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The purpose of this paper is to assess the hydrogen embrittlement sensitivity of carbon gradient heterostructure materials and to verify the reliability of the scratch method.

The surface-modified layer of 18CrNiMo7-6 alloy steel was delaminated to study its hydrogen embrittlement characteristics via hydrogen permeation, electrochemical hydrogen charging and scratch experiments.

The results showed that the diffusion coefficients of hydrogen in the surface and matrix layers are 3.28 × 10⁻⁷ and 16.67 × 10⁻⁷ cm²/s, respectively. The diffusible-hydrogen concentration of the material increases with increasing hydrogen-charging current density. For a given hydrogen-charging current density, the diffusible-hydrogen concentration gradually decreases with increasing depth in the surface-modified layer. Fracture toughness decreases with increasing diffusible-hydrogen concentration, so the susceptibility to hydrogen embrittlement decreases with increasing depth in the surface-modified layer.

The reliability of the scratch method in evaluating the fracture toughness of the surface-modified layer material is verified. An empirical formula is given for fracture toughness as a function of diffused-hydrogen concentration.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

This paper aims to design the nano-titanium dioxide (TiO₂) coating system which has superhydrophilic property, self-cleaning mechanism and antifog property as well as strong adhesion on glass substrate.

Two hydrophilic materials have been used such as TiO₂ nanoparticles as fillers and hydrophilic copolymer, Pluronic F-127 by using simple sol–gel approach. The prepared solution was applied onto glass through dip- and spray-coating techniques and then left for drying at ambient temperature.

The nano-TiO₂ superhydrophilic coating has achieved the water contact angle of 4.9° ± 0.5°. The superhydrophilic coating showed great self-cleaning effect against concentrated syrup and methylene blue where thin layer of water washes the dirt contaminants away. The nano-TiO₂ coating exhibits great antifog performance that maintains high transparency of around 89% when the coated glass is placed above hot-fog vapor for 10 min. The fog droplets were condensed into water film which allowed the transmission of light through the glass. The strong adhesion of coated glass shows no total failure at scratch profile when impacted with scratch load of 500, 800 and 1,200 mN.

Findings will be useful in the development of self-cleaning superhydrophilic coating that is applicable on building glass and photovoltaic panel.

The developed nano-TiO₂ coating is developed by the combination of hydrophilic organic copolymer–inorganic TiO₂ network to achieve great superhydrophilic property, optimum self-cleaning ability and supreme antifog performance.

The findings will be useful for residents in building glass window where the application will reduce dust accumulation and keep the glass clean for longer period.

The synthesis of nano-TiO₂ superhydrophilic coating which can be sprayed on large glass panel and cured at ambient temperature.

This paper aims to explore how graphene can improve the mechanical and anti-corrosion properties of TiO₂-SiO₂ sol-gel coating. This sol-gel coating has been prepared on aluminum alloy substrate using graphene as both nano-filler and corrosion inhibitor.

To examine the effect of graphene on mechanical properties of sol-gel coating, the abrasion resistance, adhesion strength and scratch resistance of coating have been evaluated. To reveal the effect of graphene on the anti-corrosion property of coating for aluminum alloy, the electrochemical impedance spectroscopy (EIS) has been conducted in 3.5 Wt.% NaCl medium.

Scanning electron microscopy images indicate that graphene nanoplatelets (GNPs) have been homogeneously dispersed into the sol-gel coating matrices (at the contents from 0.1 to 0.5 Wt.%). Mechanical tests of coatings indicate that the graphene content of 0.5 Wt.% provides highest values of adhesion strength (1.48 MPa), scratch resistance (850 N) and abrasion strength (812 L./mil.) for the sol-gel coating. The EIS data show that the higher content of GNPs improve both R₁ (coating) and R₂ (coating/Al interface) resistances. In addition to enhancing the coating barrier performance (graphene acts as nanofiller/nano-reinforcer for coating matrix), other mechanism can be at work to account for the role of the graphene inhibitor in improving the anticorrosive performance at the coating/Al interface.

Application of graphene-based sol-gel coating for protection of aluminum and its alloy is very promising.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

To develop and examine an efficient and reliable jujube grading model with reduced computational time, which could be utilized in the food processing and packaging industries to perform quick grading and pricing of jujube as well as for the other similar types of fruits.

The whole process begins with manual analysis and collection of four jujube grades from the jujube tree, in addition to this jujube image acquisition was performed utilizing MVS which is further followed by image pre-processing and augmentation tasks. Eventually, classification models (i.e. proposed model, from scratch and pre-trained VGG16 and AlexNet) were trained and validated over the original and augmented datasets to discriminate the jujube into maturity grades.

The highest success rates reported over the original and augmented datasets were 97.53% (i.e. error of 2.47%) and 99.44% (i.e. error of 0.56%) respectively using Adam optimizer and a learning rate of 0.003.

The investigation relies upon a single view of the jujube image and the outer appearance of the jujube. In the future, multi-view image capturing system could be employed for the model training/validation.

Due to the vast functional derivatives of jujube, the identification of maturity grades of jujube is paramount in the fruit industry, functional food production industries and pharmaceutical industry. Therefore, the proposed model which is practically feasible and easy to implement could be utilized in such industries.

This research examines the performance of proposed CNN models for selected optimizer and learning rates for the grading of jujube maturity into four classes and compares them with the classical models to depict the sublime model in terms of accuracy, the number of parameters, epochs and computational time. After a thorough investigation of the models, it was discovered that the proposed model transcends both classical models in all aspects for both the original and augmented datasets utilizing Adam optimizer with learning rate of 0.003.

The purpose of this study is to prepare thermal transfer ribbons with good alcohol resistance.

A variety of alcohol-resistant thermal transfer inks were prepared using different polyester resins. The printing temperature, printing effect, adhesion and alcohol resistance of the inks on the label were studied to determine the feasibility of using the ink for manufacturing thermal transfer ribbons. The ink formulations were prepared by a simple and stable grinding technology, and then use mature coating technology to make the ink into a thermal transfer ribbon.

The results show that the thermal transfer ink has good scratch resistance, good alcohol resistance and low printing temperature when the three resins coexist. Notably, the performance of the ribbon produced by 500 mesh anilox roller was better than that of other meshes. Specifically, the ink on the matte silver polyethylene terephthalate (PET) label surface was wiped with a cotton cloth soaked in isopropyl alcohol under 500 g of pressure. After 50 wiping cycles, the ink remained intact.

The proposed method not only ensures good alcohol resistance but also has lower printing temperature and wider label applicability. Therefore, it can effectively reduce the loss of printhead and reduce production costs, because of the low printing temperature.

Maintenance stands are the most valuable maintenance resources and provide the necessary maintenance space and maintenance facilities for aircraft maintenance. To expand the maintenance market, maintenance, repair and overhaul (MRO) urgently need to achieve a reasonable schedule between aircraft maintenance requirements and maintenance stand capability to improve aircraft maintenance continuity and reduce the risk of scratching due to aircraft movement. This study aims to design a maintenance stand scheduling (MSS) model based on spatiotemporal constraints to solve the problem of maintenance stand schedules.

To address the problem of maintenance stand schedules, this study introduces mixed-integer programming algorithm to design the MSS model on the basis of classical hybrid flow shop structure. When designing the optimization objective function of MSS modeling, the spatiotemporal constraints are mainly considered. Specifically, first, the spatial constraints between maintenance stands are fully considered so that more aircraft can be parked in the workshop. Second, the optimization objective is designed to minimize the number of aircraft movements by defining multiple maintenance capabilities of the stand. Finally, a solution based on spatiotemporal constraints is proposed in the solving process.

A set of MRO production data from Guangzhou is used as a test data set to demonstrate the effectiveness of the proposed MSS model.

The types of maintenance stands are defined and divided into four categories: fixed stand, temporary stand, half-body stand and engine ground test stand, which facilitates optimal modeling; a new scheduling model is designed considering both temporal constraints and spatial constraints, which can improve both the utilization of maintenance stand and safety (reduce the risk of scratching between aircraft).

The purpose of this study is to systematically analyze the wear of cylindrical needle bearings in rotary vector reducers under temperature rise and identify the influencing factors.

Based on the dynamic characteristics of the RV-20E reducer, the time-varying contact force of the cylindrical needle bearing and the entrainment speed of the inner and outer raceways were calculated. A mixed elastohydrodynamic lubrication model of the needle bearing, considering friction and temperature rise, was established using a dynamic rough tooth surface model. The model solved for the oil film thickness, contact stress and wear conditions of the bearing raceway contact area. The effects of the number of rolling needles, the diameter of rolling needles and surface strength on the wear characteristics were analyzed.

The results of this study show that the oil film thickness, oil film pressure and surface scratches of cylindrical needle bearings exhibit an uneven, patchy distribution under the combined effects of friction and temperature rise. When the radius of the rolling needle is less than 1.44 mm, inner ring wear is less than outer ring wear. Conversely, when the radius exceeds 1.44 mm, inner ring wear is greater. The optimal rolling needle radius is 1.6 mm. Increasing the number of rolling needles and enhancing the yield strength of the contact surface significantly extend bearing life.

This study provides valuable recommendations for optimizing bearing structural parameters and material characteristics in the design of rotary vector reducers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0242/

This study aims to investigate the surface modification on 20CrMnTi gear steel individually treated by diamond-like carbon films and nitride coatings.

For this purpose, the mechanical properties of a-C:H, ta-C and AlCrSiN coatings are characterized by nano-indentation and scratch tests. The friction and wear behaviors of these three coatings are evaluated by ball-on-disc tribological experiments under dry contact conditions.

The results show that the a-C:H coating has the highest coating-substrate adhesion strength (495 mN) and the smoothest surface (Ra is about 0.045 µm) compared with the other two coatings. The AlCrSiN coating shows the highest mean coefficient of friction (COF), whereas the ta-C coating exhibits the lowest one (steady at about 0.16). The carbon-based coatings possess excellent self-lubricating properties compared with nitride ceramic ones, which effectively reduce the COF by about 64%. The major failure mode of carbon-based coatings in dry contact is slight abrasive wear. The damage of AlCrSiN coating is mainly adhesive wear and abrasive wear.

It is suggested that the carbon-based film can effectively improve the friction-reducing and wear resistance performance of the gear steel surface, which has a promising application prospect in the mechanical transmission field.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0129/