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Статті в журналах з теми "Multi-Material optimization"

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Автор: Grafiati

Опубліковано: 6 липня 2024

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1

Singh, Jaswinder. "Multi-Response Optimization of Manual Material Handling Tasks through Utility Concept." Bonfring International Journal of Industrial Engineering and Management Science 4, no. 2 (May 30, 2014): 83–89. http://dx.doi.org/10.9756/bijiems.6034.

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2

Hvejsel, Christian Frier, Erik Lund, and Mathias Stolpe. "Optimization strategies for discrete multi-material stiffness optimization." Structural and Multidisciplinary Optimization 44, no. 2 (May 7, 2011): 149–63. http://dx.doi.org/10.1007/s00158-011-0648-5.

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3

Chandrasekhar, Aaditya, and Krishnan Suresh. "Multi-Material Topology Optimization Using Neural Networks." Computer-Aided Design 136 (July 2021): 103017. http://dx.doi.org/10.1016/j.cad.2021.103017.

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4

Ramani, Anand. "Multi-material topology optimization with strength constraints." Structural and Multidisciplinary Optimization 43, no. 5 (November 20, 2010): 597–615. http://dx.doi.org/10.1007/s00158-010-0581-z.

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5

MINAMI, Hayato, Akihiro TAKEZAWA, Masanori HONDA, and Mitsuru KITAMURA. "Layout Optimization of Multi-material Beam Elements." Proceedings of Design & Systems Conference 2017.27 (2017): 2107. http://dx.doi.org/10.1299/jsmedsd.2017.27.2107.

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6

SHINTANI, Kohei, Hideyuki AZEGAMI, and Takayuki YAMADA. "Multi-material robust topology optimization considering uncertainty of material properties." Transactions of the JSME (in Japanese) 87, no. 900 (2021): 21–00138. http://dx.doi.org/10.1299/transjsme.21-00138.

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7

Liu, Pai, Litao Shi, and Zhan Kang. "Multi-material structural topology optimization considering material interfacial stress constraints." Computer Methods in Applied Mechanics and Engineering 363 (May 2020): 112887. http://dx.doi.org/10.1016/j.cma.2020.112887.

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8

Hvejsel, Christian Frier, and Erik Lund. "Material interpolation schemes for unified topology and multi-material optimization." Structural and Multidisciplinary Optimization 43, no. 6 (January 27, 2011): 811–25. http://dx.doi.org/10.1007/s00158-011-0625-z.

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9

Zheng, Yongfeng, Zihao Chen, Baoshou Liu, Ping Li, Jiale Huang, Zhipeng Chen, and Jianhua Xiang. "Robust topology optimization for multi-material structures considering material uncertainties." Thin-Walled Structures 201 (August 2024): 111990. http://dx.doi.org/10.1016/j.tws.2024.111990.

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10

Park, Jaejong, and Alok Sutradhar. "A multi-resolution method for 3D multi-material topology optimization." Computer Methods in Applied Mechanics and Engineering 285 (March 2015): 571–86. http://dx.doi.org/10.1016/j.cma.2014.10.011.

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11

Li, Chao, and Il Yong Kim. "Multi-material topology optimization for automotive design problems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 14 (November 24, 2017): 1950–69. http://dx.doi.org/10.1177/0954407017737901.

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Анотація:

The algorithms for multi-material topology optimization were developed to solve compliance-minimization problems and applied to engineering problems in automotive concepts and lightweight design. Two small-scale problems of a long cantilever and a control arm were studied initially to verify the effectiveness of the developed algorithms and in-house program. Optimal solutions achieved by the multi-material topology optimization method developed were compared to their counterparts obtained by standard single-material topology optimization. To efficiently solve real-world engineering problems, the algorithms were further advanced to incorporate extrusion constraints and to handle multiple load cases. The effectiveness and the efficiency of the proposed method were demonstrated by the study of two real-world engineering problems: (a) the conceptual design of a cross-member for a chassis frame; and (b) the conceptual design of an automotive engine cradle. The two optimization design problems both involved complex geometries, design and non-design domains, prescribed regions with specific material allocations, multiple load cases, and manufacturing extrusion constraints. It was explicitly demonstrated that, for the same weight, the optimum designs achieved by the multi-material topology optimization method were stiffer than those achieved by standard single-material topology optimization.

12

Jung, Youngsuk, and Seungjae Min. "Material Interpolation in Multi-Material Topology Optimization for Magnetic Device Design." IEEE Transactions on Magnetics 55, no. 11 (November 2019): 1–4. http://dx.doi.org/10.1109/tmag.2019.2929079.

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13

Zhang, Hongyi, and Qian Wan. "Monte carlo optimization of multi-layer semiconductor material." IOP Conference Series: Earth and Environmental Science 675, no. 1 (February 1, 2021): 012207. http://dx.doi.org/10.1088/1755-1315/675/1/012207.

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14

MI, Dahai, and Masanori HASHIGUCHI. "Topology optimization of a multi-material thermal actuator." Proceedings of The Computational Mechanics Conference 2021.34 (2021): 258. http://dx.doi.org/10.1299/jsmecmd.2021.34.258.

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15

Clason, Christian, Florian Kruse, and Karl Kunisch. "Total variation regularization of multi-material topology optimization." ESAIM: Mathematical Modelling and Numerical Analysis 52, no. 1 (January 2018): 275–303. http://dx.doi.org/10.1051/m2an/2017061.

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This work is concerned with the determination of the diffusion coefficient from distributed data of the state. This problem is related to homogenization theory on the one hand and to regularization theory on the other hand. An approach is proposed which involves total variation regularization combined with a suitably chosen cost functional that promotes the diffusion coefficient assuming prespecified values at each point of the domain. The main difficulty lies in the delicate functional-analytic structure of the resulting nondifferentiable optimization problem with pointwise constraints for functions of bounded variation, which makes the derivation of useful pointwise optimality conditions challenging. To cope with this difficulty, a novel reparametrization technique is introduced. Numerical examples using a regularized semismooth Newton method illustrate the structure of the obtained diffusion coefficient.

16

Ashby, M. F. "Multi-objective optimization in material design and selection." Acta Materialia 48, no. 1 (January 2000): 359–69. http://dx.doi.org/10.1016/s1359-6454(99)00304-3.

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17

Wu, Chi, Jianguang Fang, and Qing Li. "Multi-material topology optimization for thermal buckling criteria." Computer Methods in Applied Mechanics and Engineering 346 (April 2019): 1136–55. http://dx.doi.org/10.1016/j.cma.2018.08.015.

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18

Zuo, Wenjie, and Kazuhiro Saitou. "Multi-material topology optimization using ordered SIMP interpolation." Structural and Multidisciplinary Optimization 55, no. 2 (June 17, 2016): 477–91. http://dx.doi.org/10.1007/s00158-016-1513-3.

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19

Li, Daozhong, and Il Yong Kim. "Multi-material topology optimization for practical lightweight design." Structural and Multidisciplinary Optimization 58, no. 3 (April 12, 2018): 1081–94. http://dx.doi.org/10.1007/s00158-018-1953-z.

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20

Queiroz Zuliani, João Batista, Miri Weiss Cohen, Frederico Gadelha Guimarães, and Carlos Alberto Severiano Junior. "A multi-objective approach for multi-material topology and shape optimization." Engineering Optimization 51, no. 6 (September 25, 2018): 915–40. http://dx.doi.org/10.1080/0305215x.2018.1514501.

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21

HU, Zhaohui. "Application of Multi-objective Optimization of Multi-material-multi-part Specification Composite Construction." Journal of Mechanical Engineering 46, no. 22 (2010): 111. http://dx.doi.org/10.3901/jme.2010.22.111.

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22

Xiong, Luchang, Zhaoyang Zhang, Zhijun Wan, Yuan Zhang, Ziqi Wang, and Jiakun Lv. "Optimization of Grouting Material Mixture Ratio Based on Multi-Objective Optimization and Multi-Attribute Decision-Making." Sustainability 14, no. 1 (December 31, 2021): 399. http://dx.doi.org/10.3390/su14010399.

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Анотація:

As a solid waste produced by coal combustion, fly ash will cause serious environmental pollution. However, it can be considered as a sustainable and renewable resource to replace partial cement in grouting materials. Fly ash grouting materials re-cement the broken rock mass and improve the mechanical properties of the original structure. It can reinforce the broken surrounding rock of mine roadway. The utilization of fly ash also reduces environmental pollution. Therefore, this paper establishes a new material mixture ratio optimization model to meet the requirement of material property through combining the methods of experimental design and numerical analysis. Based on the Box–Behnken design with 3 factors and 3 levels, a mathematical model is constructed to fit the nonlinear multiple regression functions between material properties and raw materials ratios. The influence of raw materials is analyzed on material properties (the material’s 7-day uniaxial compressive strength, initial setting time, and slurry viscosity). Then, 80 Pareto solutions are obtained through NASG-II algorithm which takes the regression functions as the objective functions for multi-objective optimization of the grouting material ratio. Finally, the best ratio solution of water-cement ratio—0.71, silica fume content—1.73%, and sodium silicate content—2.61% is obtained through the NNRP-TOPSIS method.

23

Tajs-Zielińska, Katarzyna, and Bogdan Bochenek. "Multi-Domain and Multi-Material Topology Optimization in Design and Strengthening of Innovative Sustainable Structures." Sustainability 13, no. 6 (March 19, 2021): 3435. http://dx.doi.org/10.3390/su13063435.

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Expectations and challenges of modern sustainable engineering and architecture stimulate intensive development of structural analysis and design techniques. Designing durable, light and eco-friendly constructions starts at the conceptual stage, where new efficient design and optimization tools need to be implemented. Innovative methods, like topology optimization, become more often a daily practice of engineers and architects in the process of solving more and more demanding up-to-date engineering problems efficiently. Topology optimization is a dynamically developing research area with numerous applications to many research and engineering fields, ranging from the mechanical industry, through civil engineering to architecture. The motivation behind the present study is to make an attempt to broaden the area of topology optimization applications by presenting an original approach regarding the implementation of the multi-domain and multi-material topology optimization to the design and the strengthening/retrofitting of structures. Moreover, the implementation of the design-dependent self-weight loading into the design model is taken into account as a significantly important issue, since it influences the final results of the topology optimization process, especially when considering massive engineering structures. As an optimization tool, the original efficient heuristic algorithm based on Cellular Automata concept is utilized.

24

Li, Guyang, Lin Li, Zhigang Peng, and Sanbao Hu. "Multi-objective Topology Optimization of the Temperature Field Considering the Material Nonlinearities." Journal of Physics: Conference Series 2441, no. 1 (March 1, 2023): 012018. http://dx.doi.org/10.1088/1742-6596/2441/1/012018.

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Abstract This paper presents a multi-objective topology optimization method for the temperature field considering the material nonlinearities. Although a large number of papers have studied the topology optimizations of the heat transfer problems, none of them considered the influence of the material nonlinearity properties. In this paper, the material nonlinearity is considered. Based on the nonlinear assumption, the mathematical model of the corresponding topology optimization is presented first. Then, the sensitivities of the objectives and the constraints respect to the design variables are derived. And the optimization routines are discussed. After establishing the theories and solving scheme, a software is developed by MATLAB for the topology design of the nonlinear heat transfer. Several nonlinear numerical examples are presented. And the corresponding optimal results generated under the linear assuming are also collected for comparisons. Furthermore, this paper also simply discusses the preventing approaches of instabilities and the implementation methods of some other unusual engineering constraints. These true engineering considerations are very helpful for the real industry design experiences.

25

Zhan, Jinqing, Yu Sun, Min Liu, Benliang Zhu, and Xianmin Zhang. "Multi-material topology optimization of large-displacement compliant mechanisms considering material-dependent boundary condition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 6 (October 14, 2021): 2847–60. http://dx.doi.org/10.1177/09544062211036157.

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Multi-material compliant mechanisms design enables potential design possibilities by exploiting the advantages of different materials. To satisfy mechanical/thermal impedance matching requirements, a method for multi-material topology optimization of large-displacement compliant mechanisms considering material-dependent boundary condition is presented in this study. In the optimization model, the element stacking method is employed to describe the material distribution and handle material-dependent boundary condition. The maximization of the output displacement of the compliant mechanism is developed as the objective function and the structural volume of each material is the constraint. Fictitious domain approach is applied to circumvent the numerical instabilities in topology optimization problem with geometrical nonlinearities. The method of moving asymptotes is applied to solve the optimization problem. Several numerical examples are presented to demonstrate the validity of the proposed method. The optimal topologies of the compliant mechanisms obtained by the proposed method can satisfy the specified material-dependent boundary condition.

26

Zhan, Jinqing, Yu Sun, Min Liu, Benliang Zhu, and Xianmin Zhang. "Multi-material topology optimization of large-displacement compliant mechanisms considering material-dependent boundary condition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 6 (October 14, 2021): 2847–60. http://dx.doi.org/10.1177/09544062211036157.

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Анотація:

Multi-material compliant mechanisms design enables potential design possibilities by exploiting the advantages of different materials. To satisfy mechanical/thermal impedance matching requirements, a method for multi-material topology optimization of large-displacement compliant mechanisms considering material-dependent boundary condition is presented in this study. In the optimization model, the element stacking method is employed to describe the material distribution and handle material-dependent boundary condition. The maximization of the output displacement of the compliant mechanism is developed as the objective function and the structural volume of each material is the constraint. Fictitious domain approach is applied to circumvent the numerical instabilities in topology optimization problem with geometrical nonlinearities. The method of moving asymptotes is applied to solve the optimization problem. Several numerical examples are presented to demonstrate the validity of the proposed method. The optimal topologies of the compliant mechanisms obtained by the proposed method can satisfy the specified material-dependent boundary condition.

27

Liu, Baoshou, Xiaolei Yan, Yangfan Li, Shiwei Zhou, and Xiaodong Huang. "Multi-Material Topology Optimization of Structures Using an Ordered Ersatz Material Model." Computer Modeling in Engineering & Sciences 128, no. 2 (2021): 523–40. http://dx.doi.org/10.32604/cmes.2021.017211.

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28

KISHIMOTO, Naoki, Yuki SATO, Yuki NOGUCHI, Takayuki YAMADA, Kazuhiro IZUI, and Shinji NISHIWAKI. "Topology optimization of multi-material structures considering the sensitivity of material transition." Proceedings of Mechanical Engineering Congress, Japan 2016 (2016): J0110202. http://dx.doi.org/10.1299/jsmemecj.2016.j0110202.

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29

ISODA, Kazushi, Nari NAKAYAMA, Kozo FURUTA, Sunghoon LIM, Kazuhiro IZUI, and Shinji NISHIWAKI. "Level set-based multi-material topology optimization considering material and joint cost." Proceedings of Design & Systems Conference 2022.32 (2022): 2106. http://dx.doi.org/10.1299/jsmedsd.2022.32.2106.

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30

Cherrière, Théodore, Sami Hlioui, Luc Laurent, François Louf, Hamid Ben Ahmed, and Mohamed Gabsi. "Multi-material topology optimization of a flux switching machine." Science and Technology for Energy Transition 78 (2023): 41. http://dx.doi.org/10.2516/stet/2023037.

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This paper investigates the topology optimization of the rotor of a 3-phase flux-switching machine with 12 permanent magnets located within the stator. The objective is to find the steel distribution within the rotor that maximizes the average torque for a given stator, permanent magnets, and electrical currents. The optimization algorithm relies on a density method based on gradient descent. The adjoint variable method is used to compute the sensitivities efficiently. Since the rotor topology depends on the current feedings, this approach is tested on several electrical periods and returns alternative topologies. Then, the method is extended to the multi-material case and applied to optimize the non-magnet part of the stator. When dealing with 3 phases, the algorithm returns the reference topology as well as a theoretical machine with no return conductor according to the set current angle. To illustrate the creativity of the method, the optimization is finally performed with a single-phase and returns a new topology.

31

Zheng, Maosheng, and Jie Yu. "A Probability-based Fuzzy Multi-objective Optimization for Material Selection." Tehnički glasnik 18, no. 2 (May 15, 2024): 178–82. http://dx.doi.org/10.31803/tg-20230515054622.

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Abstract: In the present paper, a rational fuzzy multi-objective optimization for material selection is developed in respect of probabilistic method for multi-objective optimization, which agrees with the viewpoint of system theory for the whole optimization of a system and is the novelty of this work. The basic ideas and algorithms of fuzzy theory together with probability theory are taken as the cornerstone to perform the formulation. In the treatment, the intersection of the membership function of fuzzy numbers of alternative material performance and the membership function of fuzzy numbers of desired material performance is used as the utility of the material performance index. Thereafter, the utility of each material performance index is further used to conduct the assessment of its partial preferable probability and formulate the multi-objective optimization by means of probability theory. Moreover, a typical example is presented to provide the rational process of the probabilistic fuzzy multi-objective optimization for material selection.

32

Ge, Wenjie, and Xin Kou. "Topology Optimization of Multi-Materials Compliant Mechanisms." Applied Sciences 11, no. 9 (April 23, 2021): 3828. http://dx.doi.org/10.3390/app11093828.

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In this article, a design method of multi-material compliant mechanism is studied. Material distribution with different elastic modulus is used to meet the rigid and flexible requirements of compliant mechanism at the same time. The solid isotropic material with penalization (SIMP) model is used to parameterize the design domain. The expressions for the stiffness matrix and equivalent elastic modulus under multi-material conditions are proposed. The least square error (LSE) between the deformed and target displacement of the control points is defined as the objective function, and the topology optimization design model of multi-material compliant mechanism is established. The oversaturation problem in the volume constraint is solved by pre-setting the priority of each material, and the globally convergent method of moving asymptotes (GCMMA) is used to solve the problem. Widely studied numerical examples are conducted, which demonstrate the effectiveness of the proposed method.

33

Liang, Xuan, and Jianbin Du. "Concurrent multi-scale and multi-material topological optimization of vibro-acoustic structures." Computer Methods in Applied Mechanics and Engineering 349 (June 2019): 117–48. http://dx.doi.org/10.1016/j.cma.2019.02.010.

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34

Florea, Vlad, Manish Pamwar, Balbir Sangha, and Il Yong Kim. "3D multi-material and multi-joint topology optimization with tooling accessibility constraints." Structural and Multidisciplinary Optimization 60, no. 6 (July 17, 2019): 2531–58. http://dx.doi.org/10.1007/s00158-019-02344-1.

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35

Korta, Jakub, and Tadeusz Uhl. "MULTI-MATERIAL DESIGN OPTIMIZATION OF A BUS BODY STRUCTURE." Journal of KONES. Powertrain and Transport 20, no. 1 (January 25, 2013): 139–46. http://dx.doi.org/10.5604/12314005.1135327.

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36

MIN, Seungjae. "K12100 Multi-Material Structural Optimization Using Level Set Method." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _K12100–1_—_K12100–2_. http://dx.doi.org/10.1299/jsmemecj.2012._k12100-1_.

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37

Santhanakrishnan, Mani Sekaran, Tim Tilford, and Chris Bailey. "Multi-Material Heatsink Design Using Level-Set Topology Optimization." IEEE Transactions on Components, Packaging and Manufacturing Technology 9, no. 8 (August 2019): 1504–13. http://dx.doi.org/10.1109/tcpmt.2019.2929017.

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38

Clason, Christian, and Karl Kunisch. "A convex analysis approach to multi-material topology optimization." ESAIM: Mathematical Modelling and Numerical Analysis 50, no. 6 (November 2016): 1917–36. http://dx.doi.org/10.1051/m2an/2016012.

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39

Yang, Xingtong, and Ming Li. "Discrete multi-material topology optimization under total mass constraint." Computer-Aided Design 102 (September 2018): 182–92. http://dx.doi.org/10.1016/j.cad.2018.04.023.

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40

Chu, Sheng, Mi Xiao, Liang Gao, Hao Li, Jinhao Zhang, and Xiaoyu Zhang. "Topology optimization of multi-material structures with graded interfaces." Computer Methods in Applied Mechanics and Engineering 346 (April 2019): 1096–117. http://dx.doi.org/10.1016/j.cma.2018.09.040.

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41

Kibsgaard, S. "Shape optimization of a multi-material sandwich plate joint." Computing Systems in Engineering 2, no. 1 (January 1991): 57–65. http://dx.doi.org/10.1016/0956-0521(91)90039-8.

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42

Wallin, Mathias, Niklas Ivarsson, and Matti Ristinmaa. "Large strain phase-field-based multi-material topology optimization." International Journal for Numerical Methods in Engineering 104, no. 9 (June 10, 2015): 887–904. http://dx.doi.org/10.1002/nme.4962.

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43

Chu, Sheng, Liang Gao, Mi Xiao, Zhen Luo, and Hao Li. "Stress-based multi-material topology optimization of compliant mechanisms." International Journal for Numerical Methods in Engineering 113, no. 7 (October 9, 2017): 1021–44. http://dx.doi.org/10.1002/nme.5697.

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44

Zhou, Mian, Mi Xiao, Mingzhe Huang, and Liang Gao. "Multi-material isogeometric topology optimization in multiple NURBS patches." Advances in Engineering Software 186 (December 2023): 103547. http://dx.doi.org/10.1016/j.advengsoft.2023.103547.

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45

Zhang, Chengwan, Kai Long, Zhuo Chen, Xiaoyu Yang, Feiyu Lu, Jinhua Zhang, and Zunyi Duan. "Multi-Material Topology Optimization for Spatial-Varying Porous Structures." Computer Modeling in Engineering & Sciences 138, no. 1 (2024): 369–90. http://dx.doi.org/10.32604/cmes.2023.029876.

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46

NAKAYAMA, Nari, Hao LI, Kozo FURUTA, Shinji NISHIWAKI, and Kazuhiro IZUI. "Topology optimization to maximize eigenfrequencies of multi-material structures." Proceedings of Mechanical Engineering Congress, Japan 2022 (2022): J012–15. http://dx.doi.org/10.1299/jsmemecj.2022.j012-15.

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47

ISODA, Kazushi, Nari NAKAYAMA, Kozo FURUTA, Kazuhiro IZUI, and Shinji NISHIWAKI. "Multi-material topology optimization considering joint stiffness and cost." Proceedings of Design & Systems Conference 2023.33 (2023): 3402. http://dx.doi.org/10.1299/jsmedsd.2023.33.3402.

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48

Majdi, Behzad, and Arash Reza. "Multi-material topology optimization of compliant mechanisms via solid isotropic material with penalization approach and alternating active phase algorithm." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 234, no. 13 (February 27, 2020): 2631–42. http://dx.doi.org/10.1177/0954406220908627.

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The present study aims at providing a topology optimization of multi-material compliant mechanisms using solid isotropic material with penalization (SIMP) approach. In this respect, three multi-material gripper, invertor, and cruncher compliant mechanisms are considered that consist of three solid phases, including polyamide, polyethylene terephthalate, and polypropylene. The alternating active-phase algorithm is employed to find the distribution of the materials in the mechanism. In this case, the multiphase topology optimization problem is divided into a series of binary phase topology optimization sub-problems to be solved partially in a sequential manner. Finally, the maximum displacement of the multi-material compliant mechanisms was validated against the results obtained from the finite element simulations by the ANSYS Workbench software, and a close agreement between the results was observed. The results reveal the capability of the SIMP method to accurately conduct the topology optimization of multi-material compliant mechanisms.

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Xiao, Yangyang, Wei Hu, and Shu Li. "Multi-Material Optimization for Lattice Materials Based on Nash Equilibrium." Applied Sciences 14, no. 7 (March 30, 2024): 2934. http://dx.doi.org/10.3390/app14072934.

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Lattice materials are regarded as a new family of promising materials with high specific strength and low density. However, in the optimization of lattice materials, it is difficult in general to determine the material distribution in lattice structures due to the complex optimization formulations and overlaps between different materials. Thus, the article proposes to use the Nash equilibrium to address the multi-material optimization problem. Moreover, a suppression formula is investigated to tackle the issue of material overlapping. The proposed method is validated using a cantilever beam example, showing superior optimization results compared to single-material methods, with a maximum improvement of 20.5%. Moreover, the feasibility and stability of the approach are evaluated through L-shaped beam examples, demonstrating its capability to effectively allocate materials based on their properties and associated stress conditions within the design. Additionally, an MBB test demonstrates superior stiffness in the proposed optimized specimen compared to the unoptimized one.

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Zheng, Maosheng. "Application of probability-based multi-objective optimization in material engineering." Vojnotehnicki glasnik 70, no. 1 (2022): 1–12. http://dx.doi.org/10.5937/vojtehg70-35366.

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Introduction/purpose: Althought many methods have been proposed to deal with the problem of material selection, there are inherent defects of additive algorithms and subjective factors in such algorithms. Recently, a probability-based multi-objective optimization was developed to solve the inherent shortcomings of the previous methods, which introduces a novel concept of preferable probability to reflect the preference degree of the candidate in the optimization. In this paper, the new method is utilized to conduct an optimal scheme of the switching material of the RF-MEMS shunt capacitive switch, the sintering parameters of natural hydroxyapatite and the optimal design of the connecting claw jig. Methods: All performance utility indicators of candidate materials are divided into two groups, i.e., beneficial or unbeneficial types for the selection process; each performance utility indicator contributes quantitatively to a partial preferable probability and the product of all partial preferable probabilities makes the total preferable probability of a candidate, which transfers a multi-objective optimization problem into a single-objective optimization one and represents a uniquely decisive index in the competitive selection process. Results: Cu is the appropriate material in the material selection for RF - MEMS shunt capacitive switches; the optimal sintering parameters of natural hydroxyapatite are at 1100°C and 0 compaction pressure; and the optimal scheme is scheme No 1 for the optimal design of a connecting claw jig. Conclusion: The probability-based multi-objective optimization can be easily used to deal with an optimal problem objectively in material engineering.