Experimental Correlation for Splashing Condition of Droplets on Solid Substrates

Droplet splashing behaviors of water-ethanol binary mixture liquids on roughened solid surfaces were experimentally observed in order to investigate the effects of surface tension, viscosity, and wettability/surface roughness on the splashing occurrence. The range of the droplet volumes was from 1.7 μL to 32.6 μL. The ranges of the surface tension and the viscosity were from 21.1 mN/m to 71.9 mN/m, and from 1 mPas to 2.91 mPas, respectively. The surface roughness range was from 0.03 μm to 1.25 μm for Ra. The present experimental data were evaluated on the basis of the existing models. Resulting from these experiments, a simple model using the Ohnesorge number evaluated by the capillary length was proposed and the accuracy of the predicted critical values such as the critical Weber and Reynolds numbers were discussed. The result indicated that the liquid properties and the quantification of the surface condition such as surface roughness are important factors for the prediction of the splashing behavior.

In-Vitro Analysis of FeMn-Si Smart Biodegradable Alloy

Special materials are required in many applications to fulfill specific medical or industrial necessities. Biodegradable metallic materials present many attractive properties, especially mechanical ones correlated with good biocompatibility with vivant bodies. A biodegradable iron-based material was realized through electric arc-melting and induction furnace homogenization. The new chemical composition obtained presented a special property named SME (shape memory effect) based on the martensite transformation. Preliminary results about this special biodegradable material with a new chemical composition were realized for the chemical composition and structural and thermal characterization. Corrosion resistance was evaluated in Ringer’s solution through immersion tests for 1, 3, and 7 days, the solution pH was measured in time for 3 days with values for each minute, and electro-corrosion was measured using a potentiostat and a three electrode cell. The mass loss of the samples during immersion and electro-corrosion was evaluated and the surface condition was studied by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). SME was highlighted with differential scanning calorimetry (DSC). The results confirm the possibility of a memory effect of the materials in the wrought case and a generalized corrosion (Tafel and cyclic potentiometry and EIS) with the formation of iron oxides and a corrosion rate favorable for applications that require a longer implantation period.

Evaluation of the Relationship Among Dressing Conditions Using Prismatic Dresser, Dressing Resistance, and Grinding Characteristics

In the grinding process, the grinding wheel surface condition changes depending on the dressing conditions, which affects the ground surface roughness and grinding resistance. Several studies have been reported on the practical application of dressing using prismatic dressers in recent years. However, only a few studies that quantitatively evaluate the effects of differences in dressing conditions using prismatic dresser on the ground surface roughness and grinding resistance have been reported. Thus, this study aims to evaluate quantitatively the effect of the difference in dressing conditions using the prismatic dresser on the ground surface roughness and grinding resistance by focusing on the dressing resistance. In the experiment, dressing is performed by changing the dressing lead and the depth of dressing cut with a prismatic dresser, and the ground surface roughness and grinding resistance are measured. Consequently, by increasing the dressing lead and the depth of dressing cut, the ground surface roughness increased, and the grinding resistance decreased. This phenomenon was caused by the increase in dressing resistance when the dressing lead and the depth of dressing cut were increased, which caused a change in the grinding wheel surface condition. Furthermore, the influence of the difference in dressing conditions using the prismatic dresser on the ground surface roughness and grinding resistance can be quantitatively evaluated by using the dressing resistance.

Impact Of Traffic Characteristics and Pavement Surface Condition on Noise Level for Rigid Pavement

The proper design of a road’s surface layer can result in pavements that are not only better in terms of ride comfort and safety, but also in terms of noise reduction. The use of low-noise pavements may be an effective measure to reduce the acoustic pollution generated by road traffic This study aims to consider the effect of changed pavement features on the noise level. Tire/pavement noise is a major contributor to traffic noise at highway speeds. The effects of pavement properties, including air-void content, gradation properties, roughness, texture, pavement surface condition are major contributors to traffic noise at highway speeds. As the overall texture and IRI, increase noise levels. The results showed that greater air void content decreases the level of high-frequency noise.

A Review of Studying the Relationship of Traffic Stream Characteristics and Noise Intensity with Flexible Pavement Surface Condition

In general, roads are a very important infrastructure to facilitate people’s access to their social and economic activities, so knowing the things that affect the efficiency of these roads is very important and how to maintain them. One of the aims of this research is to shed light on previous studies that showed the relationship between traffic characteristics and noise intensity on the condition of the breakable Pavement. The results of previous studies show that flexible Pavement has 19 potential failures and how to address them. There is a relationship between the characteristics of traffic flow and failures that occur in flexible paving. It is also possible to calculate the road condition by knowing the types of faults on the road using several roads or by using specific formulas to calculate the road condition index. There are relationships between the road condition index, the vehicles’ speed, and the models that link Noise to speed. And that most of the relationships that were used to calculate the elastic pavement condition index and its relationship to the movement characteristics are statistical relationships using ANOVA, recreation, and R2

Enhanced Hydrofobicity of Polymers for Personal Protective Equipment Achieved by Chemical and Physical Modification

The article presents significant results in research on creating superhydrophobic properties of materials which can be used as an interesting material for use in self-cleaning polymer protective gloves and similar applications where the superhydrophobicity plays a significant role. In this work the influence of laser surface modification of MVQ silicone rubber was investigated. The research was conducted using a nanosecond-pulsed laser at 1060 nm wavelength. After a process of laser ablation, the surface condition was examined using a SEM microscope and infrared spectroscopy. During the tests, the contact angle was checked both before and after the laser modification of samples pre-geometrised in the process of their production. The test results presented in the paper indicate that the chemical and physical modifications contribute to the change in the MVQ silicone rubber contact angle. A significant increase (by more than 30°) in the contact angle to 138° was observed. It was confirmed that surface geometrisation is not the only factor contributing to an increase in the contact angle of the analyzed material; other factors include a change in laser texturing parameters, such as mean beam power, pulse duration, scanning speed and pulse repetition frequency.

Study on the Road Friction Database for Automated Driving: Fundamental Consideration of the Measuring Device for the Road Friction Database

This study deals with the possibility of construction of a database on the braking friction coefficient for actual roads from the viewpoint of traffic safety, especially for automated driving, such as level 4 or higher. At these levels of automated driving, the controller needs to control the vehicle. However, the road surface condition, especially the road friction coefficient on wet roads and snowy or icy roads, changes greatly, and in some cases, changes by almost one order. Therefore, it is necessary for the controller to constantly collect environment information, such as the road friction coefficients, and prepare for emergencies, such as obstacle avoidance. However, at present, the measurement of the road friction coefficients is not systemically performed, and a method for accurately measuring has not been established. In order to improve this situation, this study examines a method for continuous measurement of the road friction characteristics, such as the μ-s characteristics. It is shown that the μ-s characteristics are continuously measured using the MF generally used in tire engineering, and the friction characteristics identified from the results are sufficiently satisfactory.

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.