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multi-objective optimization design of electric vehicle in-wheel motor based on Taguchi method

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2025年第6期239卷 2131-2140页

作者： Chen, Qiping Min, Shilong Xu, Zhihui Zheng, Zhiyi Zhong, Chengping You, Daoliang Jiang, Zhiqiang East China Jiaotong Univ Key Lab Conveyance & Equipment Minist Educ Nanchang Peoples R China Jiangling Motor Co Ltd Nanchang Peoples R China Jiangxi Vocat & Tech Coll Commun Nanchang Peoples R China East China Jiaotong Univ 808 Shuanggang East St Nanchang 330013 Peoples R China

In order to solve the problem of motor performance degradation caused by torque ripple and large cogging torque of in-wheel permanent magnet synchronous motor for electric vehicles, Taguchi is used to optimize the magnetic pole of built-in Permanent magnet synchronous motor, and the orthogonal optimization matrix of magnetic pole parameters is established. Through the parameter optimization matrix, the workload of pole optimization is reduced and the optimization speed is improved. The optimization results show that the orthogonal matrix optimization can not only improve the output torque of permanent magnet synchronous motor, but also further control its torque ripple and cogging. The orthogonal matrix optimization method used in this paper for V-I type built-in permanent magnet synchronous motor has a certain positive significance, and provides a certain reference value for the subsequent motor performance optimization.

关键词： Permanent magnet synchronous motor torque ripple output torque cogging torque multi-objective optimization

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multi-objective optimization of fracturing ball strength and corrosion rate with genetic algorithms and interpretable machine learning

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ADVANCED COMPOSITES AND HYBRID MATERIALS 2025年第1期8卷 1-15页

作者： Liu, Xiaoda Yang, Jing Yi, Liya Li, Donghu Wang, Qian Du, Huayun Hou, Lifeng Wei, Yinghui Taiyuan Univ Technol Coll Artificial Intelligence Jinzhong 030600 Peoples R China Taiyuan Univ Technol Coll Mat Sci & Engn Taiyuan 030024 Peoples R China Shanxi Polytech Coll Taiyuan 030006 Peoples R China

Traditional alloy design typically relies on trial and error and experience. Machine learning can significantly accelerate the discovery and design process of new materials. However, as the number of elements in the alloy and target performance metrics increase, alloy optimization becomes more challenging. To address this, this paper proposes a machine learning-based multi-objective optimization method for magnesium alloy fracturing balls. The machine learning model trained on the magnesium alloy corrosion and ultimate compressive strength database achieves an accuracy of 0.98 on the training set and 0.93 on the test set. By using a multi-objective genetic algorithm to optimize the element ratios of the magnesium alloy, Mg-6.4Al-3.4Zn-4.6Cu was obtained, with a corrosion rate of 538 mm/year and an ultimate compressive strength of 369 MPa. This provides a new method for the efficient, rapid, and precise preparation of novel degradable magnesium alloys.

关键词： Degradable magnesium alloys Machine learning multi-objective optimization Corrosion

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multi-objective optimization of counter-flow dew-point evaporative coolers for multi-scenario applications using non-dominated sorting genetic algorithm II

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APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Li, Yongcai Jing, Chengjun Sichuan Univ Coll Architecture & Environm Chengdu 610065 Peoples R China

As a green and low-carbon cooling technology, the improvement of evaporative cooling performance has always been the focus of attention. However, traditional research often lacks concurrent consideration of trade-offs in design parameters and multi-objective contradictions, focusing on unilateral decision-making and localized optimization in fixed environments. Therefore, this study innovatively couples non-dominated sorting genetic algorithm II with mathematical modeling. Based on MATLAB programming, a complex multi-objective optimization model of counter-flow dew-point evaporative cooler capable of parameter prediction, multi-scenario application and multi-dimensional optimization is developed. To reveal the driving mechanism of multivariable on performance parameters, comparative studies of single-objective optimization under three decision modes for two typical environments are reported. The results indicate that the better optimization is achieved by adopting five decision variables with a dew-point efficiency of 98.25 %. Compared with the original working condition, the cooling capacity and dew-point efficiency could be unilaterally increased by 128.73 % and 121.28 %, respectively, and the corresponding increment of space utilization rate reaches up to 56.39 % and 56.11 %. Subsequently, setting the cooling capacity, dew-point efficiency and fan energy consumption as the synergistic objective function, a trade-off optimization with multi-decision variables is performed for four different latitudes. The obtained Pareto frontier could flexibly invert the most potential structural and operation parameter recommendations. Especially in dry regions, a cooling capacity of 4800 W and a dew-point efficiency of 91.2 % could be realized. The method realizes the controllability of performance parameters and adjustability of energy-saving effect, which could provide a solution for the efficient design of cooling equipment.

关键词： Counter-flow dew-point evaporative cooling Model construction multi-objective optimization Non-dominated sorting genetic algorithm II Performance improvement

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multi-objective optimization of Single-Phase Induction Motor Considering Geometric and Winding Parameters

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年第1期20卷 447-459页

作者： An, Yuan-Sheng Ma, Cong-Gan Wang, Hui Wang, Xin-Ling Yang, Dong Harbin Inst Technol 2 Wenhuaxi Rd Weihai Shandong Peoples R China Weihai Quanxin Motor Co Ltd Tianshan Dev Rd EastEcon Dev Zone Weihai Shandong Peoples R China

The noise, vibration and harshness (NVH) performance of single-phase induction motor (SPIM) is often ignored, and the existing optimization methods of SPIM are difficult to achieve the improvement of output performance and NVH performance simultaneously. To solve this problem, a multi-objective optimization method considering both the geometric parameters and winding parameters of SPIM is proposed in this paper. Firstly, the key frequency components that contribute greatly to vibration and noise of SPIM are obtained through the vibration and noise measurement of the prototype. Then, the electromagnetic finite element model (FEM) of SPIM is established, and the geometric parameters of SPIM are optimized with the torque, efficiency and amplitudes of key frequency electromagnetic forces as the optimization objectives. After that, the analytical model of SPIM output performance is established, and the winding parameters are optimized with the torque and efficiency as the optimization objectives. Finally, the torques, efficiencies and amplitudes of key frequency electromagnetic forces of the SPIM before and after optimization are calculated by the electromagnetic FEM, and the optimization results are evaluated. The above process can be iterated until the optimization requirements is met. To verify the effectiveness of the proposed method, an SPIM with rated power of 800 W is taken as an example to illustrate the optimization process in detail. The output performance, vibration and noise of the SPIM before and after optimization are measured by dynamometer, vibration acceleration sensor and sound intensity sensor, respectively. After optimization, the torque and the efficiency of the motor are increased by about 4.9% and 0.5% respectively, and the vibration and noise are reduced by at least 76.6% and 4.6 dB (A) respectively.

关键词： Single-phase induction motor multi-objective optimization Geometric parameter Winding parameter Vibration and noise

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multi-objective optimization design method for marine deepwater relief well 3D trajectory

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MARINE GEORESOURCES & GEOTECHNOLOGY 2025年

作者： Dou, Zijun Liu, Yongsheng Xia, Jianxin Yang, Gansheng China Univ Geosci Key Lab Polar Geol & Marine Mineral Resources Minist Educ Beijing Peoples R China China Univ Geosci Hainan Inst Beijing Peoples R China

Deepwater drilling poses a high risk, and uncontrolled blowouts can significantly damage property and the environment. The relief well is considered the ultimate and most effective means for stopping blowouts. To quickly assess the rationality of the relief well trajectory design, 3D models for J-shaped and S-shaped trajectories are constructed using analytic geometry. Subsequently, the target section is represented as a spatial straight line and arc, and trajectory design equations with various known parameters are formulated using vector algebra. Ultimately, the optimal model for the relief well trajectory is established with the objectives of minimizing trajectory length, relative error, and trajectory energy. Case analysis results indicate that the design trajectory's terminal point meets the connection requirements of coordinates and borehole direction, confirming the accuracy of the design equations. In addition, multi-objective optimization offers significant advantages in relief well trajectory optimization, resulting in shorter trajectory length, minor relative error, and lower trajectory energy. Compared to the existing optimization model, the proposed model reduces trajectory energy by 6.5% and decreases the drilling risk. The relief well trajectory design method can serve as a valuable reference for future marine deepwater relief well construction.

关键词： Deepwater drilling relief well trajectory design multi-objective optimization

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multi-objective optimization of coaxial cables with foamed LDPE/HDPE dielectric layers: Correlating process parameters with foam structure and impact on piezoelectric and electrical properties

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POLYMER ENGINEERING AND SCIENCE 2025年第2期65卷 631-646页

作者： Rostami, Milad Daryadel, Mahsa Azdast, Taher Moradian, Milad Feizlou, Nima Urmia Univ Mech Engn Dept Orumiyeh Iran

Polymeric foams are one of the new candidates for use in the dielectric layer of coaxial cables to improve their piezoelectric and electrical properties. These properties are affected by the structural properties, which depend on material and processing parameters. In this research study, first, the effect of cell size, cell density, and expansion ratio was theoretically studied on the piezoelectric and electrical properties. Then the effect of talc content, processing temperature, and cooling method was investigated on the structural, piezoelectric, and electrical properties of the produced coaxial cables. The examinations revealed that the increase in cell size, cell density, and expansion ratio increases the piezoelectric coefficient and velocity of propagation and decreases attenuation. According to the analysis of variance results, the talc content parameter was the most effective parameter on cell size, cell density, and piezoelectric coefficient with the contribution of 72%, 83%, and 69%, respectively. Also, the processing temperature parameter was the most effective parameter on the expansion ratio and electrical properties with the contribution of 42%, and 41%, respectively. Finally, the produced sample with talc content of 2 wt%, processing temperature of 125 degrees C, and cooling method by water was introduced as the optimum sample by the multi-objective *** Production of foamed dielectric layers in extrusion process of coaxial cables. Effect of cell structure on piezoelectric and electrical properties. Effect of process on structural, piezoelectric and electrical properties. optimization of structural, piezoelectric, and electrical properties. Optimum conditions: 2 wt% of talc, temperature of 125 degrees C, and water cooling.

关键词： coaxial cables electrical properties multi-objective optimization piezoelectric properties polymeric foams

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multi-objective optimization-inspired set theory-based regularization approach for force reconstruction with bounded uncertainties

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 438卷

作者： Yang, Chen Yu, Qianqian Beihang Univ Sch Astronaut Beijing 100191 Peoples R China China Univ Geosci Sch Comp Wuhan 430074 Peoples R China

In force reconstruction, multi-source incomplete information makes it difficult for traditional methods to model and solve the problem accurately, especially with noise or measurement errors. Inspired by multi-objective optimization, this paper proposes a novel set theory-based regularization approach (STR) to enhance adaptability to uncertainties and improve reconstruction accuracy and robustness. The nominal force inversion is constituted and then extended into the interval uncertainty framework, and an effective orthogonal sampling-based interval prediction method is proposed to analyze the coupled effect of force inversion and uncertainty propagation. Once the uncertainty level of the complex structure is known, this prediction method can accurately and quickly estimate the fluctuation bound of the identified force. Enlightened by the completely same logic between the regularization method used in force inversion and multi- objective optimization problem, namely, simultaneously satisfying the norm minimization of the solution and the residual parameter, this study develops a novel multi-objective optimization- inspired set theory-based regularization parameter selection method. This method incorporates the interval dominance relationship to select the most competitive regularization parameter under interval uncertainties. Therefore, an accurate reconstuction framework with bounded uncertainties is finally proposed and verified by two numerical examples.

关键词： Force reconstruction Regularization approach Set theory multi-objective optimization Interval uncertainty

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multi-objective optimization framework for generative design of horseshoe-shaped pipe arrangement in pre-stressed underground bundles

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2025年 158卷

作者： He, Wen Pan, Yue Hou, Yongmao Chen, Jin-Jian Shanghai Jiao Tong Univ Dept Civil Engn State Key Lab Ocean Engn Shanghai Key Lab Digital Maintenance Buildings & I 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Tunnel Engn Co Ltd 1099 Wanpingnan Rd Shanghai 200082 Peoples R China

The underground bundle composite pipe integrated by transverse pre-stressing (UBIT) is an emerging method for underground excavation. It offers advantages such as the enhanced structural performance, reduced deformation, simplified construction process, and improved economic efficiency. However, the complexity inherent in the UBIT structure, coupled with the subjective nature of individual experience, renders the manual design of the UBIT pipe arrangement within tunnel cross-sections a challenging task. To address this, this research proposes an intelligent design method named multi-objective optimization-based generative design (MOOGD), which combines multi-objective optimization principles with generative design techniques. The proposed MOOGD has two main innovations: (a) It enables an "end-to-end" design process that enhances efficiency while minimizing human errors;(b) It incorporates five classic multi-objective optimization algorithms, offering flexibility for engineers to select or substitute algorithms based on specific project needs. The effectiveness of MOOGD is validated through a case study of the Pingli Station construction project on Shanghai Metro Line 20. It is a twolayer underground station in Shanghai Metro Line 20, with a horseshoe-shaped circular tunnel cross-section. Compared to the original engineering design, the MOOGD-based UBIT pipe arrangement design solutions using five candidate multi-objective optimization algorithms achieve significant improvements of 11.03 %, 10.64 %, 10.95 %, 10.89 %, and 10.80 %, respectively. MOOGD provides a highly efficient and reliable design tool for engineers, facilitating a rapid exploration of design alternatives and the automated generation of optimal UBIT pipe arrangements that adhere to real-world engineering specifications.

关键词： UBIT Generative design multi-objective optimization Horseshoe-shaped cross-section

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multi-objective optimization research on the sleeve structure parameters of the autonomous refuelling arm for oil tank trucks

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ENGINEERING optimization 2025年

作者： Shen, Yi Jiang, Weiyu Li, Jie Gu, Ting Yuan, Mingxin Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang Peoples R China

To improve the sealing performance and docking accuracy of the autonomous refuelling arm for oil tank trucks and extend the sleeve's service life, a multi-objective sleeve parameter optimization method combining a neural network, genetic algorithm, and entropy-weighted TOPSIS is proposed. A finite element model is established and validated through experiments. Latin hypercube sampling generates test samples, and a neural network maps the relationship between design variables and objectives. Using the genetic algorithm, the Pareto solution set is obtained with maximum extrusion force, minimum equivalent stress, and minimum volume as objectives. The optimal design is selected via entropy-weighted TOPSIS and verified through simulations and experiments. Results show that the optimized sleeve increases extrusion force by 4.97%, reduces equivalent stress by 18.94%, and decreases volume by 7.18%. The optimized sleeve demonstrates improved mechanical performance and lightweight design, verifying the effectiveness of the proposed optimization method.

关键词： Autonomous refuelling arm sleeve multi-objective optimization NSGA-II neural network entropy-weighted TOPSIS

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multi-objective optimization study of three-phase intrinsic self-sensing concrete conductive mix proportion

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JOURNAL OF BUILDING ENGINEERING 2025年 103卷

作者： Tian, Xinru Hu, Shaowei Xue, Xiang Qi, Hao Zhengzhou Univ Coll Water Conservancy & Environm Engn Zhengzhou 450001 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China

In order to prepare a three-phase intrinsic self-sensing concrete with both compressive strength and sensing performance, the Box-Behnken experimental design method in response surface methodology (RSM) was adopted. The macroscopic, microscopic and nanoscale dosages of steel fibers, carbon fibers and nanocarbon black as conductive phases were taken as the influencing factors, and a prediction model was established to predict the compressive strength of concrete and the absolute maximum of the fractional amplitude of resistivity change under uniaxial compression cyclic loading(|Delta FCR|max) as the performance index, to analyze the influence of each factor on the performance index, and further to obtain the optimal proportioning parameters of conductive phase by multi-objective optimization with objective weight assignment-grey relational degree analysis method. The results show that response surface modeling for each performance index has high accuracy in the test range. The objective weight assignment-grey relational degree analysis method enables optimal design of three-phase intrinsic self-sensing concrete mix proportion based on multi-objective performance requirements. Under certain test conditions, the compressive strength of the three-phase intrinsic self-sensing concrete was comparable to that of the matrix concrete and had good sensing performance when the dosages of steel fibers, carbon fibers and carbon black nanoparticles were 0.80 vol%, 0.50 vol% and 0.20 wt %, respectively.

关键词： Steel fibre Carbon fibre Carbon black nanoparticle objective weight assignment-grey relational degree analysis method multi-objective optimization

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