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multi-objectives optimization of WEDM parameters on machining of AISI 304 based on Taguchi method

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第11-12期134卷 5493-5510页

作者： Azawqari, Adel Ali Amrani, Mokhtar Ali Hezam, Labib Baggash, Muhammad Abidin, Zailani Zainal Univ Malaysia Perlis Fac Mech Engn & Technol Arau 02600 Perlis Malaysia Taiz Univ Fac Engn & Informat Technol Ind & Mfg Syst Engn Dept Taizi Yemen Al Saeed Univ Fac Engn & Informat Technol Taizi Yemen Sanaa Univ Fac Engn Mech Engn Dept Sanaa Yemen Univ Malaysia Perlis Fac Mech Engn Technol Sustainable Mfg Technol Res Grp Arau 02600 Perlis Malaysia

Wire electrical discharge machining (WEDM) is utilized to cut sophisticated curves or shapes that would be challenging to cut using traditional techniques. The challenge lies in simultaneously optimizing multiple conflicting objectives, such as maximizing material removal rate (MRR) while minimizing surface roughness (SR) and kerf width, to achieve high-quality machining results. This study addresses this challenge by introducing a multi-objective optimization approach using the Taguchi method and the Design of Experiments (DOE) to optimize WEDM parameters. The WEDM model AQ327L cut a workpiece material from stainless steel AISI 304 using a 0.25-mm brass wire electrode diameter. The variable input parameters were a pulse on time (Ton), a pulse of time (Toff), and a peak current (IP). The average of means analysis (ANOM), analysis of variance (ANOVA), and single-to-noise (S/N) ratio were used to determine the effect of WEDM parameters on the responses. The multi-objectives optimization introduced the ideal setting parameters were a combination of 11.33 mu s Ton, 24 mu s Toff, and 11 A IP. ANOVA and ANOM demonstrated that the Ton followed by IP are the most influential factors in the MRR and kerf width. In contrast, the Ton followed by the Toff are the most influential factors in SR. Finally, the findings demonstrated that the multi-objective optimization by the Taguchi approach could increase the quality of the WEDM process of AISI 304.

关键词： multi-objectives optimization WEDM AISI 304 Taguchi method

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multi-objectives optimization and convolution fuzzy C-means: control of diabetic population dynamic

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RAIRO-OPERATIONS RESEARCH 2022年第5期56卷 3245-3256页

作者： El Moutaouakil, Karim El Ouissari, Abdellatif Hicham, Baizri Saliha, Chellak Cheggour, Mouna USMBA Fac Polydisciplinary Taza Engn Sci Lab LSI Rabat Morocco CAU Fac Med & Pharm MorphoSci Res Lab Rabat Morocco CAU Fac Med & Pharm Biosci & Hlth Lab Rabat Morocco

The optimal control models proposed in the literature to control a population of diabetics are all single-objective which limits the identification of alternatives and potential opportunities for different reasons: the minimization of the total does not necessarily imply the minimization of different terms and two patients from two different compartments may not support the same intensity of exercise or the same severity of regime. In this work, we propose a multi-objectives optimal control model to control a population of diabetics taking into account the specificity of each compartment such that each objective function involves a single compartment and a single control. In addition, the Pontryagin's maximum principle results in expansive control that devours all resources because of max-min operators and the control formula is very complex and difficult to assimilate by the diabetologists. In our case, we use a multi-objectives heuristic method, NSGA-II, to estimate the optimal control based on our model. Since the objective functions are conflicting, we obtain the Pareto optimal front formed by the non-dominated solutions and we use fuzzy C-means to determine the important main strategies based on a typical characterization. To limit human intervention, during the control period, we use the convolution operator to reduce hyper-fluctuations using kernels with different size. Several experiments were conducted and the proposed system highlights four feasible control strategies capable of mitigating socio-economic damages for a reasonable budget.

关键词： Diabetes optimal control multi-objectives optimization Non-dominated Sorting Genetic Algorithm II (NSGA-II) fuzzy C-means kernel convolution

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NSGA-II multi-objectives optimization algorithm for energy management control of hybrid electric vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第4期233卷 1023-1034页

作者： Deng, Tao Lin, Chunsong Luo, Junlin Chen, Bingqu Chongqing Jiaotong Univ Sch Mechatron & Vehicle Engn 66 Xuefu Rd Chongqing 400074 Peoples R China

The currently existing energy management control optimization for hybrid electric vehicle (HEV) mainly focuses on fuel economy. Apart from this, there has been some consideration of the impact of emissions, but almost no attention has been paid to drivability performance. Therefore, from the point of view of multi-objectives optimization, the influences of fuel economy, emission and drivability performance on the energy management are comprehensively considered for a parallel HEV. The energy management control parameters and driveline parameters are selected to be optimized parameters. Then, the NSGA-II (Fast Non-dominated Sorting Genetic Algorithm-II) algorithm is proposed to solve the multi-objectives optimization problem. Furthermore, the multi-objectives optimization method for HEV energy management control is established and comparatively simulated with the parallel electric assist control strategy. The results show that the evaluation index of drivability decreases by 27.12% from the maximum and the average enhancement effect of optimization falls by 20.84%. The evaluation index of fuel economy declines by 22.30% from the maximum and the average index drops by 20.26%. The comprehensive index of emission performance descends by 11.33% from the maximum. The proposed multi-objectives optimization algorithm has good convergence and distribution, and obtains more Pareto optimal solution sets, which can provide more selectivity in building HEV energy management control strategies.

关键词： Energy management hybrid electric vehicle multi-objectives optimization NSGA-II Pareto

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multi-objectives optimization Design of A-type Frame in an Electric Mining Dump Truck Considering multi-Source Uncertainties Based on the Interval Method

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MECHANIKA 2021年第2期27卷 168-174页

作者： Mi, Chengji Huang, Zhonglin Li, Wentai Kang, Di Zhang, Dong Yao, Qishui Ming, Xingzu Ni, Zhengshun Hunan Univ Technol Dept Mech Engn Zhuzhou 412007 Peoples R China Hunan Acad Forestry Sci Changsha 410004 Peoples R China

In order to improve fuzzy fatigue reliability and make weight reduction of A-type frame in an electric mining dump truck considering multi-source uncertainties from design, manufacturing and use stage, a multi-objectives optimization method based on the interval theory was proposed in this paper. The thickness of four steel plates in welded A-type frame was regarded as certain design variables, while the elastic modulus and density of material was considered as uncertain variables as well as the load at front traction joint. The relationship between optimization objectives and variables was constructed by the response surface method, and was transformed by the interval method. Then, the optimization problem was solved by the non-dominated sorting genetic algorithm, which was found that the fuzzy fatigue reliability reached up to 98.45% at the expense of some weight.

关键词： multi-objectives optimization interval method response surface method genetic algorithm multi-source uncertainties

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Cooperative optimization of cooling units arrangement on gas turbine endwall with generating adversarial network-based surrogate models

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ENERGY 2025年 323卷

作者： Song, Yu Liu, Zhao Han, Yuhui Zhang, Weixin Feng, Zhenping Xi An Jiao Tong Univ Shaanxi Engn Lab Turbomachinery & Power Equipment Sch Energy & Power Engn Xian 710049 Peoples R China

The optimization of film cooling arrangement significantly enhances the operational reliability of gas turbine blades, making it an essential design process for advanced gas turbines. Generating adversarial network-based surrogate models are considered highly advantageous for image generation. To demonstrate their application potential in improving the prediction of film cooling effectiveness over the turbine endwall, this study utilizes UNet framework as the architecture for image processing. The genetic algorithm was selected as the global optimization method. After training the surrogate model, the mean absolute error of the validation set was 0.98 %, and the correlation index was 0.999. The results showed that the optimal film hole arrangement significantly improved the endwall film cooling performance while reducing passage total pressure losses. Specifically, film cooling performance increased by 119.73 % to 158.26 %, and the passage total pressure loss was reduced by 12.23 % to 13.59 % for optimized balance samples. The increase in the total heat transfer coefficient mainly resulted from the mixing of the leakage with the mainstream downstream of the mid-passage gap. optimization achieves significant improvements in endwall cooling, with only a slight reduction in phantom cooling.

关键词： Surrogate model Generating adversarial network multi-objectives optimization Cooling arrangement Turbine endwall

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Complex Path Planning for Manipulators Based on an Improved NSGA-III Algorithm With Hopfield Network

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IEEE ACCESS 2025年 13卷 60186-60199页

作者： Sun, Xiaoxiang Wang, Jue Wang, Wei Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

In various production applications across industries such as manufacturing and agriculture, manipulators often face complex trajectories composed of numerous locally discrete paths during movement. To enhance planning efficiency and convergence accuracy, this paper proposes an improved NSGA-III multi-objective optimization algorithm based on Hopfield neural network. First, we construct an optimization objective function considering three dimensions: the length of the manipulator's motion path, motion time, and joint impact. We introduce a Hopfield neural network to replace the initial random sampling of the NSGA-III algorithm, allowing for the rapid generation of an initial optimized population. Based on non-dominance relationships, we use reference points for population crossover, mutation, and selection, thereby accelerating convergence to the optimal solution. Additionally, we employ seventh-degree polynomial smooth interpolation to improve the geometric properties of the trajectory. The main contributions include: By utilizing the energy gradient of the Hopfield neural network, we rapidly reduce the random sampling space formed by the multi-objective functions along the gradient direction towards the suboptimal solution set. This approach effectively eliminates a significant number of inferior solutions during the iterations of NSGA-III, leading to a marked improvement in the efficiency of generating the Pareto front. Specifically, the optimization efficiency is enhanced by 45.86%, the length of the robot's motion path is reduced by 77.9%, motion efficiency is increased by 51.89%, and joint motion stability is improved by 69.45%. The feasibility of the algorithm has been validated through simulations and experiments in complex three-dimensional motion scenarios.

关键词： Manipulators optimization Hopfield neural networks Robots Trajectory Linear programming Genetic algorithms Sorting Service robots Robot kinematics Manipulator trajectory planning multi-objectives optimization NSGA-III Hopfield

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Setting optimization of Distribution Network Current Protection Based on Constrained multi-objectives Backbone Particle Swarm optimization

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第3期19卷 318-326页

作者： Wang, Xue Luan, Kun North China Elect Power Univ Dept Elect Engn Baoding 071003 Hebei Peoples R China

With the increase in residential electricity consumption, the number of branch lines connected to the 10 kV main line and the load all increase. When the main line is segmented into several shorter lines, the current protections of these lines are difficult to coordinate with each other. This condition may cause the number of protection mal-operation increases, the power outage scope expands, and the reliability of the power supply decreases. Therefore, this paper studies the setting optimization method of distribution network current protections. First, a mathematical model of protection setting optimization is established with three objective functions keeping the fault clearing time minimal, the protection sensitivity highest, and the outage load minimal. Some constraints are considered, such as the cooperation between the protections of the adjacent lines. Second, the multi-objective backbone particle swarm optimization algorithm is used. The problems of the traditional particle swarm optimization algorithm that lacks population diversity and easily falls into local optimization are solved by adopting the time-varying variation and linear decreasing dynamic allocation strategy. Finally, the K-means clustering algorithm is used to get the most representative optimal solution providing a variety of feasible solutions for the practical application. This paper takes a 10 kV line as an example to test the setting optimization method and compares the optimized settings with the traditional ones. The results show that the setting optimization method based on the constrained multi-objective backbone particle swarm can effectively reduce the protection mal-operations and improve the overall performance of distribution network protections. (c) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： relay protection multi-objectives optimization backbone particle swarm optimal solution

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Hydrogen production from ammonia: Preliminary design of portable cracking system and related micro-channel reactor

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 128卷 597-612页

作者： Nguyen, Dong Ho Nguyen, Phu Quoc Kim, Sang Moon Kim, Ji Hoon Shim, Gyu Hyeon Ahn, Ho Seon Van Lang Univ Inst Comp Sci & Artificial Intelligence Lab Comp Mech Ho Chi Minh City Vietnam Van Lang Univ Sch Technol Fac Mech Elect & Comp Engn Ho Chi Minh City Vietnam Incheon Natl Univ Dept Mech Engn Incheon South Korea Incheon Natl Univ Nucl Safety Res Inst Incheon South Korea AHN Mat Inc Incheon South Korea

This study introduces an innovative and energy-efficient portable ammonia decomposition system designed for mobile applications. Key features include: (1) a capillary tube that utilizes ambient heat to evaporate liquid ammonia, reducing energy consumption by 7.7 %;(2) a plate heat exchanger that recovers heat from reactor products to preheat the ammonia feed, cutting reactor heating energy by 8.7 %;and (3) a microchannel reactor with Joule heating, which significantly lowers thermal resistance compared to conventional combustion methods. Heat balance analysis shows that heating power accounts for approximately 47 % of total generated electricity during startup and 38.9 % during steady operation. In addition, this work presents the first integration of microchannel technology with Joule heating for ammonia decomposition. A 3D model, developed in COMSOL multiphysics 6.1, explores the effects of heating power, catalyst thickness, channel length, and hydraulic diameter on reactor performance. Results reveal that the system operates under reaction-controlled conditions (Damkohler number <0.1), making heating power the most influential factor for ammonia conversion. Mass transfer is sufficiently rapid, limiting the impact of catalyst layer thickness, channel length, and hydraulic diameter on conversion but significantly affecting pressure drop. Moreover, to optimize reactor performance, a multi-objective framework is proposed, combining a modified building performance optimization (BPO) technique, artificial neural networks (ANNs), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II algorithm). Key design parameters, including channel dimensions, were optimized to maximize ammonia conversion and minimize pressure drop. The TOPSIS decision-making method identified an optimal design, achieving a 26.68 % improvement in conversion and an 85.79 % reduction in pressure drop compared to the base case. Overall, the results of this paper provide a compre

关键词： Ammonia decomposition Micro-channel reactor Joule heating multi-objectives optimization Artificial neural networks TOPSIS method

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A Novel, Self-Adaptive, multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation

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COMPUTERS 2025年第3期14卷 81-81页

作者： Ben Alla, Hicham Ben Alla, Said Ezzati, Abdellah Touhafi, Abdellah Hassan 1 Univ Sci & Tech Fac Math & Comp Sci Dept LAVETE Lab Settat 26000 Morocco Vrije Univ Brussel Dept Elect & Informat ETRO Pl Laan 2 B-1050 Brussels Belgium

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.

关键词： cloud computing environment multi-objectives optimization tasks scheduling resource allocation multiclass priority

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multi-objective optimization of the sizing of a hybrid electrical vehicle

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2017年第Sup2期53卷 S179-S190页

作者： Reinbold, Vincent Gerbaud, Laurent Vinot, Emmanuel Univ Grenoble Alpes Grenoble France IFSTTAR LTE 25 Av Francois Mitterrand F-69675 Bron France

Hybrid electrical vehicles involve two sources of energy, usually gasoline and electricity. The energy management determines the power sharing between the internal combustion engine and the electrical machine (EM). It is highly dependent on the driving cycle (i.e. the use of the vehicle). In this context, the optimal sizing of the EM is determined by: the driving cycle, the power-train characteristics (i.e. ratios and physical limitations e.g. maximum torque available) and the energy management. The key idea of this work is to involve the driving cycle and the environment of the electrical machine in a global multi-objective optimization process taking into account an optimal energy management and an accurate model of the EM based on magnetic circuit equivalent model.

关键词： Electrical machine hybrid electrical vehicle magnetic equivalent circuit sizing energy management multi-objectives optimization

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