves hole extraction efficiency. CuI addition corrects surface imperfections of the perovskite and avoids defects caused by Spiro-OMeTAD pinholes, leading to excellent hole extraction performance and fast hole mobility rates. Due to this adjustment, power conversion efficiency (PCE) is improved by 26%, resulting in an optimized PCE of 12.39%. The filling factor and the short circuit current density (J sc) were increased to 17.93 mA cm−2 and 0.71, respectively. In addition, the stability of CuI is improved due to the barrier effect of inorganic Cul on air and water entering the perovskite layer. The results show that CuI doped hole transport layer film is a promising method to realize high performance and air-stable PSC.