Optimal Speed Research Articles

Enhanced Malaysian License Plate Recognition System Using an Improved YOLOv2 Model

License Plate Recognition (LPR) has gained popularity among researchers due to its wide range of applications, including law enforcement, monitoring, and toll gate systems. However, existing LPR systems still require improvements to achieve optimum accuracy and speed. The advancements in Convolutional Neural Network (CNN) variants offer potential solutions for these challenges. This primary aim of this system is to ensure accurate and efficient recognition of the vehicle plate characters using CNN techniques. This research utilizes two CNN network architectures for deep object detection to address the Malaysian License Plate Recognition (MLPR) task. The first network is designed to detect the license plate, while the second is responsible for recognizing the characters on the plate. Both networks are cascaded from the architecture of two-stage YOLOv2, providing promising speed and accuracy. The MLPR system achieved an accuracy of 98.75% and a processing speed of 0.0104 seconds, using a total of 2,200 license plate images. In conclusion, the system adapted from deep object detection techniques presents a promising solution for the MLPR problem, based on the achieved accuracy and speed.

Comparing Carotid Artery Velocities with Current ASCVD Risk Stratification: A Novel Approach to Simpler Risk Assessment.

To explore the potential of a novel approach to simplify risk assessment by comparing carotid artery velocities with current atherosclerotic cardiovascular disease (ASCVD) risk stratification method using nonlinear measurements. In this prospective study conducted at a medical center in southern Taiwan from January 1, 2020, to December 31, 2021, 1636 participants aged 40-75 years without prior ASCVD events were enrolled. Carotid flow velocity was obtained through duplex ultrasonography. ASCVD risk was categorized into two groups according to the 2022 USPSTF guidelines for primary prevention. We analyzed associations between flow indices and ASCVD risk using logistic regression and generalized additive models (GAMs). The end diastolic velocity (EDV) of common carotid artery (CCA) and the peak systolic velocity (PSV) of internal carotid artery (ICA) were inversely and nonlinearly associated with cardiovascular event risk. Multivariate logistic regression analysis with ROC curves revealed that the optimal speed for the EDV of CCA was approximately 23.75cm/s, and the optimal PSV and EDV of ICA were approximately 81.75cm/s and 26.75cm/s, respectively. The GAMs showed U-shaped relationships between elevated ASCVD risk and blood flow velocity in the carotid arteries, with inflection points of approximately 82cm/s in the PSV of ICA and near 25cm/s in the EDV of CCA. Both methods revealed similar results. The EDVs and PSVs of the CCA and ICA are associated with the development of cardiovascular events. Optimal velocity ranges were identified; however, further hemodynamic investigations are warranted.

МОДЕЛЬ НЕПРЕРЫВНОГО ТРАНСПОРТНОГО ПОТОКА БЕСПИЛОТНЫХ АВТОМОБИЛЕЙ ПРИ ДВИЖЕНИИ НА СЕРПАНТИНЕ С ВИРАЖАМИ С УЧЕТОМ СРЕДНЕЙ СКОРОСТИ ДВИЖУЩИХСЯ ВПЕРЕДИ АВТОМОБИЛЕЙ

The paper proposes a new model of continuous traffic flow of unmanned autonomous cars when travelling on a serpentine with curves, taking into accountthe average speed of the cars ahead. The review of existing traffic flow models, in particular, the models of following the leader, such as the optimal speed model, generalised traffic model, model of total velocity difference is carried out. It is noted that these models do not take into account the peculiarities of traffic on serpentines with turns. On the basis of the existing models, a new model of continuous traffic flow has been developed, the principal difference of which is the consideration of the transverse slope of the road (turn) when travelling on a serpentine. The model takes into account the action of forces on the vehicle, including gravity, driving force and centripetal force. The prospects of using the developed model in the conditions of using intelligent transport systems, when the information about the average speed of vehicles moving ahead is transmitted over the network, are shown. Th proposed model allows to take into account the transverse slope of the road on curvilinear serpentine sections when calculating speed modes, to adequately assess the carrying capacity and identify potentially dangerous sections for optimisation of design decisions when creating unmanned transport.

Optimization of combined algorithms control electric drives of installations natural gas cooling

The relevance of the problem of reducing energy consumption by electric drives of natural gas cooling units at compressor stations of main gas pipelines is substantiated. The possibility of solving this problem with the help of a combined control system containing two groups of gas air cooling devices is considered: with continuously and discretely controlled speed of electric fan drives. An algorithm for control that is optimal according to the criterion of maximum power savings is obtained, taking into account the peculiarities of temperature distribution along the length of the heat exchanger of the cooling apparatus. It is shown that under conditions of restrictions on control actions, the optimal control algorithm consists of several intervals. Relationships were obtained for calculating the optimal speed of electric fan drives of the first group, the number of switched on electric fan drives of devices of the second group, and power savings at individual control intervals. The influence of the number of devices with continuous fan speed control on the amount of power savings was assessed. The possibility of using a simplified methodology for assessing energy savings based on average indicators of the operating mode of a cooling installation is discussed.

Research on the jet active control mechanism of gas-liquid separation in helical-axial multiphase pumps

In the field of deep-water oil and gas extraction, the intermittent gas blocking phenomenon in helical-axial multiphase pumps leads to decreased pump performance and reliability, severely limiting their large-scale application and promotion in engineering projects. To address this challenge, this study is based on the principle of jet flow field control to flow separation, utilizing external energy for active intervention to suppress the separation phenomena and improve the hydraulic efficiency within the impeller flow channel. The research reveals that the jets achieved a reconstruction of the internal flow in the helical-axial gas-liquid mixed transport pumps. Using orthogonal experimental methods, a preliminary predictive analysis of the factors influencing the effectiveness of the jet system was conducted. The study explored the impact of jet parameters on enhancing the performance of helical-axial gas-liquid mixed transport pumps from the perspective of diminishing gas aggregation at the trailing edge of the rotor cascade and curtailing backflow conditions, and established a mathematical model for the head and efficiency of helical-axial multiphase pumps under jet active control. Research indicates that the jet velocity significantly impacts the efficiency enhancement of multiphase pumps, with an optimal jet speed coefficient, Cjet = 10, identified. Beyond this speed, the benefits brought by increased jet velocity do not compensate for the costs of introducing high-speed jets, and instead, may lead to a reduction in the efficiency of the pump. Jet flow field control methods alleviate the suction side gas aggregation downstream of the jet holes and in the flow domain traversed by the jets, effectively reducing the aggregation of gas phases in the rear channel of the rotor and suppressing backflow near the trailing edge of the rotor cascade. Jet control measures strengthen the working capacity of the blade in the flow direction beyond 0.35 times the length of the streamline, expanding the working range of the blade and enhancing the work efficiency of the multiphase mixed transport pump. Higher jet velocities result in more significant enhancements of blade working power, especially under conditions of high initial gas content, where the relative increase in blade load is more pronounced.

AsPINN: Adaptive symmetry-recomposition physics-informed neural networks

Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs). However, PINNs’ loss, the regularization terms, can only guarantee that the prediction results conform to the physical constraints in the average sense, which results in PINNs’ inability to strictly adhere to implied physical laws such as conservation laws and symmetries. This limits the optimization speed and accuracy of PINNs. Although some feature-enhanced PINNs attempt to address this issue by adding explicit constraints, their generality is limited due to specific question settings. To overcome this limitation, our study proposes the adaptive symmetry-recomposition PINN (AsPINN). By analyzing the parameter-sharing patterns of fully connected PINNs, specific network structures are developed to provide predictions with strict symmetry constraints. These structures are incorporated into diverse subnetworks to provide constrained intermediate outputs, then a specialized multi-head attention mechanism is attached to evaluate and composite them into final predictions adaptively. Thus, AsPINN maintains precise constraints while addressing the inability of individual structural subnetworks’ generality. This method is then applied to address several physically significant PDEs, including both forward and inverse problems. The numerical results demonstrates AsPINN’s mathematical consistency and generality, offering advantages in terms of optimization speed and accuracy with a reduced number of trainable parameters. The results also manifest that AsPINN mitigates the impact of ill-conditioned data.

Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement

Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

Toward green container liner shipping: joint optimization of heterogeneous fleet deployment, speed optimization, and fuel bunkering

AbstractContainer liner shipping companies, under the international shipping carbon reduction indicators proposed by the International Maritime Organization, must transform two key aspects: technology and operations. This paper defines a green liner shipping problem (GLSP) that integrates the deployment of a heterogeneous fleet, speed determination, and fuel bunkering. The objective is to achieve low‐carbon operations in liner shipping, taking into consideration the diversification of power systems, the use of alternative fuels in ships, and the continuous improvement of alternative fuel bunkering systems. For this purpose, we present a bi‐objective mixed integer nonlinear programming model and develop two methodologies: an epsilon‐constraint approach and a heuristic‐based multi‐objective genetic algorithm. We validate the effectiveness of our model and methods through a case study involving container ships of various sizes deployed on intra‐Asian short sea routes by SITC International Holdings Co., Ltd. The experimental results highlight the crucial role of dual‐fuel (DF) ships in the pursuit of low‐carbon strategies by liner companies, with liquefied natural gas and ammonia DF ships being the most widely used. Additionally, fuel cell (FC) ships, particularly those powered by ammonia and hydrogen, demonstrate significant carbon reduction potential. Furthermore, ships with larger container capacities have a greater cost advantage. For the GLSP, speed determination is an auxiliary decision, and the lowest speed is not necessarily the optimal choice. Decision‐makers must carefully balance competing economic and carbon emission reduction objectives, as deploying more alternative fuel ships may increase fuel bunkering and fuel consumption, resulting in a higher total operating cost.

Automated Microfluidics-Assisted Hydrogel-Based Wet-Spinning for the Biofabrication of Biomimetic Engineered Myotendinous Junction.

The muscle-tendon junction (MTJ) plays a pivotal role in efficiently converting the muscular contraction into a controlled skeletal movement through the tendon. Given its complex biomechanical intricacy, the biofabrication of such tissue interface represents a significant challenge in the field of musculoskeletal tissue engineering. Herein, a novel method to produce MTJ-like hydrogel yarns using a microfluidics-assisted 3D rotary wet-spinning strategy is developed. Optimization of flow rates, rotational speed, and delivery time of bioinks enables the production of highly compartmentalized scaffolds that recapitulate the muscle, tendon, and the transient MTJ-like region. Additionally, such biofabrication parameters are validated in terms of cellular response by promoting an optimal uniaxial alignment for both muscle and tendon precursor cells. By sequentially wet-spinning C2C12 myoblasts and NIH 3T3 fibroblasts, a gradient-patterned cellular arrangement mirroring the intrinsic biological heterogeneity of the MTJ is successfully obtained. The immunofluorescence assessment further reveals the localized expression of tissue-specific markers, including myosin heavy chain and collagen type I/III, which demonstrate muscle and tenogenic tissue maturation, respectively. Remarkably, the muscle-tendon transition zone exhibits finger-like projection of the multinucleated myotubes in the tenogenic compartment, epitomizing the MTJ signature architecture.

Coupled Motion Response Analysis for Dynamic Target Salvage under Wave Action

The strategic recovery of buoys is a critical task in executing deep-sea research missions, as nations extend their exploration of marine territories. This study primarily investigates the dynamics of remotely operated vehicle (ROV)-assisted salvage operations for floating bodies during the recovery of dynamic maritime targets. It focuses on the hydrodynamic coefficients of dual floating bodies in this salvage process. The interaction dynamics of the twin floats are examined using parameters such as the kinematic response amplitude operator (RAO), added mass, damping coefficient, and mean drift force. During the “berthing stage”, when the double floats are at Fr = 0.15–0.18, their roll and yaw Response Amplitude Operators are diminished, resulting in smoother motion. Thus, the optimal berthing speed range for this stage is Fr = 0.15–0.18. During the “side-by-side phase”, the spacing between the ROV and FLOAT under wave action should be approximately 0.4 L to 0.5 L. The coupled motion of twin floating bodies under the influence of following waves can further enhance their stability. The ideal towing speed during the “towing phase” is Fr = 0.2. This research aims to analyze the mutual influence between two floating bodies under wave action. By simulating the coupled motion of dual dynamic targets, we more precisely assess the risks and challenges inherent in salvage operations, thus providing a scientific basis for the design and optimization of salvage strategies.

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Design and Experiment of an Inter-Row Weeding Machine Applied in Soybean and Corn Strip Compound Planting (SCSCP)

To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel’s reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding.

A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems

The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the social behavior of whale hunting. However, the WOA has the defects of easily falling into local optimum and slow convergence speed. Therefore, to overcome the shortcomings of the original WOA, a novel variant of WOA called nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) is proposed in this study. In detail, the proposed NRRMWOA includes three novel strategies as compared with original WOA. Firstly, a nonlinear adaptive parameter strategy is introduced to achieve nonlinear adjustment of search pattern along with the iteration time. The second improvement is the random reuse strategy, which can fully utilize the current optimal whale to improve the solution accuracy. The thirdly proposed late disturbance mutation strategy has the function of increasing population diversity in the later stage of iteration. The three proposed strategies complement each other and significantly improve the convergence efficiency and global exploration capability of WOA. To verify the superiority of the proposed NRRMWOA, a series of typical benchmark functions are utilized to conduct comprehensive numerical experiments. Compared with three sets of comparison algorithms (five variants of WOA, five advanced algorithms, and five well-known algorithms), the ARVs (average ranking values) obtained by NRRMWOA are 1.4667, 1.9167, and 2.1333, respectively. The rankings are all the best in the corresponding group. The detailed experimental and statistics results indicate that the presented NRRMWOA has higher convergence speed and better solution quality compared with the three sets of existing algorithms. In addition, 15 real-world optimization problems are employed to further test the performance of NRRMWOA for solving engineering optimization problems. For the results of 15 real-world optimization problems, the proposed NRRMWOA achieves the best results including 8 best “Mean”, 10 best “Min.”, and 7 best “Max.”. As a conclusion, the proposed NRRMWOA is a promising and excellent algorithm.

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

The effect of welding speed on the microstructure, mechanical properties, and fatigue failure of Q690D high-strength steel

This study analyzes the effect of welding speed on the microstructure and mechanical properties of CO2 gas-shielded welded joints of 4 mm thick Q690D high-strength steel. The results show that the weld zone is mainly composed of acicular ferrite. The coarse-grained zone and fine-grained zone contain lath bainite and lath martensite. As the welding speed increases, the degree of recrystallization in the weld zone initially increases and then decreases. At the optimal welding speed of 38 cm/min, the tensile strength of the welded joint reached 692.1 MPa, with an elongation of 9.63 %. The maximum microhardness of the welded joint was 303 HV, and the residual stress was a tensile stress of 167 MPa. After fitting the S-N curve, the ultimate fatigue strength of the welded joint was 210 MPa. Both fatigue and tensile fracture surfaces displayed second-phase precipitate particles, impacting the mechanical properties.

Ultrasonic-induced amorphization and strengthening mechanisms of ultrasonic vibrations assisted friction stir Al/Ti welds

Integration of aluminum (Al) and titanium (Ti) alloys offers an effective approach to sustainable and high-quality development of the automotive and aviation industries. However, challenges arise when welding Al alloys to Ti alloys due to the formation of detrimental intermetallic compounds at the interface. With the implementation of ultrasonic vibrations, the temperature and strain rate were both increased during Al/Ti friction stir welding (FSW), leading to the formation of an amorphous interlayer, instead of intermetallic compound (IMC), at the interface. The interfacial microcracks were eliminated in the ultrasonic vibrations assisted friction stir welding (UVFSW). There were Ti particles separated from the Ti substrate and dispersed in the Al alloy, thereby resulting in a more gradual and moderate evolution of the interfacial microstructure. Due to the improved interfacial microstructure with ultrasonic vibrations, the lap shear strength was almost twice that of the conventional FSW welds within same welding conditions. Meanwhile, ultrasonic vibrations also improved the fabrication efficiency with a higher optimal traversing speed. The failure mode shifted from interface separation of FSW welds to a shear dimple fracture of the UVFSW welds, demonstrating the better plasticity and reliability of the UVFSW Al/Ti dissimilar joints.

Voyage charterparty arrangement for river tramp shipping: Green and traditional vessels comparison

Voyage charterparty arrangement (VCA) for river shipping is a common agreement between a shipowner and a charterer regarding the transportation of specific cargoes from one port to another via a tramp vessel. Optimizing VCA with the essential terms such as laytime and sailing speed of the vessel, is a crucial task for both parties to reduce potential disputes and achieve win-win outcomes. This paper models the problem from two perspectives: the VCA negotiation stage and execution stage. Firstly, according to the given probability distribution of port time, the theoretically optimal laytime is obtained by using textbook formulation of newsvendor problems (NVP). On this basis, considering the concerns about the inaccuracy of the given probability distribution and the adjustment measures taken by charterers and shipowners in reality, an integrated adjustment model is constructed to determine the optimal laytime in the VCA negotiation stage. Secondly, taking into account the vessel's uncertain lock crossing time, an operations coordination model with information sharing of the berth plan is established to obtain the optimal sailing speed under a certain confidence level in the VCA execution stage. Finally, a case study of chartering tramp vessels on the Yangtze River validates the applicability and effectiveness of the models proposed in this paper, and some important management insights are derived. The results show that establishing lower optimal speed limits and relatively shorter laytime when signing a contract with the shipowner of green river vessels is reasonable for the charterer. By notifying the shipowner of the berth plan after the green river vessel crosses through the lock, the charterer can avoid higher demurrage costs due to speeding-up. Operations coordination would be mutually beneficial without abusing the contract terms. Compared to traditional river vessels, it's more reasonable to reduce the benefits allocated to charterers from the deceleration of green river vessels by shipowners.

Enhanced Corrosion Protection of Printed Circuit Board Electronics using Cold Atmospheric Plasma-Assisted SiOx Coatings.

The miniaturization and widespread deployment of electronic devices across diverse environments have heightened their vulnerability to corrosion, particularly affecting copper traces within printed circuit boards (PCBs). Conventional protective methods, such as conformal coatings, face challenges including the necessity for a critical thickness to ensure effective barrier properties and the requirement for multiple steps of drying and curing to eliminate solvent entrapment within polymer coatings. This study investigates cold atmospheric plasma (CAP) as an innovative technique for directly depositing ultrathin silicon oxide (SiOx) coatings onto copper surfaces to enhance corrosion protection in PCBs. A systematic investigation was undertaken to examine how the scanning speed of the CAP deposition head impacts the film quality and corrosion resistance. The research aims to determine the optimal scanning speed of the CAP deposition head that achieves complete surface coverage while promoting effective cross-linking and minimizing unreacted precursor entrapment, resulting in superior electrical barrier and mechanical properties. The CAP coating process demonstrated the capability of depositing SiOx onto copper surfaces at various thicknesses ranging from 70 to 1110 nm through a single deposition process by simply adjusting the scanning speed of the plasma head (5-75 mm/s). Evaluation of material corrosion barrier characteristics revealed that scanning speeds of 45 mm/s of the plasma deposition head provided an effective coating thickness of 140 nm, exhibiting superior corrosion resistance (30-fold) compared to that of uncoated copper. As a proof of concept, the efficacy of CAP-deposited SiOx coatings was demonstrated by protecting an LED circuit in saltwater and by coating printed circuits for potential agricultural sensor applications. These CAP-deposited coatings offer performance comparable to or superior to traditional conformal polymeric coatings. This research presents CAP-deposited SiOx coatings as a promising approach for effective and scalable corrosion protection in miniaturized electronics.

Effect of the Laser Cladding Parameters on Microstructure and Elevated Temperature Wear of FeCrNiTiZr Coatings.

In order to prepare coating with good friction and wear resistance at elevated temperature on the surface of hot-working tool steel, by using a CO2 laser, FeCrNiTiZr high-entropy alloy coating with different laser scanning speeds (360, 480 and 600 mm/min, respectively) was successfully fabricated by using laser cladding technology on the surface of H13 steel in this paper. Phase constitutions, microhardness, microstructure, and wear characteristics of FeCrNiTiZr coatings under different laser scanning speeds were analyzed. It was determined that 480 mm/min was the optimal laser scanning speed. The results showed that the coating at the scanning speed of 480 mm/min consists of a BCC phase with significant lattice distortion and high dislocation density; the crystal structure is cellular crystal and dendrite crystal. The coating demonstrates the highest microhardness (842 HV0.2), which is 4.2 times that of the substrate (200 HV0.2). Its average friction coefficients at room temperature and 823 K are approximately one-seventh and one-third of the substrate's, respectively, and its wear volume is reduced by about 98% and 81% under these conditions. Compared to the substrate, the coating underwent slight abrasive wear, adhesive wear, and oxidative wear at both room temperature and 823 K. In contrast, the substrate underwent severe abrasive wear, adhesive wear, oxidative wear, and even fatigue wear.