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Static and dynamic analyses and multi-objective optimization of wafer thinning machine's design variables

ENGINEERING RESEARCH EXPRESS 2024年第3期6卷 035429-035429页

作者： Xie, Guijiu Zhang, Wenbin He, Dongge Yi, Zhongbo Liu, Zhu Wang, Shibo Wang, Yan CETC Beijing Elect Equipment Co Ltd Beijing 100176 Peoples R China Tianjin Univ Sci & Technol Sch Mech Engn Tianjin 300457 Peoples R China

In order to meeting the physical strength, heat dissipation and dimensional requirements of chips, the wafer surface needs to be thinned by wafer thinning machines. In the design of wafer thinning machine, the analysis and optimization of castings is an important and complex issue. In this study, the multi-objective optimization of wafer thinning machine' s design variables are executed due to static and dynamic analyses. According to the analysis results, the design quality, amplitude and equivalent stress of the casting are optimized. The inner diameter of the ring, the height of the ring, the height of the middle groove, and the height of the groove on both sides are selected as the main design variables of the optimization. The results show that the static deformation of the optimized cast structure is 8% lower than the original structure, the overall mass is 4% lower, the operating frequency is 3.5% lower, higher stability, smaller mass and amplitude are obtained after optimization. The research has a great significance for the wafer thinning machines design, and provides theoretical guidance for the development of other lithography equipments.

关键词： wafer thinning machine static and dynamic characteristics harmonic response analysis multi-objective optimization design

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design optimization of cross-sectional configuration of rib-reinforced thin-walled beam

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THIN-WALLED STRUCTURES 2009年第8-9期47卷 868-878页

作者： Zhang, Zonghua Liu, Shutian Tang, Zhiliang Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116023 Peoples R China

In this paper, a rib-reinforced thin-walled hollow tube-like beam (named as rib-reinforced beam) is presented for potential application in vehicle bumper. Through numerical simulation of the bending behavior under impact loads, the rib-reinforced beam is compared with thin-walled hollow tube-like beams filled with and without foam materials (empty beam and foam-filled beam) in crashworthiness. The effects of the shape of the reinforced rib are investigated and the shape optimization design is performed for increasing energy absorption and reducing the initial peak force. A multi-objective crashworthiness optimization formulation including maximum energy absorption, maximum specific energy absorption and minimum initial peak force is constructed based on the ideal point method (IPM). The optimum configuration of the reinforced rib is given with a normalized cubic spline function. Numerical example results show that, compared with the empty and foam-filled beams with same weights, the optimized rib-reinforced beam has higher energy absorption performance and lower initial crash force. It is found that for the rib-reinforced beam little rumple is formed around the compressed indention, which helps to retard the collapse of the side wall and means more energy absorption. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Impact energy absorption Rib-reinforced thin-walled beam multi-objective optimization design Crashworthiness

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optimization design of Gear Train based on Particle Swarm optimization Algorithm

Optimization Design of Gear Train based on Particle Swarm Op...

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International Conference on Mechatronics, Robotics and Automation (ICMRA 2013)

作者： Wu Chang-wei Wu Yong-hai Ma Cong-bin Wang Cheng Huaiyin Inst Technol Fac Traff Engn Huaian Peoples R China

ISBN: (纸本)9783037858066

Particle swarm optimization algorithms have lots of advantages such as fast convergence speed, good quality of solution and robustness in multidimensional space function optimization and dynamic target optimization. It is suitable for structural optimization design. In this paper, manual transmission gear train of a tractor is taken as research object, the minimum quality and minimum center distance of the gear train is taken as optimization goal, the gear ratio, modulus, helix angle, tooth width and equilibrium conditions of the axial force are taken as the constraints, a multi-objective optimization model of the gear train is established. The optimal structure design programs and Pareto optimal solution are obtained by using particle swarm optimization algorithm.

关键词： Gear train multi-objective optimization design Particle Swarm optimization

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Cross section shape optimization design of fabric rubber seal

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COMPOSITE STRUCTURES 2021年 256卷 113047-113047页

作者： Dong, Yifeng Yao, Xuefeng Xu, Xiaoyao Tsinghua Univ Dept Engn Mech Appl Mech Lab Beijing 100084 Peoples R China

In this paper, the cross section shape of fabric rubber seal is designed and optimized. First, the cross section shape optimization design method is introduced based on the structural features of fabric rubber seal. Then, according to the key performance of fabric rubber seal, the optimization constraints, optimization objective and corresponding calculation methods are determined. Finally, the optimal cross section shapes of fabric rubber seal are obtained under different gas pressure differences, temperatures and multi-objective optimization design. The results indicate that the shape optimization method has an important guiding for the design and manufacture of the fabric rubber seal.

关键词： Cross section shape optimization Fabric rubber seal Gas pressure difference Temperature multi-objective optimization design

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Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Wang, Mengfan Zhang, Lixin Fu, Changxin Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China Xinjiang Prod & Construct Corps Key Lab Modern Agr Shihezi 832003 Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Agr Equipment Shihezi 832003 Peoples R China Shihezi Univ Bingtuan Energy Dev Inst Shihezi 832003 Peoples R China

Predicting and optimizing the mechanical performance of the helically wound nylon-reinforced rubber fertilizer hose (HWNR hose) is crucial for enhancing the performance of hose pumps. This study aims to enhance the service life of HWNR hoses and the efficiency of liquid fertilizer transport. First, a finite element simulation model and a mathematical model were established to analyze the influence of fiber layer arrangement on the maximum shear strain on the coaxial surface (MSS) and the reaction force on the extrusion roller (RF). For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). Results showed CPO-GRNN's superiority in handling complex nonlinear problems. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for optimization design. Compared to the original HWNR hose with an MSS of 0.906 and an RF of 30,376N, the optimized design reduced the MSS by 7.99% and increased the RF by 2.46%, significantly enhancing their service life and liquid fertilizer transport capacity. However, further research on fatigue damage is needed.

关键词： Prediction of mechanical properties multi-objective optimization design CPO-GRNN Fiber-reinforced composite material fertilizer hoses

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CFD-based multi-objective optimization of a waterjet-propelled trimaran

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OCEAN ENGINEERING 2020年第0期195卷 106755-000页

作者： Guo, Jun Zhang, Yan Chen, Zuogang Feng, Yukun Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Marine Design & Res Inst China Sci & Technol Water Jet Prop Lab Shanghai 200011 Peoples R China

In the present paper, the multi-objective optimization of a waterjet-propelled trimaran was carried out based on computational fluid dynamics (CFD) considering the interactions between the waterjet and the hull. Numerical results were validated with experimental data for one waterjet propulsor and the original hull. The simulation-based design (SBD) optimization was performed for the waterjet and the hull. Furthermore, the parametric reconstruction of the duct was executed based on its shape characteristics. The Lackenby method and the free-form deformation method were applied to transform the hull shape. The optimal Latin hypercube design (Opt LHD) method was used to generate samples, and the surrogate model was developed based on the support vector regression (SVR) method. The non-dominated sorting genetic algorithm II (NSGA-II) was employed to optimize design objectives at the design speed of Fr = 0.628. In comparison to the original shape, the total resistance, the thrust, the torque, and the power consumption of the optimized trimaran at self-propulsion, respectively, decreased by 3.85%, increased by 6.38%, increased by 1.73%, and decreased by 10.39%. Therefore, it can be inferred that the SBD technique is effective to solve multi-objective optimization problems of ship hydrodynamics.

关键词： multi-objective optimization design Trimaran Waterjet propulsion Computational fluid dynamics SBD

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Optimal design and verification of interior permanent magnet synchronous generator based onFEAand Taguchi method

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第11期30卷 1-15页

作者： Karimpour, Seyyed Reza Besmi, Mohammad Reza Mirimani, Seyyed Mehdi Shahed Univ Dept Elect & Elect Engn Tehran Iran Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

Background Permanent magnet machines can be used for many applications, due to their high power density, high efficiency, observable torque-to-current ratio, and low maintenance cost. As such, the generator design in wind turbine systems is highly dependent on improvement of efficiency and generator performance. Thus, in order to improve the quality of manufactured electrical machines, a robust and precise optimization model should be developed to substitute the traditional deterministic models. Purpose This paper investigated the optimization design process of an interior permanent magnet synchronous generator (IPMSG) for wind power systems using the finite element analysis (FEA) based on a prototype IPMSG. To that end, a multi-objective optimization design of a permanent magnet generator (PMG) based on Taguchi method was proposed. design/Method The influence of the pole arc angle, magnet inset, magnet thickness, magnet width, and stator tooth width as well as slot depth were considered as the design parameters. The main characteristics of generator efficiency, total harmonic distortion (THD) and the amplitude of induction electromotive force (EMF) were taken as the optimization objectives, as well. The orthogonal matrix was established based on the number of the selected variables and the level factor of each variable, and finite element method (FEM) was, in turn, applied to solve the experimental matrix. As a result, an improved generator with higher maximum efficiency and EMF and lower THD was designed. Conclusions Finally, the prototype IPMSG which was manufactured based on the analysis results and Taguchi method was tested. The experimental tests were then performed to confirm the validity of the proposed design process and the generator efficiency. The prototype experiments and the finite element simulation results verified the efficacy of the optimization method.

关键词： finite element method interior permanent magnet machine multi-objective optimization design orthogonal matrix Taguchi method

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A thermal-structure coupled optimization study of lithium-ion battery modules with mist cooling

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第15期44卷 12295-12311页

作者： Qian, Liqin Yangtze Univ Sch Mechatron Engn Jingzhou Hubei Peoples R China

Lithium-ion battery packs' discharging results in significant heat generation, which leads to safety issues and negative impact during the application of electric vehicles. Previous studies focused more on the configurations and design of cell packs/modules with a cooling system to control the battery cell's temperature. However, temperature differences are more difficult to control to ensure thermal uniformity. Maximum pressure is a significant parameter that affects energy consumption and the cost of the cooling system. This study proposed a comprehensive approach to design an efficient mist cooling system, which considered both maximum temperature and temperature standard deviation. This design method generates candidate design points by Latin Hypercubes Sampling and design of experiments (DOEs);effects of some significant parameters of the cooling structure on the cooling efficiency are evaluated by sensitivity analysis. Surrogate models are then selected and optimized by the multi-objective optimization approach to acquire an optimal scheme of the mist cooling structure. The optimized results showed that the optimized mist cooling battery module has better thermal performance with a lower cost. This approach can be used in the battery pack design process to control the temperature rise and temperature distribution, and energy consumption of the cooling system.

关键词： energy consumption Li-ion battery module mist cooling multi-objective optimization design temperature SD

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Optimized design of Micro-Joint Motors Using MPSO With Embedded ELM Indirect Surrogate

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第2期39卷 1314-1324页

作者： Li, Bin Zhang, Zhongyi Gao, Peng Yuan, Ye Li, Guidan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This paper develops a modified particle swarm optimization algorithm with an embedded extreme learning machine (ELM-MPSO), improving accuracy and efficiency for global optimization of a micro-jointed motor with multiple modes. Different from conventional optimization, ELM acts as an indirect surrogate model (indirect-SM) to drive the MPSO algorithm online. To enhance the generalization ability of ELM, a fitness grading strategy is formulated. By just training optimized samples, indirect-SM can be built into the algorithm to form an online interaction mechanism. With this mechanism, optimized particles are pre-filtered and pre-supplemented online according to grade, thereby enhancing their quality. Sobol sampling and double updating techniques are introduced to ensure particle global and local characteristics. In addition, fitness is evaluated according to its complexity during optimization, which reduces function calls. optimization evaluation results show that the ELM-MPSO algorithm improves convergence and diversity for global optimization. An optimal candidate design is obtained by applying the proposed algorithm to representative points (RPs) optimization of a micro-joint motor. Finally, the prototype experiments demonstrate that the entire optimization strategy is effective.

关键词： Extreme learning machine (ELM) indirect surrogate model (indirect-SM) multi-objective optimization design particle swarm optimization (PSO)

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multi-objective optimization of pulsatile ventricular assist device hemocompatibility based on neural networks and a genetic algorithm

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INTERNATIONAL JOURNAL OF ARTIFICIAL ORGANS 2015年第6期38卷 325-336页

作者： Xu, Zihao Yang, Ming Wang, Xianghui Wang, Zhong Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai 200240 Peoples R China E China Normal Univ Dept Phys Shanghai 200062 Peoples R China

Purpose: Given the benefit of pulsatile blood flow for perfusion of coronary arteries and end organs, pulsatile ventricular assist devices (VADs) are still widely used as paracorporeal mechanical circulatory support devices in clinical applications. However, poor hemocompatibility limits the service period of the VADs. Most previous improvements on VAD hemocompatibility were conducted by trial-and-error CFD analysis, which does not easily arrive at the best solution. Methods: In this paper, a multi-objective optimization method integrating neural networks and NSGA-II (Non-dominated Sorted Genetic Algorithm-II) based on FSI simulation was developed and applied to a pulsatile VAD to optimize its hemocompatibility. First, the VAD blood chamber was parameterized with the principal geometrical parameters. Three hemocompatibility indices including hemolysis, platelet activation, and platelet deposition were chosen as goal functions. The neural networks were built to fit the nonlinear relationship between goal functions and geometrical parameters. Next, a multi-objective optimization algorithm (NSGA-II) was used to search out the Pareto optimal solutions in the built neural networks. Finally, the best compromise solution was selected from the Pareto optimal solutions by a fuzzy membership approach and validated by FSI simulation. Results: The best compromise solution simultaneously possesses an acceptable hemolysis index, platelet activation index, and platelet deposition index, and the corresponding relative errors between the indices predicted by optimization algorithm and the one calculated by FSI simulations are all less than 5%. Conclusions: The results suggest that the proposed multi-objective optimization method has the potential for application in optimizing pulsatile VAD hemocompatibility, and may also be applied to other blood-wetted devices.

关键词： Pulsatile VAD FSI simulation Thrombosis Hemolysis multi-objective optimization design

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