High-performance Design Research Articles

Hole processing in parts made of titanium alloys

The features of machining of titanium alloys by cutting are considered. Measures are developed to improve the machinability of these alloys. It is shown that a decrease in the vibration amplitudes of the machine—fixture—tool—part (MFTP) technological system during cutting can significantly increase the machinability of such materials. Methods are given to increase the vibration resistance of the MFTP system, the accuracy of processing and tool life when threading with taps and drilling, namely: reducing the contact area of the tool with the workpiece in the cutting zone; increasing the strength and stiffness of the tool itself. It is indicated that the first method is the main one for increasing the tool life and the quality of hole processing. Taking this into account, patented high-performance designs of taps and attachments, drills are developed. The results of their tests are presented. Keywords: titanium alloys, machinability of titanium alloys, processing of holes, taps, drills for titanium machining. ragus05@mail.ru

AI 공격용 드론 개발 방향 및 시사점

The role of attack drones in the ongoing war between Ukraine and Russia is drawing worldwide attention. In order to prepare for changes in the defense security environment and rapid decline in military resources, the Ministry of National Defense announced the National Defense Innovation 4.0 Basic Plan in March 2023, and established the concept of joint operation by reflecting the operation of AI-based manned and unmanned complex systems and new concept weapon systems. did Drones are the most important force in building manned and unmanned complex systems, and the dronebot combat system will be the basis for promoting Defense Innovation 4.0. Drones are a system that can be operated for multiple purposes that change their use according to mission equipment, and it is key to have advanced technology that is suitable for the future battlefield environment and efficient. If the existing attack drone is an operating system in which combatants remotely control the ground control device while watching the ground control device, the future direction of the development of attack drones for the Korean military to apply to dronebots is an AI attack drone in which the drone detects the target by itself and directly strikes and self-destructs. will develop into In order to develop AI attack drones, not only the performance of the drone system, but also AI-based cutting-edge technology for object tracking technology, object identification technology and shape recognition technology of reconnaissance equipment (EO/IR), which is mission equipment, as well as attack ammunition or Small, high-performance warhead design technology, ultra-small fuze design technology, shock absorption and launch control device technology, etc. must be developed to mount the projectile. In addition, as the amount of image information acquired through mission equipment increases, high-speed data transmission technology development to improve the transmission rate between the aircraft and the ground control equipment in real time must be simultaneously developed. It is necessary to quickly overcome drone and AI technologies, which are currently very poor compared to advanced countries, and take the lead in developing intelligent AI attack drones with excellent autonomy based on Korea's excellent ICT technology. Therefore, this study aims to find out the technological trends of domestic and foreign attack drones and to consider the development plans and tasks of AI attack drones.

Modelling and Multi-Objective Optimisation of Finger Joints: Improving Flexural Performance and Minimising Wood Waste

The wood industry faces the dual requirements of improving the quality of timber products and minimising waste during the manufacturing process. The finger joint, which is an end-to-end joining method for timber boards, is one of the most important aspects of engineering wood products. This study presents a numerical and optimisation investigation of the effects of finger-joint design parameters on the flexural behaviour of finger-jointed timber beams. A numerical model based on advanced three-dimensional finite element analysis was developed to model the behaviour of finger-jointed beams. Using the validated finite element (FE) model and automated parameterisation, a parametric study was conducted to assess the impact of each design parameter of the finger joint, including finger length, tip thickness, and the number of finger joints. The results indicate that the number of fingers and finger length significantly influence the maximum load capacity, while the tip thickness has a marginal effect on performance. This study identifies a design threshold of five fingers and a 14 mm finger length for achieving efficient, high-performance finger-joint designs. In addition, the multi-objective modified firefly algorithm (MOMFA) was proposed to maximise the finger joint resistance while simultaneously minimising the material waste. The optimisation shows that there will be a significant amount of wood waste when using traditional single-objective optimisation that only focuses on structural performance. In contrast, the proposed method achieves comparable load capacity while significantly reducing waste (up to 53.31%) during the joining process. The automated finite element modelling framework and holistic optimisation developed in this study can be used to design and optimise engineering wood products for construction applications.

Optimizing FPGA-Based Convolutional Neural Network Performance

In deep learning, convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs), most commonly applied to analyze visual imagery. They are also known as Shift-Invariant or Space-Invariant Artificial Neural Networks (SIANNs), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Recently, various architectures for CNN based on FPGA platform have been proposed because it has the advantages of high performance and fast development cycle. However, some key issues including how to optimize the performance of CNN layers with different structures, high-performance heterogeneous accelerator design, and how to reduce the neural network framework integration overhead need to be improved. To overcome and improve these problems, we propose dynamic cycle pipeline tiling, data layout optimization, and a pipelined software and hardware (SW–HW)-integrated architecture with flexibility and integration. Some benchmarks have been tested and implemented on the FPGA board for the proposed architecture. The proposed dynamic tiling and data layout transformation improved by 2.3 times in the performance. Moreover, with two-level pipelining, we achieve up to five times speedup and the proposed system is 3.8 times more energy-efficient than the GPU.

High-performance designs for fiber-pigtailed quantum-light sources based on quantum dots in electrically-controlled circular Bragg gratings.

We present a numerical investigation of directly fiber-coupled hybrid circular Bragg gratings (CBGs) featuring electrical control for operation in the application relevant wavelength regimes around 930 nm as well as the telecom O- and C-band. We use a surrogate model combined with a Bayesian optimization approach to perform numerical optimization of the device performance which takes into account robustness with respect to fabrication tolerances. The proposed high-performance designs combine hybrid CBGs with a dielectric planarization and a transparent contact material, enabling > 86% direct fiber coupling efficiency (> 93% efficiency into NA 0.8) while exhibiting Purcell factors > 20. Especially the proposed designs for the telecom range prove robust and can sustain expected fiber efficiencies of more than (82.2±4.1)-5.5+2.2% and expected average Purcell factors of up to (23.2±2.3)-3.0+3.2 assuming conservative fabrication accuracies. The wavelength of maximum Purcell enhancement proves to be the most affected performance parameter by the deviations. Finally, we show that electrical field strengths suitable for Stark-tuning of an embedded quantum dot can be reached in the identified designs. Our work provides blueprints for high-performance quantum light sources based on fiber-pigtailed and electrically-controlled quantum dot CBG devices for quantum information applications.

Design and In Situ Additive Manufacturing of Multifunctional Structures

Multifunctional structures (MFSs) integrate diverse functions to achieve superior properties. However, conventional design and manufacturing methods—which generally lack quality control and largely depend on complex equipment with multiple stations to achieve the integration of distinct materials and devices—are unable to satisfy the requirements of MFS applications in emerging industries such as aerospace engineering. Motivated by the concept of design for manufacturing, we adopt a layer regulation method with an established optimization model to design typical MFSs with load-bearing, electric, heat-conduction, and radiation-shielding functions. A high-temperature in situ additive manufacturing (AM) technology is developed to print various metallic wires or carbon fiber-reinforced high-melting-point polyetheretherketone (PEEK) composites. It is found that the MFS, despite its low mass, exceeds the stiffness of the PEEK substrate by 21.5%. The embedded electrics remain functional after the elastic deformation stage. Compared with those of the PEEK substrate, the equivalent thermal conductivity of the MFS beneath the central heat source area is enhanced by 568.0%, and the radiation shielding is improved by 27.9%. Moreover, a satellite prototype with diverse MFSs is rapidly constructed as an illustration. This work provides a systematic approach for high-performance design and advanced manufacturing, which exhibits considerable prospects for both the function expansion and performance enhancement of industrial equipment.

Design of High Performance and Power Efficient Flash ADC

In this paper, Flash Analog to digital converter is implemented. The designed Flash ADC consists of a resistive ladder network, comparators, the thermometer to a binary encoder and the entire design is carried out using Tanner tools employing 180nm technology. The reference voltage applied to the resistive ladder network is 1.8V. A two-stage operational amplifier is used as a comparator in the flash ADC. Here, we are modifying the structure of operational Amplifier using Power Gating Technique. Binary code is obtained from the thermometer code by utilizing a Mux based encoder by designing the MUX in domino Logic. The major problem that usually appears in flash ADC is as the number of resolution bits increases, the Area, as well as the power consumption of the circuit, also increases. In this paper, we principally concentrated to lessen the propagation delay of the ADC by optimizing encoder circuitry. With the purpose of reducing latency, Encoder is implemented using 2:1 mux based on Pseudo NMOS Logic. Performance parameters of Flash ADC such as delay as well as average power are calculated and compared.

Enhanced solar/electric-to-thermal energy conversion capability of double skeleton based shape-stabilized phase change materials

In this work, we developed a type of shape-stabilized phase change materials (SSPCMs) based on carbonized melamine foam/graphene aerogel (CMGA) as a supporting material and polyethylene glycol (PEG) as a phase change material. [email protected] SSPCMs were prepared by fabricating a series of CMGA double skeleton with different carbonization temperatures using freeze-drying and high-temperature carbonization, followed by vacuum impregnation of PEG into the CMGA double skeleton. The morphological structure, thermal storage property, shape stability, thermal conductivity, and solar/electric conversion behavior of the fabricated SSPCMs are investigated. The results showed that [email protected] SSPCMs exhibited excellent shape stability, high latent thermal storage capacity of 181.2 J/g, thermal cycling stability, enhanced thermal conductivity with 387% higher than that of pure PEG, solar-to-thermal energy conversion capability with high efficiency of 91.7% and electric-to-thermal energy conversion capability with high efficiency of 87.3%. The results of this work provide a strategy for the design and development of high performance, multiple energy conversion forms of SSPCMs for promising applications in solar energy utilization, energy-efficient buildings, and heat preservation.

Design and Analysis of High-Performance and Small-Size Piezoresistive Pressure Sensors

In this article, a rapid thermal process is proposed to manufacture piezoresistance to improve the performance of piezoresistive devices, and an absolute piezoresistive pressure sensor is prepared by anodic bonding between silicon film and glass with the cavity to reduce the size of the device. The size of the sensor is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.0\times 1.0\times0.513$ </tex-math></inline-formula> mm. At the same time, the arrangement of piezoresistance is optimized by the finite element method. In addition, the deformation of the diaphragm is measured with a profiler, which verified the feasibility of Ti as the absorption of oxygen generated in the anodic bonding process. In the pressure range of 0–200 kPa, the output voltage and nonlinearity of the pressure sensor prepared by the rapid thermal annealing (RTP) process are 91.6 mV and 0.14%FSO, respectively, and the temperature coefficient of resistance (TCR) and the temperature coefficient of sensitivity (TCS) are 571 and −1676 ppm/°C respectively. Compared with the pressure sensor prepared by the common piezoresistive process, the TCR and TCS of the designed sensor are reduced by 73% and 24%, respectively, which has the advantages of high output voltage, low TCR and TCS, and small-size.

High-performance shim coil design and engineering optimization for use in an extremely high field superconducting magnet

The extremely high field has significant advantages in imaging quality and analyzing the fine structure of substances. However, its excellent performance requires the support of a higher-performance shim technique. In this paper, a novel structural design pattern of the shim coil for a 27 T extremely high field superconducting magnet is proposed. According to the contours of the stream function, we designed and optimized the shim coil pattern and engineering processing. The novel design was realized by using the contours as the centerline, and the wire spacing was controlled at 1 mm. The performance of the novel pattern was compared with those of alternative winding schemes. The results indicate that the novel design can improve coil performance, achieving higher fidelity and lower power dissipation.

A High-Performance Vortex Adjustment Design for an Air-Cooling Battery Thermal Management System in Electric Vehicles

To boost the performance of the air-cooling battery thermal management system, this study designed a novel vortex adjustment structure for the conventional air-cooling battery pack used in electric vehicles. T-shape vortex generating columns were proposed to be added between the battery cells in the battery pack. This structure could effectively change the aerodynamic patterns and thermodynamic properties of the battery pack, including turbulent eddy frequency, turbulent kinetic energy, and average Reynolds number, etc. The modified aerodynamic patterns and thermodynamic properties increased the heat transfer coefficient with little increase in energy consumption and almost no additional cost. Different designs were also evaluated and optimized under different working conditions. The results showed that the cooling performance of the Design 1 improved at both low and high air flow rates. At a small flow rate of 11.88 L/s, the Tmax and ΔT of Design 1 are 0.85 K and 0.49 K lower than the conventional design with an increase in pressure drop of 0.78 Pa. At a relative high flow rate of 47.52 L/s, the Tmax and ΔT of the Design 1 are also 0.46 K and 0.13 K lower than the conventional design with a slight increase in pressure drop of 17.88 Pa. These results demonstrated that the proposed vortex generating design can improve the cooling performance of the battery pack, which provides a guideline for the design and optimization of the high-performance air-cooling battery thermal management systems in electric vehicles.

Experimental study of topology optimized, additively manufactured microchannel heat sinks designed using a homogenization approach

Topology optimization generates complex geometry heat sink designs having intricate features well-suited for fabrication by additive manufacturing. In particular, a homogenization approach to topology optimization creates microchannel heat sink designs wherein the material porosity distribution corresponds to microstructures of varying dimensions, thereby eliminating the need to penalize the porosities to achieve binarized solid/liquid designs. This approach is inherently able to take into consideration the capabilities and limitations of available additive manufacturing processes as the types of microstructures can be user-defined. The current study is the first to fabricate and experimentally test microchannel heat sink designs that are topology optimized using the homogenization approach. To this end, a multi-objective optimization is performed to generate a series of Pareto optimal designs that minimize pressure drop and thermal resistance. The effect of the grid resolution is investigated. The resulting topology optimized designs are found to have different geometries and performances with different grid cell sizes because this impacts the physical dimensions of the microstructures. A series of pin fin arrays with different grid cell sizes and fin thicknesses are fabricated using Direct Metal Laser Sintering of AlSi10Mg. The additively manufactured surfaces have high roughness which creates connections between pin fins with small gap sizes and creates tortuous flow paths. The smallest pin fin that consistently survived the fabrication process is ∼0.25 mm thick and hence series of topology optimized microchannel heat sinks with a 0.5 mm grid cell size are additively manufactured. The pressure drop and thermal resistance of the fabricated heat sinks are experimentally characterized, and the measured results lie on the Pareto optimality curve predicted by the topology optimization algorithm. This work demonstrates that the homogenization approach to topology optimization generates high-performance microchannel heat sink designs that can be achieved using available additive manufacturing processes.

Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer (CL) is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell (PEMFC). Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL. In this study, a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC. Moreover, a research framework combining the response surface method (RSM) and artificial neural network (ANN) model is proposed to conduct a quantitative analysis, and further a rapid and accurate prediction. With the help of this research framework, the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated. The results show that the mass of platinum, the mass of carbon, and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance. At the selected points, the sensitivity of the decision variables is ranked: volume fraction of dry ionomer > mass of platinum > mass of carbon > agglomerate radius. In particular, the sensitivity of the volume fraction of dry ionomer is over 50% at these points. Besides, the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set. This in-depth study is beneficial to provide feasible guidance for high-performance CL design.

Operational Parameter Analysis and Performance Optimization of Zinc–Bromine Redox Flow Battery

Zinc–bromine redox flow battery (ZBFB) is one of the most promising candidates for large-scale energy storage due to its high energy density, low cost, and long cycle life. However, numerical simulation studies on ZBFB are limited. The effects of operational parameters on battery performance and battery design strategy remain unclear. Herein, a 2D transient model of ZBFB is developed to reveal the effects of electrolyte flow rate, electrode thickness, and electrode porosity on battery performance. The results show that higher positive electrolyte flow rates can improve battery performance; however, increasing electrode thickness or porosity causes a larger overpotential, thus deteriorating battery performance. On the basis of these findings, a genetic algorithm was performed to optimize the batter performance considering all the operational parameters. It is found that the battery energy efficiency can reach 79.42% at a current density of 20 mA cm−2. This work is helpful to understand the energy storage characteristics and high-performance design of ZBFB operating at various conditions.

Optimization of high-performance concrete mix ratio design using machine learning

High-durability concrete is required in extremely cold or ocean environments, making the design of concrete mixes highly important and complicated. In this study, a hybrid intelligent framework for multi-objective optimization based on random forest (RF) and the non-dominated sorting genetic algorithm version II (NSGA-II) is developed to efficiently predict concrete durability and optimize the concrete mix ratio. The relative dynamic elastic modulus of concrete after 300 freeze–thaw cycles and the chloride ion permeability coefficient at 28 days are defined as the standard measures of durability. The concrete mix ratio is taken as the influencing parameter, and orthogonal test data and engineering practice data are collected as the datasets. The proposed framework is applied to a realistic expressway project in a cold region of China. The results demonstrate that (1) a hybrid intelligent framework based on RF-NSGA-II can effectively predict concrete durability and optimize the mix ratio. (2) The developed RF model has an excellent regression learning ability, while the goodness of fit (R2) of concrete durability reaches 0.9503 and 0.9551, respectively, with root mean square error (RMSE) values of only 0.096 and 0.043, the mean absolute percentage error (MAPE) values of 2.54% and 2.17%. (3) After optimization, the concrete durability reaches a high standard, with a frost resistance of >95% and a chloride ion permeability coefficient of <3*10−8 cm2/s, at a unit volume cost of only 376.77 yuan. Hence, the proposed framework can be used to effectively optimize the concrete mix design and provide guidance for similar projects.

An Overview of the Mechanical and Tribological Characteristics of Non - Ferrous Metal Matrix Composites for Advanced Engineering Applications

The ever increasing service requirement of industries like automobile, aerospace, marine, infrastructure and transportation have evolved non-ferrous metal matrix composites with different combinations of ceramic and soft reinforcements which have resulted in the improvement of their tribo-mechanical properties as compared to an unreinforced alloy. Secondary reinforcements provide safe disposal of agro-industrial waste thereby reducing environmental degradation. The scanning electron micrographs reveals fairly uniform dispersal of reinforcement in metal matrix if proper conditions are maintained and porosity levels are found to be in acceptable range. The density, hardness, wear resistance, tensile strength, fatigue strength and impact strength of non-ferrous composites have been found to be either comparable or superior to an unreinforced alloy with minimum weight and cost. The purpose of current study is to explore the nonferrous composites and propose their application area based on material properties. An attempt has been made for indepth review on the effect of controllable process parameters on tribo-mechanical properties of the non-ferrous composites. The study concludes that non- ferrous composites with single or more than one reinforcement have superior tribo-mechanical characteristics making them suitable for design of high performance, low cost and low weight to strength ratio components.

Dual mode fiber optic high-performance sensor designs for ultra-low concentrations in NIR region

Dual mode fiber optic high-performance sensor designs for ultra-low concentrations in NIR region

A flexible organic mechanoluminophore device

A flexible mechanoluminophore device that is capable of converting mechanical energy into visualizable patterns through light-emission holds great promise in many applications, such as human-machine interfaces, Internet of Things, wearables, etc. However, the development has been very nascent, and more importantly, existing mechanoluminophore materials or devices emit light that cannot be discernible under ambient light, in particular with slight applied force or deformation. Here we report the development of a low-cost flexible organic mechanoluminophore device, which is constructed based on the multi-layered integration of a high-efficiency, high-contrast top-emitting organic light-emitting device and a piezoelectric generator on a thin polymer substrate. The device is rationalized based on a high-performance top-emitting organic light-emitting device design and maximized piezoelectric generator output through a bending stress optimization and have demonstrated that it is discernible under an ambient illumination as high as 3000 lux. A flexible multifunctional anti-counterfeiting device is further developed by integrating patterned electro-responsive and photo-responsive organic emitters onto the flexible organic mechanoluminophore device, capable of converting mechanical, electrical, and/or optical inputs into light emission and patterned displays.

Species Distribution During Solid Electrolyte Interphase Formation on Lithium Using MD/DFT-Parameterized Kinetic Monte Carlo Simulations

Lithium metal batteries are one of the promising technologies for future energy storage. One open challenge is the generation of a stable and well performing Solid Electrolyte Interphase (SEI) between lithium metal and electrolyte. Understanding the complex interaction of reactions at the lithium surface and the resulting SEI is crucial for knowledge-driven improvement of the SEI. This study reveals the internal species distribution and geometrical aspects of the native SEI during formation by model-based analysis. To achieve this, a combination of molecular dynamics, density functional theory, and stand-alone 3D-kinetic Monte Carlo simulations is used. The kinetic Monte Carlo model determines the SEI growth features over a long time and length scale so that the SEI can be analyzed quantitatively. The simulation confirms the frequently postulated layered SEI structure arising from the decomposition of an ethylene carbonate/lithium hexafluorophosphate (2 M) electrolyte with lithium metal. These layers are not clearly separated, which is contrary to what is often reported. The gradient distribution of the species within the SEI therefore corresponds to a partly mosaic structured SEI at the borders of the layers. At the lithium surface, an inorganic layer of lithium fluoride and then lithium carbonate is observed, followed by an organic, more porous SEI layer consisting of lithium ethylene dicarbonate. Simulations further reveal the strong prevalence of corrosion processes of the metal, which provide more than 99% of the lithium for the SEI reaction processes. The salt contributes less than 1% to the SEI formation. Additionally, SEI formation below and above the initial interface was observable. The here presented novel modeling approach allows an unprecedented in-depth analysis of processes during native SEI formation that can be used to improve design for high battery performance and durability.

Correlation between multi-factor phase diagrams and complex electrocaloric behaviors in PNZST antiferroelectric ceramic system

Ferroelectric (FE) phase transition with a large polarization change benefits to generate large electrocaloric (EC) effect for solid-sate and zero-carbon cooling application. However, most EC studies only focus on the single-physical factor associated phase transition. Herein, we initiated a comprehensive discussion on phase transition in Pb_0.99Nb_0.02[(Zr_0.6Sn_0.4)_1−<i>x</i>Ti_<i>x</i>]_0.98O₃ (PNZST100<i>x</i>) antiferroelectric (AFE) ceramic system under the joint action of multi-physical factors, including composition, temperature, and electric field. Due to low energy barrier and enhanced zero-field entropy, the multi-phase coexistence point (<i>x</i> = 0.12) in the composition–temperature phase diagram yields a large positive EC peak of maximum temperature change (Δ<i>T</i>_max) = 2.44 K (at 40 kV/cm). Moreover, the electric field–temperature phase diagrams for four representative ceramics provide a more explicit guidance for EC evolution behavior. Besides the positive EC peaks near various phase transition temperatures, giant positive EC effects are also brought out by the electric field-induced phase transition from tetragonal AFE (AFE_T) to low-temperature rhombohedral FE (FE_R), which is reflected by a positive-slope boundary in the electric field–temperature phase diagram, while significant negative EC responses are generated by the phase transition from AFE_T to high-temperature multi-cell cubic paraelectric (PE_MCC) with a negative-slope phase boundary. This work emphasizes the importance of phase diagram covering multi-physical factors for high-performance EC material design.