计算智能在微尺度力学研究中的应用
THE APPLICATION OF COMPUTING INTELLIGENCE ON THE RESEARCH OF MICRO-SCALE MECHANICS
查看参考文献14篇
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文摘
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微尺度力学的研究常遇到力学模型分析、复杂本构方程求解和跨尺度计算等问题,而传统的数学方法很难给出所研究的微尺度力学问题的解析解.该文探讨运用计算智能方法(主要包括人工神经网络、遗传算法和模糊数学等知识)对微尺度材料的力学行为进行研究.首先,采用人工神经网络建立微尺度材料的纳米压痕硬度随压痕深度变化的力学模型,并用其预测氧化镁材料的纳米压痕硬度;其次,运用遗传算法对A533-B号钢的球形压痕的载荷-位移曲线进行反分析,进而获取其力学参数. |
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其他语种文摘
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Based on the traditional mathematical methods, the analysis solutions for micro-scale mechanics problems are not usually solved properly for the building of a mechanical model, the solving of comprehensive constitutive equations and multi-scale computing need to be dealt with. In the present paper, computing intelligence, which includes artificial neural network, genetic algorithm, fuzzy mathematics and others, is used to study the mechanical behaviors of micro-scale materials. First, the mechanical model built with artificial neural network, which describes the variety of hardness to the indentation depth, is used to predict the nanoindentation hardness of MgO. Second, the load-depth curves of A533-B steel of indentation with spherical indenter are inversely analyzed by using genetic algorithm. |
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来源
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工程力学
,2009,26(6):10-15 【核心库】
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关键词
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尺度效应
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微尺度力学
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计算智能
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纳米压痕
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反问题
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地址
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1.
天津大学建筑工程学院, 天津, 300072
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中国科学院力学研究所, 北京, 100080
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-4750 |
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学科
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力学 |
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文献收藏号
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CSCD:3577088
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参考文献 共
14
共1页
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1.
Fleck N A. A phenomenological theory for strain gradient effects in plasticity.
Journal of the Mechanics and Physics of Solids,1993,41(12):1825-1873
|
CSCD被引
86
次
|
|
|
|
|
2.
Nix N D. Indentation size effects in crystalline materials:a law for strain gradient plasticity.
Journal of the Mechanics and Physics of Solids,1998,46:411-425
|
CSCD被引
1
次
|
|
|
|
|
3.
Gao H. Mechanism-based strain grain gradient plasticity-Ⅰ.Theory.
Journal of the Mechanics and Physics of Solids,1999,47:1239-1263
|
CSCD被引
155
次
|
|
|
|
|
4.
Gcrbcrich W W. Interpretations of Indentation size effects.
Journal of Applied Mechanics,2002,69:433-442
|
CSCD被引
1
次
|
|
|
|
|
5.
徐宗本.
计算智能:模拟进化计算,2005
|
CSCD被引
1
次
|
|
|
|
|
6.
Muliana A. Artificial neural network and finite element modeling of nanoindentataion tests.
Metallurgical and Materials Transactions A-Physical Metallurgy and Materials Science,2002,33(7):1939-1947
|
CSCD被引
2
次
|
|
|
|
|
7.
Huber N. Determination of constitutive properties from spherical indentation data using neural network.Part Ⅰ:the case of pure kinematic hardening in plasticity laws.
Journl of the Mechanics and Physics of Solids,1999,47:1569-1588
|
CSCD被引
6
次
|
|
|
|
|
8.
Huber N. On the analysis of the stress-strain behaviour of thin metal films on substratcs using nanoindentation.
Philosophical Magazine,2006,86(33/35):5505-5519
|
CSCD被引
2
次
|
|
|
|
|
9.
舒思齐. 神经网络专家系统在微结构材料力学中的应用.
工程力学,2003,3(增刊):604-607
|
CSCD被引
1
次
|
|
|
|
|
10.
丁永生.
计算智能-理论、技术及应用,2004
|
CSCD被引
1
次
|
|
|
|
|
11.
阎平凡.
人工神经网络与模拟进化计算,2000
|
CSCD被引
365
次
|
|
|
|
|
12.
Gang Feng. Indentation size effect in MgO.
Scripta Materialia,2004,51:599-603
|
CSCD被引
1
次
|
|
|
|
|
13.
周明.
遗传算法原理及应用,1999
|
CSCD被引
1038
次
|
|
|
|
|
14.
Taljat B. Kosel E New analytical procedure to determine stress-strain curve from spherical indentation data international.
Journal of Solid and Structures,1998,35(33):4411-4426
|
CSCD被引
9
次
|
|
|
|
|
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