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multi-objective optimization for CF/PPS-stainless steel induction welding with pin structure based on SSA-BP neural network

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ADVANCED COMPOSITE MATERIALS 2025年

作者： Li, Hao Pang, Leishuo Wang, Wenchao Yu, Tian Zhang, Zijian Li, Shipeng Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300072 Peoples R China Aerosp Precis Prod Inc Ltd Henan Key Lab Fastening Connect Technol Xinyang 464000 Henan Peoples R China

Carbon Fiber Reinforced Thermoplastic Plastic (CFRTP) has been increasingly used in aerospace and automotive manufacturing with its excellent mechanical properties. Based on the melt-curing characteristics of thermoplastic composites, combining the full-thickness reinforced joining technology with induction welding can provide an effective way for high-strength joining. In this paper, the full factorial experimental design method is used to deeply explore the influence law of welding time, consolidation force and heating current on the tensile properties of welded joints. Combined with Sparrow Search Algorithm (SSA) and BP neural network, a welding joint tensile strength prediction model was constructed. In addition, a multi-objective model based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was developed. The multi-objective optimization method of TOPSIS was used to select the optimal parameter combinations with ultimate tensile strength and first debonding strength as the optimization objectives: welding time of 29.995 min, consolidation force of 638.669 N, and heating current of 443.351 A. Experimental studies have shown that the optimized welding joints have an increase in ultimate tensile strength of up to 32.4 % and an increase in first time debonding strength of up to 47.0 % with respect to the non-optimized welding joints.

关键词： Cfrtp induction welding ssa-bp neural network 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 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 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 for a combined heat and power unit with steam ejector at varied boundary: Energy, Economic and Environment (3E) analysis

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ENERGY 2025年 323卷

作者： Chen, Ranjing Cao, Yue He, Tianyu Si, Fengqi Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ 2 Sipailou Nanjing 210096 Jiangsu Peoples R China

Combined heat and power (CHP) represents a promising technology for achieving carbon neutrality. To enhance the energy, economic and environment (3E) performance of the ejectors integrated CHP unit, a data-corrected mechanism model was firstly developed. Subsequently, the limitations of different heating modes are analyzed at full production and under production. The maximum heat load of the condenser and availability of exhaust flow are primary concerns for exhaust heating. Extraction flow presents a high-grade heat source, yet it has an adverse effect on the system power output and results in a notable increase in fuel consumption. With a novel marginal heating efficiency, economic, efficiency and environment analysis was conducted, then a global sensitivity index analysis was performed in order to reveal the contribution of each parameter. The profitability of high back pressure heating, steam ejector and extraction heating is increasingly strong, but their impact on efficiency and environment must be considered. Finally, multi-objective optimization is conducted, and the correlations of each target are examined. Selected from the Pareto front, the marginal heating efficiency is 2.52, heating profit is 11424.9 $/h and the emission is 448.1 tCO2/h at full production conditions.

关键词： Combined heat and power Steam ejector Data and mechanism modeling Energy Economic and environment (3E) analysis multi-objective optimization

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multi-objective optimization for submarine optical fiber cable route planning based on collaborative reinforcement learning with LPGP framework

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OPTICAL FIBER TECHNOLOGY 2025年 92卷

作者： Zhao, Zanshan Gao, Guanjun Gan, Weiming Zhang, Jialiang Wang, Zengfu Wang, Haoyu Guo, Yonggang Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China Chinese Acad Sci Inst Acoust Hainan Acoust Lab Haikou Peoples R China Northwestern Polytech Univ Sch Automat Xian Peoples R China Chinese Acad Sci Inst Acoust Beijing Peoples R China

Submarine optical fiber cables are essential to international communication, transmitting approximately 99% of global traffic. The cost and survivability of these cables are key factors that must be carefully considered during the design stage. However, the cost of submarine cables and the risks closely associated with their survivability have not yet been decoupled and simultaneously optimized. In this paper, we propose a Local Pareto to Global Pareto (LPGP) paradigm for multi-objective optimization. Based on this paradigm, we design an offline collaborative reinforcement learning LPGP (Off-CRL-LPGP) framework that effectively decouples and simultaneously optimizes the cost and risk of submarine optical fiber cable routing. The results demonstrate that Off-CRL-LPGP reduces accumulated costs by 28.83% compared to ant colony optimization (ACO) under the same risk conditions, while requiring significantly less computational time. Compared to multi-agent cross reinforcement learning (MA-XRL), under the same accumulated risk and accumulated cost conditions, the Off-CRL-LPGP could respectively reduce accumulated cost by 3% and accumulated risk by 1.1%, at the expense of some additional computational time. Compared to online summation for global Pareto (On-Sum-GP), Off-CRL-LPGP could respectively reduce accumulated cost by 7.8% and risk by 23.48%. We also investigate the impact of algorithm combinations on the performance of Off-CRL-LPGP. Alternating Q-learning and SARSA (alternate Q-S/S-Q) could reduce accumulated costs by 3.83% and risk by 13.78%, while improving the convergence level for cost by 2.18 times and for risk by 3.30 times. Furthermore, data smoothing method proposed in this work reduces accumulated cost and risk by 4.58% and 6.17%, respectively, and improves stability in 97.66% of iterations, with a maximum stability improvement of 6.64 times.

关键词： Submarine cable route planning multi-objective optimization Local Pareto to global Pareto Collaborative reinforcement learning

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multi-objective optimization of inlet and outlet in a circular culture fish tank using the GBDT-NSGAII algorithm and computational fluid dynamic

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AQUACULTURAL ENGINEERING 2025年 110卷

作者： Zhang, Shanhong Yu, Guanghui Guo, Yu Wang, Yang Li, Daoliang Zhao, Xiaoli O'Brien, Dannie D. Chiang, Jui-Che China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China China Agr Univ Shenzhen Zhongnongda Frontier Technol Res Inst Shenzhen 518116 Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Agr Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Shandong Polytech Sch Bioengn Jinan 250104 Peoples R China AquaBioTech Grp Cent Complex Naggar St Targa Gap MST-1761 Mosta Malta

In a circular culture tank, hydrodynamics plays an essential role for the growth and development of aquatic products in the recirculating aquaculture system (RAS). The challenge of maintaining the maximum effective energy utilization rate and achieving uniform velocity distributions remains a significant hurdle. To solve this problem, a novel multi-objective optimization approach was employed, utilizing the Gradient Boosting Decision Tree-Non-Dominated Sorting Genetic Algorithm II (GBDT-NSGAII) algorithm, to optimize the parameters of inlet and outlet of culture tanks as achieving suitable hydrodynamics. Specifically, the initial circular tank is regarded as the benchmark, four vital parameters of circular tank including nozzle quantities, nozzle diameters, outlet diameters, and inlet angles has been investigated by CFD model. The development of a GBDT-NSGA-II-based model and parameter optimization is conducted to obtain the Pareto front, with the objective of maximizing effective energy utilization rate while minimizing velocity STD. The main results show that: 1) At an inlet velocity of 0.5 m/s, the benchmark successfully establishes dynamic homogeneous velocity fields, while exhibiting a highly pressurized domain in the inlet pipe primarily influenced by the nozzle diameter. 2) The GBDT model demonstrates excellent predictive capability for the CFD database, with an RMSE of 0.002 m/s for average velocity and an RMSE of 0.304 % for velocity STD.3) The diameter of nozzles (12.76 mm) and the number of nozzles (7) have a highly significant influence on both average velocity and velocity uniformity compared to the benchmark model among the 63 groups of optimal Pareto fronts. 4) The optimal inlet-outlet parameter combination mainly includes an inlet velocity of 0.49 m/s and an outlet diameter of 22.26 mm, which improves the energy utilization rate up to 88 % compared to the benchmark model. The GBDT-NSGA-II model, driven by CFD simulation dataset, can effectively repla

关键词： Circular culture tank Velocity field Energy utilization CFD and GBDT multi-objective optimization

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multi-objective optimization in the design of load sharing systems with mixed redundancy strategies under random shocks

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 85卷

作者： Yaghtin, Mohammad Javid, Youness Ardakan, Mostafa Abouei Kharazmi Univ Engn Fac Ind Engn Dept Tehran Iran

The redundancy allocation problem (RAP) focuses on assigning one or more components in parallel to enhance the overall reliability of a system. Selecting a redundancy type (active or standby) for each component is a critical challenge in system design. Active components can share the load among themselves (unlike standby components), and standby components are not subjected to shock attacks (unlike active components). This research presents a multi-objective optimization model to enhance system reliability and minimize costs. The proposed model is designed for a load-sharing system with a series-parallel structure, subject to shock attacks. Reliability (availability) is calculated using a stochastic approach based on the Markov chain, and the NSGA-II algorithm solves the multi-objective optimization problem. Two numerical examples investigate the proposed approach, identifying appropriate solutions through Pareto frontiers and analyzing the impact of load-sharing and shock attacks on optimization results.

关键词： Reliability Load sharing Shock attacks multi-objective optimization Mixed redundancy strategies

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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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