bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Objective Metaheuristic Optimization for Enhancing Wear Characteristics in Al6060/Si3N4/Bn Hybrid Composites
Authors: Jai Rajesh P , Balambica V. , Achudhan M. 
Published: October 2024

Abstract
This paper is devoted to research work on the tribological parameters of Si3N4/BN coating composites whose goal is to minimize the wear rate and COF (coefficient of friction). A variety of experimental studies were carried out employing several parameter variations such as various amplitudes of applied loading, sliding speed, distance, and Si3N4 and BN weight fractions. The experimental results were applied to make a sense of the models which expressed the COF and the wear rate with the Si3N4 and BN weight percentages; applied load; sliding velocity as well as sliding distance. Predictive models were calibrated on platform and experimental results and have shown good agreement. Furthermore, among cause identification was performed an in-depth mistake analysis to reveal affects on the accuracy of prediction. The paper will then branch out into multi-objective optimization through applying various algorithms, such as Particle Swarm (PSO), Firefly Algorithm, Cuckoo-Search, Grey Wolf Optimization (GWO), Multi-objective Teaching-Learning Based Optimization (MOTLBO), and Genetic Algorithm mixed with Non-dominated Sorting Genetic Algorithm II (GA-NSGA-II The optimized tribological parameters were figured out. For instance, it was revealed at what weight percentages Si3N4 and BN were optimal, how applied normal load and sliding velocity should be and how far sliding distance ought to be. The work is summarized in a part depicting the validation of models as predictive and the targeting of multiple objectives to further enhance the overall tribological results. The real world implications of enhancing abrasion resistance as regards composite coatings which consist of Si3N4/BN particles and the potential uses in different industries are described as well. Apart from this, it is also suggested to perform several additional experiments for this purpose, such as the testing different optimization algorithms and analyzing different impacting parameters. It contributes to the response of the tribological influence on composite coating revealing more complex data about molecular processes that are consequently useful for shell-like materials design.
Toolpath and Tool Design Innovations in Deformation Machining for Superior AA-7075 T6 Fins
Authors: Ponnusamy Prasanth , Seenivasan S , Loganathan Ganesh Babu , Magesh Babu D , Sivalingam A , Nanthakumar S , Girimurugan R 
Published: October 2024

Abstract
A hybrid manufacturing process, deformation machining (DM) combines subtractive manufacturing with incremental forming. Monolithic components with complex profiles are created through the hybridization of manufacturing technologies, which also reduces the wastage of raw materials. The properties of geometry and surface quality of the fin made from the aluminum alloy Al-7075 T6 using the DM process’s bending mode was examined in this research, as were the effects of various approaches, toolpath methods, and tool design. To achieve the desired shape for the machined fin, various combinations of arcuate and slanting fin toolpath techniques were explored utilizing both top-down and bottom-up methods. The bottom-up method and the arcuate toolpath strategy were used to investigate various tool profiles. Using the best combination of toolpath strategy and tool profile, named as the T3 tool profile, which has a 0.6 mm nose radius and a circular sector, bottom-to-top (BTT)technique, and arcuate toolpath method, components with better geometrical features and surface roughness (SR) were produced. The analysis of the forming force in the bending approach of the DM progression was further carried out using this optimal combination.
Performance Estimation of Self-Circulating Fluidized Bed Gasification with mixtures of Biomass
Authors: Manickam Selvaraj , Jaganraj R. , Ganesh R. , Srinivasa Rao N. , Pradeep A. , Anbuchezhiyan G. 
Published: October 2024

Abstract
The thermochemical conversion method is a well-proven technology for producing synthesis gas. Mixture of biomass in varied ratios is a novel strategy that has been tested in Self-Circulating Fluidized Bed gasification (SFBG). SFBG is designed and tested using the aforesaid biomass at a mixture ratio of 75%-25% for equivalency ratios of 0.3, 0.4, and 0.5. The gasification system’s overall effectiveness was assessed by considering crucial factors, including gas production, the heating value of the generated gas, carbon conversion, and the efficiency of the system in producing cold gas.
Artificial intelligence and machine learning for defect detection in castings.
Authors: Alamuru Sunanda , Reddy G. S. , Jagannadha Raju M.V. 
Published: October 2024

Abstract
This research delves into the intersection of artificial intelligence (AI) and quality control processes in the context of casting operations, presenting a multifaceted exploration of methodologies aimed at enhancing efficiency, accuracy, and overall quality in manufacturing. The study encompasses diverse approaches, ranging from defect prediction utilizing machine learning models to applying AI techniques in various casting methods. One pivotal contribution is the introduction of Smart Quality Inspection (SQI), an innovative AI-based approach. The study details the design and implementation of a custom Convolutional Neural Network (CNN) model for SQI, achieving an exceptional accuracy rate of 99.86% in inspecting casting products. The success of SQI not only transforms traditional inspection practices but also minimizes the impact of operator, social, organizational, task, and environmental factors. The research highlights the versatility of AI in defect identification, addressing challenges in visual inspection through proposed methods such as second-order derivative and morphology operations, row-by-row adaptive thresholding, and 2-D wavelet transform. Notably, the wavelet transform emerges as a versatile technique, effectively addressing various casting defects, marking a promising avenue for automatic defect detection in castings based on X-ray inspection. While celebrating accomplishments, the study recognizes ongoing challenges, emphasizing the need for continuous refinement and innovation. Future directions include formally classifying defects, localized defect detection, and exploring controlled environmental conditions for data collection, ensuring the adaptability and reliability of AI-based approaches. Looking ahead, the vision involves seamlessly integrating AI systems into assembly lines, automating inspections, and leveraging real-time accessibility for continual improvements. Furthermore, the potential synergy with Natural Language Processing (NLP) techniques on inspection log data presents exciting possibilities for a holistic understanding of manufacturing maintenance processes. This research not only contributes to the evolving landscape of quality control but also sets the stage for a dynamic and intelligent future in the manufacturing industry.
Development of Regression model and Optimization of Process Parameters for Wire Electrical Discharge of SAE 1010 Steel using Taguchi Grey Approach
Authors: Manikandan N , Thejasree P , Raghurami Reddy D , Kumar Peyyala Pramod 
Published: October 2024

Abstract
SAE 1010 is a predominant material that possesses exceptional qualities that make it highly suitable for automotive applications, especially for use in headed fasteners and bolts. Its primary usage is in the automotive industry, although it also exhibits significant potential in various other technical domains. Various challenges in conventional machining have been resolved by adopting alternative methodologies that entail the elimination of material. Wire electrical discharge machining (WEDM) is one of the advanced machining techniques. The primary objective of this study is to propose a Taguchi-based grey technique for optimizing multiple performance indicators in the Wire Electrical Discharge Machining (WEDM) process of SAE 1010 material. A regression model has been constructed to examine the correlation between the two sets of data. The experimental design employs Taguchi’s method, with the independent variables being the status of the pulse (on/off) and the magnitude of the applied current. Material removal rate, surface roughness, dimensional variation, and form/orientation tolerance errors are considered as performance measures in this investigation and these are optimized by Taguchi-Grey approach. Performing an effective investigation on developing systems assists in enhancing the efficiency and effectiveness of manufacturing processes.
3-Blade Propeller Design and Acoustical Analysis
Authors: Sura Sreekanth , Nagaraj Goud B , Santhani Raju , Sathwik  , Manideep  
Published: October 2024

Abstract
In the recent time, the deployment and usage of small scale unmanned aerial vehicles (UAVs) have grown rapidly for a wide range of civilian and military applications. These applications range from racing, delivery and photography for civilians to land based reconnaissance operations for military purposes. The noise produced by the propellers is one of the major factors which are stopping its application in delivery of goods. As a lot of drones around the houses making annoying noises will lead to the frustration and stress to local residents. Even in the application of drone in military purpose for reconnaissance the noise generated by the propeller should be decreased to make the drone stealthy. This projects goal is to design a 3-blade propeller for a small size unmanned aerial vehicle (UAV) using propeller theories and to analyse its acoustical behaviour. The propeller design is validated by using two methods they are – Analytical, Experimental. Blade Element Theory is used for analytical analysis whereas experimental validation has been done using thrust stand. For acoustical analysis a python code has been developed based on Gutin & Deming theory. The parameters of a propeller like Diameter, RPM (Revolutions per minute), and Load distribution are iterated to design an efficient, low noise propeller which is capable to produce required amount of thrust.
Advanced Machining Performance Through Optimization of Awjm Parameters using Metaheuristic Techniques
Authors: Jai Rajesh P , Balambica V. , Achudhan M. 
Published: October 2024

Abstract
This study comprehensively deals with the optimization of parameters in abrasive water jet machining (AWJM) with material associated factors giving special focus to machining performance. By fine-tuning of water pressure, abrasive flow rate, traverse speed and standoff distance, the work provides a multi-dimensional platform for comparative analysis. In other words, it provides the predictive models for MRR and DOC helped by the knowledge gained through these experiments. The efficiency of models is proved when compared to the measured data, thus their higher accuracy of prediction is displayed. Subsequently, different meta heuristics, i.e., Firefly, Particle Swarm, Cuckoo Search, Grey Wolf Optimization, and Teaching-Learning-Based Optimization algorithms, for AWJM parameters tuning to achieve material-specific machining performance. Results show some improvements in MRR and DOC over the experimental baseline which certify the effectiveness of the presented optimization method. The scope of this paper also covers the broader perspectives of machining applications in the future. This research brings us a step closer to the development of novel machining processes that are tailored for the characteristics inherent in any material through the utilization of the right machining parameter selection. The suggested method can be used as a reliable decision base by engineers for making choice on AWJM operations for more complex materials, thus institutionalizing foundation for future research and improvisations in related material cuttings etc. The results shown here are responsible for inaugurating a new era in the development of AWJM material-design methods with more accuracy and efficiency, applied across different industries.
Sensorless control of HSPMSM based on fuzzy feedforward phase locked loop
Authors: Fan Shengwen , Wang Yunqian , Tan Jingge 
Published: October 2024

Abstract
Aiming at the problem that the dynamic and steady-state performance of the traditional phase locked loop speed identification method will become worse with the increase of the speed after obtaining the information of Counter-electromotive force or flux linkage in the sensorless control of the motor, a sliding mode observer combining fuzzy PI and feedforward phase locked loop control is proposed to realize the sensorless control of the motor. To improve the steady-state performance of the phase-locked loop PI regulator, a fuzzy PI regulator is applied to feedforward phase-locked loop control, and the proportional integral coefficient of the phase-locked loop PI regulator is adjusted through a fuzzy controller; for the dynamic performance of the motor, a feedforward phase-locked loop is used to reduce steady-state position tracking errors under load disturbances. The simulation results demonstrate that the fuzzy feedforward phase-locked loop speed identification method is suitable for high-speed motor sensorless control, and its dynamic and steady-state performance is superior to traditional phase-locked loop methods.
Enhanced output performance of perovskite-based triboelectric nanogenerators devices by surface engineering
Authors: Li Jiajia , Qin Qi 
Published: October 2024

Abstract
Finding materials with excellent dielectric properties to increase the friction charges density is an effective method to improve the output performance of triboelectric nanogenerators (TENGs). As a star material in semiconductors, perovskite has remarkable dielectric properties and can generate considerable frictional charges, which is expected to enable TENGs to achieve high performance. In this study, the organic-inorganic hybrid (MAPbI3) perovskite is used as friction materials to prepare the TENGs device. The Poly (ethylene glycol) diacrylate (PEGDA) molecule is used to modify the surface of MAPbI3 to passivize the surface defects of the film, so as to improve the performance of the device greatly. At a mechanical operating frequency of 2 Hz, after PEGDA treatment, the open-circuit voltage, short-circuit current density and transfer charges of the champion device are 276.86 V, 68.61 mA m−2 and 56.08 nC, respectively, which are 54 %, 64 % and 39 % higher than those of the pristine device. And the highest output power density of the device is increased from 3.13 W m−2 to 9.17 W m−2. The device has excellent continuous operation stability, and the optimized device can light 147 commercial blue LEDs with a power of 0.06 W.
Short-term photovoltaic power prediction based on IMRFO-StemGNN
Authors: Han Changning , Wang Qing , Wang Pingxin , Li Congcong 
Published: October 2024

Abstract
To improve the accuracy of photovoltaic power generation prediction, this paper proposes a photovoltaic power generation prediction method based on the Improved Manta ray foraging optimization (IMRFO) algorithm and Spectral Temporal Graph Neural Network (StemGNN). Firstly, by collecting photovoltaic module data and weather forecast data, a multivariate photovoltaic feature database is constructed, and key meteorological feature quantities are selected as input data for the prediction model based on the Pearson correlation coefficient. Then, a spectral time graph neural network will be built, and the IMRFO algorithm will be used to optimize the hyperparameters of the StemGNN model to improve prediction accuracy. Simulation examples show that the proposed method can effectively improve the accuracy of photovoltaic power generation prediction.
Mask optimization for optical lithography based on UNet
Authors: Song Yabo , Xu Shuang , Lin Kefan 
Published: October 2024

Abstract
Traditional photolithography methods are increasingly unable to meet the ever-stringent demands for pattern resolution and alignment accuracy, and developing efficient mask optimization techniques has become an urgent need in the industry. This paper delves into the application of Inverse Lithography Technology (ILT) in nanoscale photolithography and proposes an improved mask optimization algorithm. The backbone network of this algorithm employs a UNet architecture, trained initially on a prepared dataset comprising original masks and the ones optimized through traditional methods. The pre-trained backbone network generates a coarse mask, which is then inputted into a correction layer to refine the mask, enhancing pattern accuracy and processing efficiency. Compared to traditional gradient-based mask optimization methods, neural ILT demonstrates superior effectiveness, which enhances the efficiency and pattern quality of the lithography process while reducing production costs.
An analog switch circuit structure suitable for low-distortion transmission
Authors: Lv Zhengze , Wei Hailong , Wu Qi 
Published: October 2024

Abstract
A MOSFET analog switch circuit structure suitable for low-distortion bidirectional signal transmission is proposed. By introducing a “threshold voltage matching MOSFET” into the MOSFET switch driver circuit structure, the driving voltage corrected by the threshold voltage is generated by the driving circuit, eliminating the problem of poor conduction on-resistance flatness in traditional analog switch circuit structure due to MOSFET second-order effects and non-constant overdrive voltage. The circuit completed schematic and layout design based on the 0.13 μm BCD process. Simulation results show that the switch has bidirectional transmission capability, with a typical on-resistance value of 115 Ω. It achieves less than 5% on-resistance non-flatness under resistive loads ranging from 500 Ω to 1 KΩ, which is more than 50% improvement compared to traditional analog switch structures. Moreover, the on-resistance flatness is insensitive to changes in load and temperature.
Bi-level planning of electric vehicle charging station considering energy consumption per unit mileage
Authors: Shen Kunpeng , Li Keyao , Yu Yibao , Yang Wei 
Published: October 2024

Abstract
This article addresses the phenomenon of neglecting traffic conditions and congestion levels in existing EV charging load modeling, and innovatively considers the energy consumption per unit mileage of electric vehicles, presents a bi-level planning model for EV charging stations. Firstly, based on the traffic network model, the speed flow model is introduced, and the mileage energy consumption model is established. Combined with the Dijkstra method, the driving path is planned, and the EV charging demand model is formed. Secondly, a bi-level programming model of the charging station, with the charging station side and user side as upper and lower layers is established, and the KKT (Karush-Kuhn-Tucker Conditions) condition is introduced to decouple the upper and lower layers. The Improved Whale Optimization Algorithm (IWOA) and Voronoi diagram are used to solve the model and determine the service range and capacity of the charging station. Finally, the proposed method’s rationality and effectiveness are confirmed through an example taken from a region in south.
Optimizing energy storage configuration scheme considering optimization of electricity prices, carbon emissions, and flexible load
Authors: Xu Jian , Du Changyu , Li Moujun 
Published: October 2024

Abstract
This paper presents a method for optimizing energy storage considering electricity prices, carbon emissions, and load flexibility. It uses an advanced K-means algorithm to cluster photovoltaic data, creating typical output scenarios. The study then explores how optimizing these factors affects EV charging, factoring in environmental temperature effects on EV power consumption to develop an EV charging model. Incorporating demand response, it establishes an economic model focusing on the costs of electricity carbon emissions and revenue from electricity sales. Results show that optimizing these elements in energy storage design can cut emissions and enhance economic viability.
A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids
Authors: Li Yongxin , Wang Dazhi , Ni Yongliang , Song Keling 
Published: October 2024

Abstract
The droop technique has its limitations, including poor current-sharing accuracy and bus voltage deviations that result from its inherent characteristics. Based on traditional droop control, a more effective droop control approach scheme based on battery cells’ state of charge (SOC) is proposed. By adding SOC values to the droop coefficient, it is able to equalize both the SOC and the energy storage modules’ output power. Thus, it succeeds in achieving the objective of balancing the energy storage batteries’ SOC and output power. Furthermore, to compensate for the voltage deviation, an adaptive PI controller is incorporated into the secondary loop of the system. The effectiveness of the suggested control method is evaluated using the MATLAB/Simulink platform.
A single-frequency Doppler radar target tracking method based on the IPSO algorithm
Authors: Jiang Yaxuan , Ding Yipeng 
Published: October 2024

Abstract
In this paper, a target tracking algorithm, which is based on improved particle swarm optimization (IPSO) technology, is proposed for single-frequency Doppler radar (SF-DR). First, the target detection model corresponding to an SF-DR structure is constructed. In contrast to conventional Doppler radar systems, our proposed model achieves a more simplified radar design by decreasing the quantity of transmitting elements, which can not only contribute to the radar engineering parameter optimization, such as radar size, cost, and power consumption, but also improve the echo quality by avoiding the RF interference between transmitting components. Then, based on the target detection model, an IPSO-based target localization algorithm is proposed. To improve the computational efficiency and target positioning accuracy, the iterative criterion of the inertia weight parameter is modified. Finally, numerous experiments were carried out, and the outcomes confirmed the efficacy of the proposed approach.