数据软计算建模与优化及其在材料工程中的应用
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摘要
由材料成分和工艺预测性能，或由预定性能设计材料成分和工艺，实现材料的计算设计，是材料科学与工程中重要的研究课题之一，但目前尚难以从机理出发通过理论计算设计工程材料。一种可行的方法是，建立数据模型，进行经验设计。由于传统的统计建模难以反映材料中存在的复杂交互作用关系，而用软计算实现材料经验数据建模和优化更为有效，目前正成为新的学科交叉点和研究热点。因此，开展材料经验数据建模与优化研究，不但可以丰富和完善材料设计理论和方法，而且具有重要的实用价值。
鉴于此，论文旨在形成一个用于材料经验设计的数据软计算建模与优化框架。主要对以下内容开展研究：改进神经网络学习算法，简化模糊推理系统；改进遗传算法和粒群优化算法；开展软计算在工程材料中的应用研究。
在建模与优化理论方面，主要创新与突破表现在：提出了基于贝叶斯权重规范化的差异演化训练算法，改善了多层前馈型网络的学习精度和泛化能力；综合采用模糊聚类、误差回逆学习及基于相似分析的规则库约减，提高了高木—关野(Takagi-Sugeno)型模糊推理系统的可解释性；改进了遗传算法编码方式，并用模糊控制策略调整交叉和变异过程，形成了高效的十进制遗传算法和十进制近似遗传算法，提高了寻优速度和计算效率；用模糊控制和差异演化控制粒群优化参数，形成了高效快速的复合粒群算法，同时将捕食优化等新算法用于材料优化；实现了模糊决策的多目标材料参数优化，形成了基于软计算的材料经验设计理论框架。
在实际应用方面，取得的成果包括：建立了基于网形图的冲天炉熔炼工艺模型，在预定熔化率和炉温的模糊限制条件下，以提高热效率为目标，实现了工艺参数的模糊多目标优化；通过ZL101A合金成分－力学性能建模分析，对Si、Mg和Fe含量进行优化，在湿砂型T5状态下，使抗拉强度(b和伸长率(5稳定在250±10MPa和3.0±0.5%；通过A319铸造铝合金固溶处理工艺试验，建立了固溶工艺模型，得到了最佳固溶温度和时间参数，在金属型T6状态下，使抗拉强度稳定在340MPa左右且伸长率在5％以上；通过高碳当量灰铁成分－性能建模，进行成分设计，制备出具有良好工艺性能的高碳当量高强度灰铁；通过建立HT250成分、出铁温度、浇注温度与抗拉强度和硬度之间的关系，用多种遗传算法实现了预定强度和硬度下铁水成分与冲天炉熔炼工艺的综合优化。
通过综合采用多种软计算中技术，开展经验数据建模与优化研究，改进现有算法，形成了材料经验数据软计算建模与优化框架体系，并成功用于工业合金材料的成分和工艺设计。
研究形成的技术和方法具有一定的通用性和推广价值，可以应用到其它类似工艺过程的建模分析与优化。
One important problem in material science and engineering is the computational design of material, that’s to predict properties through theoretical calculations, or optimize compositions and process parameters of material with prescribed properties. However, for most industrial materials, it’s difficult to design compositions and processes from physical principle. So, a practical alternative is empirical design，that is to develop data-driven models directly from industrial data. For traditional statistical analyses could not deal with the complexity in material data, the soft computing has been used in material modeling and optimization recently， and its applications in material are becoming a new trend in multi-discipline researches. Therefore, the researches on data-driven soft computing modeling and optimization could provide new practical methods, and then enrich methodology in material design theory。
The goal of this work is to develop a systematic data-driven modeling and optimization framework for empirical design of material, so it focuses on solving the following problems. First, an attempt is made to improve the generability of neural network and the interpretability of fuzzy system. Then, combining empirical models with improved optimization algorithms, a systematic data-driven soft computing modeling and optimization framework is to be developed. And finally, all of the above would be validated in material processing practices based on industrial data.
After systematic researches on the data-driven modeling and optimization, thesis’s main theoretical contributions are as following. Firstly, using Bayesian complexity regularization for weight decay in the error back-propagation learning procedure, differential evolution method is used in determination of optimal multilayer perceptrons networks with better approximation and generalization. Secondly, an adaptive neuro-fuzzy system consisted of reduced interpretable Takagi-Sugeno type inference rules is also proposed to improve the predictability and generalization. In fuzzy inference system, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. Thirdly, instead of binary string encoding, more quickly genetic algorithms with improved genetic operators are developed, in which digits vary over the numbers 0, 1, 2, . . . 9 and fuzzy controllers are also used to adapt the crossover and mutation. A hybrid particle
swarm optimization (PSO) exhibiting higher success rates is developed. The hybrid PSO utilizes fuzzy control method and differential evolution algorithm to determine the appropriate set of parameters during the optimization. At the same time, bacterial foraging optimization (BFO) is also applied to the material processing. And finally, a systematic modeling and fuzzy multi-objects optimization framework is proposed, in which by the use of fuzzy multi-attributes material design method one could describe uncertain and imprecise information in materials.
In practices, integrating adaptive neruo-fuzzy inference models into optimization procedure, a number of industrial problems related to material processing have been solved. With operating rules generated directly from cupola melting experimental data, a set of neuro-fuzzy inference models has been developed to determine the optimal blast and carbon rate for the lowest energy consumption with fuzzy constrained tap temperature and melting rate. After modeling the correlation between mechanical properties and compositions of ZL101A cast aluminum alloy, with the optimized compositions, the ultimate tensile strength and elongation are controlled within the range of 250(10MPa and 3.0(0.5%。Based on solution treatment experiments of A319 cast aluminum alloy, neuro-fuzzy models have been obtained to describe the relationship between mechanical properties and solution parameters. Combined the fuzzy models with optimization pr
鉴于此，论文旨在形成一个用于材料经验设计的数据软计算建模与优化框架。主要对以下内容开展研究：改进神经网络学习算法，简化模糊推理系统；改进遗传算法和粒群优化算法；开展软计算在工程材料中的应用研究。
在建模与优化理论方面，主要创新与突破表现在：提出了基于贝叶斯权重规范化的差异演化训练算法，改善了多层前馈型网络的学习精度和泛化能力；综合采用模糊聚类、误差回逆学习及基于相似分析的规则库约减，提高了高木—关野(Takagi-Sugeno)型模糊推理系统的可解释性；改进了遗传算法编码方式，并用模糊控制策略调整交叉和变异过程，形成了高效的十进制遗传算法和十进制近似遗传算法，提高了寻优速度和计算效率；用模糊控制和差异演化控制粒群优化参数，形成了高效快速的复合粒群算法，同时将捕食优化等新算法用于材料优化；实现了模糊决策的多目标材料参数优化，形成了基于软计算的材料经验设计理论框架。
在实际应用方面，取得的成果包括：建立了基于网形图的冲天炉熔炼工艺模型，在预定熔化率和炉温的模糊限制条件下，以提高热效率为目标，实现了工艺参数的模糊多目标优化；通过ZL101A合金成分－力学性能建模分析，对Si、Mg和Fe含量进行优化，在湿砂型T5状态下，使抗拉强度(b和伸长率(5稳定在250±10MPa和3.0±0.5%；通过A319铸造铝合金固溶处理工艺试验，建立了固溶工艺模型，得到了最佳固溶温度和时间参数，在金属型T6状态下，使抗拉强度稳定在340MPa左右且伸长率在5％以上；通过高碳当量灰铁成分－性能建模，进行成分设计，制备出具有良好工艺性能的高碳当量高强度灰铁；通过建立HT250成分、出铁温度、浇注温度与抗拉强度和硬度之间的关系，用多种遗传算法实现了预定强度和硬度下铁水成分与冲天炉熔炼工艺的综合优化。
通过综合采用多种软计算中技术，开展经验数据建模与优化研究，改进现有算法，形成了材料经验数据软计算建模与优化框架体系，并成功用于工业合金材料的成分和工艺设计。
研究形成的技术和方法具有一定的通用性和推广价值，可以应用到其它类似工艺过程的建模分析与优化。
One important problem in material science and engineering is the computational design of material, that’s to predict properties through theoretical calculations, or optimize compositions and process parameters of material with prescribed properties. However, for most industrial materials, it’s difficult to design compositions and processes from physical principle. So, a practical alternative is empirical design，that is to develop data-driven models directly from industrial data. For traditional statistical analyses could not deal with the complexity in material data, the soft computing has been used in material modeling and optimization recently， and its applications in material are becoming a new trend in multi-discipline researches. Therefore, the researches on data-driven soft computing modeling and optimization could provide new practical methods, and then enrich methodology in material design theory。
The goal of this work is to develop a systematic data-driven modeling and optimization framework for empirical design of material, so it focuses on solving the following problems. First, an attempt is made to improve the generability of neural network and the interpretability of fuzzy system. Then, combining empirical models with improved optimization algorithms, a systematic data-driven soft computing modeling and optimization framework is to be developed. And finally, all of the above would be validated in material processing practices based on industrial data.
After systematic researches on the data-driven modeling and optimization, thesis’s main theoretical contributions are as following. Firstly, using Bayesian complexity regularization for weight decay in the error back-propagation learning procedure, differential evolution method is used in determination of optimal multilayer perceptrons networks with better approximation and generalization. Secondly, an adaptive neuro-fuzzy system consisted of reduced interpretable Takagi-Sugeno type inference rules is also proposed to improve the predictability and generalization. In fuzzy inference system, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification. Thirdly, instead of binary string encoding, more quickly genetic algorithms with improved genetic operators are developed, in which digits vary over the numbers 0, 1, 2, . . . 9 and fuzzy controllers are also used to adapt the crossover and mutation. A hybrid particle
swarm optimization (PSO) exhibiting higher success rates is developed. The hybrid PSO utilizes fuzzy control method and differential evolution algorithm to determine the appropriate set of parameters during the optimization. At the same time, bacterial foraging optimization (BFO) is also applied to the material processing. And finally, a systematic modeling and fuzzy multi-objects optimization framework is proposed, in which by the use of fuzzy multi-attributes material design method one could describe uncertain and imprecise information in materials.
In practices, integrating adaptive neruo-fuzzy inference models into optimization procedure, a number of industrial problems related to material processing have been solved. With operating rules generated directly from cupola melting experimental data, a set of neuro-fuzzy inference models has been developed to determine the optimal blast and carbon rate for the lowest energy consumption with fuzzy constrained tap temperature and melting rate. After modeling the correlation between mechanical properties and compositions of ZL101A cast aluminum alloy, with the optimized compositions, the ultimate tensile strength and elongation are controlled within the range of 250(10MPa and 3.0(0.5%。Based on solution treatment experiments of A319 cast aluminum alloy, neuro-fuzzy models have been obtained to describe the relationship between mechanical properties and solution parameters. Combined the fuzzy models with optimization pr
引文
T. Saito. Computational material design [M]. Berlin Heidelberg: Springer-Verlag, 1999:1
陈念贻. 数据信息采掘与材料设计的半经验方法[C]. 见：熊家炯. 材料设计[M]. 天津：天津大学出版社，2000：238~266
L. A. Zadeh. Fuzzy Logic, Neural networks, and soft computing [J]. Communications of the ACM, 1994, 37(3): 77~84
B. Bouchon-Meunier, Yager. R., and L. A. Zadeh. Fuzzy logic and soft computing [M]. Singapore: World Scientific, 1995:74~89
P. Bonissone. Soft computing: the convergence of emerging reasoning technologies, In: Soft Computing A Fusion of Foundations, Methodologies and Applications [M], Springer- Verlag, 1997, 1 (1): 6~18
P. Bonissone, Y. T. Chen, K. Goebel, P. Khedkar. Hybrid soft computing systems: industrial and commercial applications[C], Proceedings of the IEEE, 1999, 87(9): 1641~1667
J,-S. R. Jang, C. T. Sun, E. Mizutani著 张平安，高春华 译. 神经－模糊和软计算[M]. 西安：西安交通大学出版社，Prentice-Hall, 2000：1～6，17，38，241～245
刘洪霖, 陈念贻, 伍晓玲等．模式识别预报与转变温度135K超导体的制备[J]．科学通报，1991,36(13)：991~993
严六明, 陈念贻. 模式识别和人工神经网络预报复合硫族元素化合物结构类型[J]. 稀有金属, 1996, 20(2): 92~95
蔡煜东, 许伟杰, 陈念贻. 自组织人工神经网络用于金属间化合物三元填隙D88结构形成条件的判别[J]. 金属学报，1995，31B(6)：208~283
陈念贻, 张未名. 模式识别-人工神经网络方法在钢铁冶金中的应用[J]. 钢铁, 1994, 29(10): 68~71:56
刘洪霖, 陈念贻，甄久林等．热轧硅钢退火工艺的ODP-ANN智能优化[J]．计算机与应用化学，1996；13(1)：25~28
徐荣军，姚忠卯，程大振等．热轧硅钢牌号用PLS-ANN人智能优化[J]．钢铁，1997, 32(1)：36~39
李军善，王守安，刘洪霖等．退火工艺对热轧硅钢片板形影响因素的模式识别[J]．钢铁研究学报，1997，9(2)：66~68
乔俊飞, 郭戈, 柴天佑, 王伟. 神经网络在板形检测中的应用[J]. 中国有色金属学报, 1998, 8(3): 551~556
H. L. Liu，J. Cuo，N. Y. Chen et al. A PLS-BPN pattern recognition method applied to
computer-aided materials design [J]. Analytical Letters，1996, 29(2)：341~350
P. Villars, K. Brandenburg, M. Berndt, S. LeClair, A. Jackson, Y.-H. Pao , B. Igelnik ,M. Oxley , B. Bakshi , P. Chen , S. Iwata. Binary, ternary and quaternary compound former/nonformer prediction via Mendeleev number [J]. Journal of Alloys and Compounds, 2001(317–318): 26~38
S. L. Thaler. Predicting ultra-hard binary compounds via cascaded auto- and heteroassociative neural networks [J]. Journal of Alloys and Compounds, 1998 (279): 47~59
F. U. Margrave, K. Algas, D. A. Braldey. The use of neural networks in ultrasonic flaw detection [J]. Measurement. 1999, 25 (2 ): 143 ~ 154
王有明, 孙国雄. 灰铸铁性能预测的自组织模型法[J]. 现代铸铁，1998(1)：34~36
徐建林, 王智平, 路阳, 陈超. 球墨铸铁铸件缺陷诊断的神经网络模型[J]. 铸造，2000，49(8)：481~483
D. M. Tsai, C. F. Tseng. Surface roughness classification for castings [J]. Pattern Recognition 1999 (32): 389~405
C. C. Lee, F. Mansfeld. Automatic classification of polymer coating quality using artificial neural networks [J]. Corrosion Science, 1999 (41): 328~350
D. J. C. MacKay. Bayesian interpolation [J], Neural Computation, 1992, 4(3): 415~447
K. Ichikawa, H. K. D. H. Bhadeshia, D. J. C. MacKay. Model for solidification cracking in low alloy steel weld metals [J]. Science and Technology of Welding and Joining, 1996, 1(1): 43-49
M. N. Gibbs, Bayesian Gaussian processes for regression and classification [Dissertation], PhD Thesis, Cambridge University, 1997
张晓燕, 林维正, 苏勇 等. 利用模糊神经网络识别墙体材料的种类[J]. 建筑材料学报, 1998, 1(3): 234~238
张晓燕，林维亚. 利用模糊神经网络识别墙体材料的种类[J]. 施工技术，1999，28(10)：16~17
V. den. Broek, W. H. A. M. Wienke. Plastic material identification with spectroscopic near infrared imaging and artificial neural networks [J]. Analytica Chimica Acta, 1998, (361): l ~ 2
D. C. Reddy, C. S. Reddy. Development of Neural Network Methodology to Predict TTT Diagrams [J]. AFS Transactions, 1996, 104:191~196.
J. J. Wang, P. J. wan der Wolk. Effects of carbon concentration and cooling rate on continuous cooling transformations predicted by artificial neural network. ISIJ International, 1999, 39(10): 1038~1046.
S. Malinov, W. Sha, Z. Guo. Application of artificial neural network for prediction of
time–temperature–transformation diagrams in titanium alloys [J]. Materials Science and Engineering 2000, (A283): 1~10
W. G. Vermeulen P. F. Morris, A. P. de Weijer. Prediction of martnesite start temperature using artificial neural networks [J]. Ironmaking and Steel making, 1996,23 (5): 433-437.
戴起勋, 戴希敏. 神经网络在奥氏体钢设计中的应用[J]. 钢铁研究学报，1997，9(6)：37~40.
L. Gavard, H. K. D. H. Bhadeshia, D. J. C. MacKay. Bayesian neural network model for austenite formation in steels [J]. Mater. Sci.Tech., 1996,12:453~462.
夏伯才, 钱翰城, Awad. Samir H. 铸造铝硅合金液相线自适应神经－模糊建模[J]. 重庆大学学报，2004, 27 (已接受)
L. A. Dobrzanski, W. Sikek. Gomparison of hardenability calculation methods of the heattreatable constructional steels [J]. Mater. Proc.Tech., 1997,64:117~126.
L. A. Dobrzanski, W. Sitek. Application of a neural network in modelling of hardenability of constructional steels [J]. J. Mater. Proc.Tech, 1998,78: 59-66.
L. A. Dobrzanski, W. Sitek. The modelling of hardenability of using neural networks [J]. J. Mater. Proc. Tech., 1999,92-92:8~14.
W. G. Vermeulen, P. J. van der Wolk, A. P. de Weijer. Prediction of Jominy Hardness Profiles of Steels Using Artificial Neural Networks [J]. J. Mater. Eng. Perform., 1996, 5 (1): 57-63.
牛济泰, 孙雷剑, 李海涛. 基于人工神经网络的微合金钢热轧奥氏体晶粒尺寸模型的研究[J]. 材料科学与工艺，1999，47(1)：12~16
M. Q. Li, D. J. Chen, A. M. Xiong, L. Long. An adaptive prediction model of grain size for the forging of Ti–6Al–4V alloy based on fuzzy neural networks [J]. J. Mater. Proc. Tech., 2002, 123: 377~381
M. A. Yescas, H. K. D. H. Bhadeshia, D.J. MacKay. Estimation of the amount of retained austenite in austempered ductile irons using neural networks [J]. Materials Science and Engineering, 2001A, 311: 162~173
宋新华, 陈 茁, 俞汝勤. 人工神经网络用于对位取代苯酚定量构效关系的研究[J]. 中国科学(B辑)，1993，23(3)：245~251
郭进，刘洪霖，黄铁生等．富镧N－MH电池阴极材料性能的最小二乘法 ——人工神经网络研究[J]. 中国稀土学报，1996, 14(4)：369~371
陆文聪, 陈念贻. 人工神经网络法用于V-PTC材料诸性能的预报[J]. 应用科学学报, 1996, 14(3): 375~378
刘洪霖, 包 宏. 化工冶金过程人工智能优化[M]. 北京：冶金工业出版社，1999: 24~143
王国栋, 刘相华．金属轧制过程人工智能优化[M]．北京：冶金工业出版社，2000: 67~185
刘振宇, 王昭东, 王国栋, 张强. 应用神经网络预测C-Mn钢力学性能[J]. 钢铁研究学报，1995，7(4)：61~66
R.B. Yao, C.X. Tang, G.X. Sun, Predicting grey cast iron properties with artificial neural network [J], AFS Trans., 1996, 104: 635~642.
张兆春, 吴 铸. 模式识别－人工神经网络在改善汽车发动机缸体铸件力学性能方面法人应用[J]. 金属学报，1998，34(10)：1068~1072
吴铸, 张兆春, 李重河, 钦佩, 陈念贻. 模式识别在缸体铸件缩裂缺陷原因分析方面的应用[J]. 计算机与应用化学. 1998, 15(4): 233~236
J. Voracek. Prediction of mechanical properties of cast irons [J]. Applied Soft Computing, 2001, 1: 119~125
M. Perzyk, A. Kochanski. Prediction of ductile cast iron quality by artificial neural networks[C], in: Proceedings of the International Conference on Advances in Production Engineering'98, Warsaw University of Technology, Warsaw, Poland, June 1-3, 1998, pp. 74-83.
M. Perzyk, A. W. Kochanski. Prediction of ductile cast iron quality by artificial neural networks [J]. Journal of Materials Processing Technology, 2001, 109: 305~307
S. Calcaterra, G. Campana, L. Tomesani. Prediction of mechanical properties in spheroidal cast iron by neural networks [J]. Journal of Materials Processing Technology, 2000,104: 74~80
M. A. Yescas. Modeling the microstructure and mechanical properties of austempered ductile irons [Dissertation]. PhD Thesis, University of Cambridage, Nov. 2001
T. Cool, H. K. D. H. Bhadeshia, D. J. C. Mackay. The yield and ultimate tensile strength of steel welds [J]. Materials Science & Enfineering, 1997, A223: 186~200
J. Jones. Neural network modelling of the tensile properties of nickel base superalloys [Dissertation]. PhD thesis, University of Cambridge, UK, 1997
R. J. Grylls. Mechanical properties of a high-strength cupronickel alloy-bayesian neural network analysis [J], Materials Science and Engineering, 1997, A234-236: 267-270.
F. Tancret, H.K.D.H. Bhadeshia, D.J.C Mackey. Comparison of artificial neural networks with gaussian processes to model the yield strength of nickel-base superalloys [J]. ISIJ International, 1999, 39 (10): 1020-1026
C. Dumortier, P. Lehert, Y. Riquier. Statistical modelling of mechanical tensile properties of steels by using neural networks and multivariate analysis [J]. ISIJ International, 1999, 39 (10): 986-990
F. Brun, T. Yoshida, J. D. Robson, V. Narayan, H. K. D. H. Bhadeshia, and D. J. C. MacKay.
Theoretical design of ferritic creep resistant steels using neural network, kinetic, and thermodynamic models [J]. Materials Science and Technology, 1999, 15(5): 547- 554
V. Narayan, R. Abad, B. Lopez, H.K.D.H. Bhadeshia and D.J.C. MacKay. Estimation of hot torsion stress strain curves in iron alloys using a neural network analysis [J]. ISIJ International, 1999, 39 (10): 999-1005
S. H. Lalam, H. K. D. H. Bhadeshia and D. J. C. MacKay. Estimation of mechanical properties of steel welds part 1: yield and tensile strength [J], Science and Technology of Welding, 2000, 5 (3): 135~147.
S. H. Lalam, H. K. D. H. Bhadeshia and D. J. C. MacKay. Estimation of mechanical properties of steel welds part 2: Elongation and Charpy toughness [J], Science and Technology of Welding, 2000, 5 (3) : 149~160.
G. J. Hopkin. Modeling anisothermal recrystallization austenitic stainless steels [Dissertation], PhD Thesis, University of Cambridage, Nov. 2001
E. A. Metzbower, J. J. Deloach, S. H. Lalam and H. K. D. H. Bhadeshia. Neural network analysis of strength and ductility of welding alloys for high strength low alloy ship building steels [J], Science and Technology of Welding, 2001, 6 (2): 116~124.
E. A. Metzbower, J. J. DeLoach, S. H. Lalam, and H. K. D. H. Bhadeshia. Neural network analysis of strength and ductility of welding alloys for high strength low alloy shipbuilding steels [J]. Science and Technology of Welding and Joining, 2001, 6 (2): 116~124
M. Marimuthu. Design of welding alloys creep and toughness [Dissertation]. PhD thesis, University of Cambridge, UK, 2002
F. Tancret, H. K. D. H. Bhadeshia and D. J. C. MacKay. Design of a creep resistant nickel base superalloy for power plant applications Part 1 – Mechanical properties modeling [J]. Materials Science and Technology, 2003, 19(3): 283~290
J. Warde, D. M. Knowles. Application of neural networks to mechanical property determination of Ni-base superalloys [J]. ISIJ International, 1999, 39 (10): 1006-1014
J. Warde, D.M. Knowles. Use of neural networks for alloy design [J]. ISIJ International, 1999, 39 (10): 1015~1019
张兴全，彭颖红，阮雪榆. Ti17合金本构关系的人工神经网络模型[J]. 中国有色金属学报,1999, 9(3): 590~595
L. Kong, P. D. Hodgson. The application of constitutive and artificial neural network models to predict the hot strength of steels [J]. ISIJ International, 1999, 39 (10): 991~998
S. Pernot, C. H. Lamarque. Application of neural networks to the modelling of some constitutive laws [J]. Neural Networks, 1999, 12: 371~392
J. Liu, H. B. Chang, T. Y. Hsu (Xu Zuyao), X. Y. Ruan. Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network [J]. Journal of Materials Processing Technology, 2000, 103: 200~205
A. Muliana, R. Steward, R. M. Hai-Ali, and A. Saxena. Artificial neural network and finite element modeling of nanoindentation tests [J]. Metallurgical and Materials Transactions, 2002, 33A: 1939~1945
J. Z. Wang, H. G. Ni, J. Y. He. The application of automatic acquisition of knowledge to mix design of concrete [J]. Cement and Concrete Research. 1999, 29: 1875~1880
王继宗，倪宏光. 基于BP神经网络的水泥抗压强度预测研究[J]. 硅酸盐学报. 1999,27(4): 408~414
E. Douglas, G. Pouskouleli. prediction of compressive strength of mortars made with protland cement-blast furnace slag-fly ash blends [J]. Cem. Conc. Res., 1999, 21(4): 523~534.
袁群, 赵国藩. 用神经网络方法预测新老混凝土的粘结强度. 建筑材料学报, 2001, 4(2):132~137
H. K. D. H. Bhadeshia. Neural networks in materials science [J]. ISIJ International, 1999, 39 (10): 966-979
Special Issue on "Application of neural network analysis in materials science". ISIJ International [J], 1999, 39(10)
Z. Zhang, K. Friedrich. Artificial neural networks applied to polymer composites: a review [J]. Composites Science and Technology, 2003, 63: 2029~2044
P. F. Bartelt N. G. Blist J.S. Moberley. Application of artificial intelligence to power input control in the modern foundry [J]. AFS Trans. 1995, 103: 221~225
P. F. Bartelt, J. Moberly. Applying artificial intelligence to the modern foundry [J]. Mod. Cast., 1996, 86(2): 52~55.
E. D. Larsen D. E. Clark H. B. Smart et al. Intelligent control of cupola melting [J]. AFS Trans., 1995(103): 215~219
姚瑞波, 汤崇熙, 孙国雄等. 冲天炉熔炼的人工神经网络模型[J]. 铸造，1997(2)：14~17
孙瑜, 姚瑞波, 孙国雄等. 利用人工神经网络分析熔炼工艺参数与铁液成分的关系[J]. 现代铸铁. 1998(4): 39~41
K. D. V. Prasad, Y. Eric, W. C. Cheng. A neural network system for the prediction of process parameters in pressure die casting [J]. Journal of Materials Processing Technology, 1999, 89–90: 583~590
罗蓬, 周锦进. 基于神经网络的压铸工艺参数设计[J]. 铸造，2000，49(5)：286~288
黄石生，李迪，宋永伦. 焊接过程的神经网络建模及控制研究[J]. 机械工程学报，1994，
30(3): 24~30
L. H. Qi, J. J. Hou, P. L. Cui, H. J. Li. Research on prediction of the processing parameters of liquid extrusion by BP network [J]. Journal of Materials Processing Technology, 1999, 95: 232~237
王德翔, 刘来英, 王振宝等. 基于人工神经网络技术的注塑成型工艺参数优化[J]. 模具技术. 2001(6): 1~4
王文成，吕俊伟，徐大干．基于神经网络的气体渗碳碳势测定[J]．金属热处理，1998(1)：1l~13
T. Malinova, S. Malinov, N. Pantev. Simulation of microhardness profiles for nitrocarburized surface layers by artificial neural network [J]. Surface and Coatings Technology, 20011, 35: 258~267
K. Genel. Use of artificial neural network for prediction of ion nitrided case depth in Fe-Cr alloys [J]. Materials and Design, 2003, 24: 203~207
申炳昕. 基于神经网络的烧结矿化学成分预报模型的研究[Dissertation]. 硕士学位论文，中南大学，2002
范小慧, 王海东. 烧结过程数学模型与人工智能[M]. 长沙：中南工业大学出版社，2002:10~185
I. J. Cox, R.W. Lewis, R.S. Ransing, H. Laszczewski, G. Berni. Application of neural computing in basic oxygen steelmaking [J]. Journal of Materials Processing Technology, 2002, 120: 310~315
J. Larkiola, P. Myllykoski, A. S. Korhonen, L. Cser.The role of neural networks in the optimisation of rolling processes [J]. Journal of Materials Processing Technology, 1998, 80–81: 16~23
姜义祥. 模糊聚类分析在型砂控制中的应用[J]. 工程数学学报, 2000,17(2): 136~138
金永红, 朱训生. 模糊综合评判法在链轮材料选择中的应用[J]. 机械工程师, 1997(5):7~9
S. V. Wong, A. M. S. Hamouda, M. A. El Baradie. Generalized fuzzy model for metal cutting data selection [J]. Journal of Materials Processing Technology, 1999, 89–90: 310~317
R. Grimberg, A. Savin, L. Iancu, S. Chifan. Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors [J]. Sensors and Actuators, 2000, 81: 248~250
胡定煜, 罗爱芹, 范丛林, 傅若农. 模糊聚类法用于聚乙烯材料的分类鉴别[J]. 北京理工大学学报，2000，20(3)：394~396
Y. J. Ryoo, Y. C. Lim, K. H. Kim. Cassification of material using temperature response curve fitting and fuzzy neural network [J]. Sensors and Actuators A, 2001, 94: 11~18
王守琰, 孔德英, 宋诗哲. 基于模糊模式识别的金属材料海水腐蚀形貌诊断系统[J]. 金属学报，2001，37(5):517~521
俞树荣，王志文. 材料力学性能的模糊模式识别[J]. 甘肃科学学报，1999，11(3)：1~4
F. Kojimaa, N. Kubot, S. Hashimoto. Identification of crack profiles using genetic programming and fuzzy inference [J]. Journal of Materials Processing Technology, 2001, 108: 263~267
范志康, 王贻青, 王家析. 模糊回归在改善高碳当量灰铁组织中的应用[J]. 铸造，1991(12)：9~12
黄汉英, 熊先安, 魏明新. 模糊线性规划在优化饲料配方软件中的应用[J]. 农业工程学报，2000，16(3)：107~110
艾景军, 刘大有, 曹东军, 姜丽. 模糊线性规划饲料配方模型的建立与应用[J]. 电子学报，2001，29(11)：1~3
O. P. Femminella, M.J. Starink, M. Brown, I. Sinclair, C.J. Harris and P. A. S. Reed. Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu-alloys [J]. ISIJ International, 1999, 39 (10): 1027~1037
L. Arafeh, H. Singh, and S. K. Putatunda. A neuro-fuzzy logic approach to material processing [C]. IEEE Trans. Syst., Man, Cybern. C, 1999, 29 (8): 362~370
M. Y. Chen, D.A. Linkens. Hybrid fuzzy modelling using simulated annealing and application to materials property prediction. Intelligent Processing and Manufacturing of Materials [C]. 1999. IPMM '99. Proceedings of the Second International Conference on fuzzy system, Volume: 1, 10~15 July 1999 Pages: 395~400 vol.1
M. Y. Chen. Material property prediction using neural-fuzzy network intelligent control and automation [C]. 2000. Proceedings of the 3rd World Congress on fuzzy system, Volume: 2, 28 June-2 July 2000 Pages: 1092~1097 vol.2
M. Y. Chen, D. A. Linkens. A systematic neuro-fuzzy modeling framework with application to material property prediction [J]. IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, 2001, 31(5): 781~790
M. Y. Chen, D.A. Linkens. Rule-base self-generation and simplification for data-driven fuzzy models [J]. Fuzzy Sets and Systems, 2004, 142: 243~265
M. F. Abbod, D.A. Linkens, Q. Zhu, M. Mahfouf. Physically based and neuro-fuzzy hybrid modelling of thermomechanical processing of aluminium alloys [J]. Materials Science and Engineering, 2002, A333: 397~408
Q. Zhu, M. F. Abbod, J. Talamantes-Silva, C.M. Sellars, D.A. Linkens, J.H. Beynon. Hybrid modelling of aluminium–magnesium alloys during thermomechanical processing in terms of
physically-based, neuro-fuzzy and finite element models [J]. Acta Materialia, 2003, 51: 5051~5062
夏伯才，汪法根，李明进 等. 灰铸铁强度自适应模糊神经网络预测[J]. 铸造，2000，49(6)：349~352
H. C. Qian, B. C. Xia(夏伯才), S. Z. Li, F. G. Wang. Fuzzy neural network modeling of material properties [J]. J. Mater. Process. Technol. 2002, 122(2-3): 196~200
Z. J. Luo, Q. Yang, D. Liu. A novel method for predicting the grain size of superalloy forgings based on the fuzzy method and the FEM [J]. Journal of Materials Processing Technology, 2000, 99: 246~249
夏伯才，钱翰城，汪法根 等. 冲天炉网形图重构的自适应模糊推理方法[J]. 中国机械工程，2002，14(8)：646~648
K. A. Tawab, A. K. Noor. Uncertainty analysis of welding residual stress fields [J]. Comput. Methods Appl. Mech. Engrg., 1999, 179: 327~344
R. Kovacevic, Y. M. Zhang. Neurofuzzy model-based weld fusion state estimation [J]. IEEE Control Systems Magazine, 1997, 17(2): 30~42
Y. M. Zhang, R. Kovacevic. Neurofuzzy model-based predictive control of weld fusion zone geometry [J]. IEEE Transactions On Fuzzy Systems, 1998, 6(3): 389~401
李迪, 宋永伦, 井上胜境. 基于模糊神经网络的弧焊熔池几何参数调节[J]. 焊接学报. 1999(12):131-137
E. Li, J. Li, J. S. Yu. A genetic neural fuzzy system-based quality prediction model for injection process [J]. Computers and Chemical Engineering, 2002, 26: 1253~1263
Y. H. Lu, F. H. Yeh, C.L. Li, M. T. Wu, C. H. Liu. Study of ductile fracture and preform design of upsetting process using adaptive network fuzzy inference system [J]. Journal of Materials Processing Technology, 2003, 140: 576~582
J. C. Chiou, J. Y. Yang. A CVD epitaxial deposition in a vertical barrel reactor: process modeling using cluster-based fuzzy logic models [J]. IEEE Transactions On Semiconductor Manufacturing, 1998, 11(4): 645~653
刘平，程翼宇. 辨识药物定量构效关系的模糊神经网络方法研究[J]. 高等学校化学学报，2000，21(10)：1473~1478
胡明玉，唐明述. 模糊神经网络在高强混凝土强度预测与配合比设计中的应用[J]. 计算机与应用化学, 2001,18(5): 423~428
胡明玉，唐明述. 降维网络及其在混凝土配合比优化设计中的应用[J].计算机与应用化学, 2001: 18(3): 241~246
江厚满, 张若棋, 张寿齐. 用遗传算法确定材料物态方程参数[J]. 高压物理学报. 1998,
12(1): 47~53
X. T. Feng, C. X. Yang. Genetic evolution of nonlinear material constitutive models [J]. Computer Methods in Applied Mechanics and Engineering, 2001, 190(45): 5957~5973.
郑延斌, 曹益林, 宋予民. 遗传算法用于结晶过程动力学参数辩识[J]. 计算机与应用化学. 1999,16(2): 121~132
S. Garcia. Experimental design optimization and thermophysical parameter estimation of composite materials using genetic algorithms [Dissertation], Ph.D. Thesis, Virginia Polytechnic Institute and State University, Nantes, France, 1999
G. R. Liu, W. B. Ma and X. Han, An inverse procedure for determination of material constants of composite laminates using elastic waves [J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191(33): 3543~3554.
M. Keser, S. I. Stupp. Genetic algorithms in computational materials science and engineering: simulation and design of self-assembling materials [J]. Comput. Methods Appl. Mech. Eng., 2000, 186: 373~385
N. Chakraborti, P. S. De and R. Prasad, Genetic algorithms based structure calculations for hydrogenated silicon clusters [J], Materials Letters, 2002, 55(1-2): 20~26.
K. Mizaki, T. Inoueya Genetic algorithm simulation for deposited structure of atoms [J]. Surface Science, 2002, 501: 93~101
L. R. Marim, M. R. Lemes, A. Dal Pino Jr. Ground-state of silicon clusters by neural network assisted genetic algorithm [J]. Journal of Molecular Structure (Theochem), 2003, 663: 159~165
U. Rodemerck, D. Wolf, O.V. Buyevskaya, P. Claus, S. Senkan, M. Baerns. High-throughput synthesis and screening of catalytic materials: case study on the search for a low-temperature catalyst for the oxidation of low-concentration propane [J]. Chemical Engineering Journal, 2001, 82: 3~11
U. Rodemerck, M. Baerns, M. Holena, D. Wolf. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials [J]. Applied Surface Science, 2004, 223: 168~174
S. Nandi, Y. Badhea, J. Lonari, U. Sridevi, B.S. Rao, S. S. Tambe, B.D. Kulkarni. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst [J]. Chemical Engineering Journal, 2004, 97: 115~129
韩力群. 催化剂配方的神经网络建模与遗传算法优化[J]．化工学报，1999，50(4)：58~61
赵善荣, 蒋华良, 刘东祥, 陈凯先. 遗传算法与药物分子设计[J]. 化学进展, 1997, 9(4):
405~415
M. Walker, R.E. Smith. A technique for the multiobjective optimization of laminated composite structures using genetic algorithms and finite element analysis [J]. Composite Structures, 2003, 62: 123~128
A. J. Kulkarni, K. Krishnamurthy, S. P. Deshmukh, R.S. Mishra. Microstructural optimization of alloys using a genetic algorithm [J]. Materials Science and Engineering, 2004, A 372: 213~220
张培新, 张奇志, 吴黎明等. 用神经网络－遗传算法优化MgO-B2O3-SiO2渣系组成[J]. 金属学报，1995，31B(6)：284~288
R.G. Song, Q. Z. Zhang, M. K. Tseng, B. J. Zhang. The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminum alloys [J]. Mater. Sci. Eng., 1995, C11: 39~41
R. G. Song, Q. Z. Zhang, Heat treatment optimization for 7175 aluminum alloy by genetic algorithm [J], Materials Science and Engineering, 2001, C17 (1-2): 133~137.
R. G. Song, Q. Z. Zhang, Heat treatment technique optimization for 7175 aluminum alloy by an artificial neural network and a genetic algorithm [J], Journal of Materials Processing Technology, 2001, 117(1-2): 84~88.
宋仁国, 董敏, 张奇志．7175铝合金工艺优化的遗传算法[J]．材料科学与工程，1998，16(1)：28~31
阎加强, 张培新, 伍卫琼等. 神经网络－遗传算法优化反应烧结ZrO2-SiC材料制备工艺[J]. 无机材料学报. 1998, 13(6): 937~940
潘清跃, 宋仁国, 张奇志等. 基于人工神经网络－遗传算法的1Gr18Ni9Ti钢激光表面熔凝工艺优化[J]. 材料研究学报. 1998, 12(3): 251~256
张国英, 刘贵立, 曾梅光. 模拟退火算法在无钴高强韧钢设计中的应用[J]. 材料科学与工程，2000，18(3)：19~22
张国英，刘贵立，曾海光．材料机械性能预测及模拟退火算法优化[J]．沈阳工业大学学报，2000，22(l)：55~58
王强, 邵惠鹤, 张钟俊. 神经网络的遗传设计及其在甲醛生产建模及优化中的应用[J]. 上海交通大学学报，1996，30(4)：143~150
张春慨, 邵惠鹤. 基于神经网络模型的混沌优化及其应用[J]. 化工自动化及仪表，2000，27(2)：19~22
蔡煜东, 程兆年, 陈念贻等．遗传程序设计用于工业调优——炼钢转炉炉龄预测[J]．自动化学报，1997, 23(1)：50~54
何耀华, 华贲, 杨道红. 系统模拟优化中的神经网络和遗传算法[J]. 石油炼制与化
工,1998, 29(9):53~57
周兆凯, 周毓菁. 遗传算法-优化设计-神经网络的耦合技术[J]. 武汉汽车工业大学学报. 1999, 21(2): 52~56
金蓉, 曹柳林. 遗传算法优化工业对象的RBF神经网络模型[J]. 北京化工大学学报, 2000, 27(3): 67~70
C. L. Karr, D. A. Stanley, B. McWhorter. Optimization of hydrocyclone operation using a geno-fuzzy algorithm [J]. Comput. Methods Appl. Mech. Engrg., 2000, 186: 517~530
C. H. Lim, Y. S. Yoon. Joong-Hoon Kim. Genetic algorithm in mix proportioning of high-performance concrete [J]. Cement and Concrete Research, 2004, 34: 409~420
H. Su, C. F. Yang, J. C. Shen, Z. L Tian. A systemic self-modelling method and its application to material design and optimization [J]. Modelling Simul. Mater. Sci. Eng., 2001, 9: 97~109
N. Chakraborti, R. Kumar, D. Jain. A study of the continuous casting mold using a pareto-converging genetic algorithm [J]. Applied Mathematical Modelling, 2001, 25: 287~297
N. Chakraborti, P. Mishra, A. Banerjee. Optimization of aluminum oxynitride compaction process using a Gray-coded genetic algorithm [J]. Materials Letters, 2003, 58: 136~141
B. J. Reardon. Inversion of micromechanical powder consolidation and sintering models using Bayesian inference and genetic algorithms [J]. Modelling Simul. Mater. Sci. Eng., 1999, 7: 1061~1081.
B. J. Reardon, S. R. Bingert. Inversion of tantalum micromechanical powder consolidation and sintering models using bayesian inference and genetic algorithms [J]. Acta mater., 2000, 48: 647~658
Y. X. Su, C. H. Zheng and B. Y. Duan, An optimal layout methodology for cold forming rectangular parts using genetic algorithms [J], Robotics and Computer-Integrated Manufacturing, 2001, 17(5): 429~433
罗仁平, 姚华, 彭颖红, 张永清. 微观遗传算法在预锻模优化设计中的应用[J]. 模具，2000(1)：53~55
J. C. Lin. Optimum cooling system design of a free-form injection mold using an abductive network [J]. Journal of Materials Processing Technology, 2002, 120: 226~236
J. S. Chung, S. M. Hwang. Application of a genetic algorithm to process optimal design in non-isothermal metal forming [J]. Journal of Materials Processing Technology, 1998, 80–81: 136~143
C. F. Castro, C. A. C. António, L.C. Sousa. Optimisation of shape and process parameters in metal forging using genetic algorithms [J]. Journal of Materials Processing Technology, 2004,
146: 356~364
A. G. Cunha, Modeling and optimization of single screw extrusion [Dissertation], Ph. D. Thesis, University of Minho, Guimaras, Portugal, 2000
齐乐华, 候俊杰, 杨茂奎 等. 液态挤压工艺ANN/GA建模与优化研究[J]. 西北工业大学学报，2001，19(1)：114~117
Y. S. Tarng, J. L. Wu, S. S. Yeh and S. C. Juang. Intelligent modeling and optimization of the gas tungsten arc welding process [J]. Journal of Intelligent Manufacturing, 1999, 10: 73~79
Y. S. Tarng, H. L. Tsai, S. S. Yeh. Modeling, optimization and classification of weld quality in tungsten inert gas welding [J]. International Journal of Machine Tools & Manufacture, 1999, 39: 1427~1438
余圣甫，李志远，王福德 等. 基于人工神经网络的药芯焊丝焊接工艺研究[J]. 华中理工大学学报，2000，28(4)：1~3
C. C. Tai, J. C. Lin. The optimal position for the injection gate of a die-casting die[J]. Journal of Materials Processing Technology, 1999, 86: 87~100
夏伯才, 钱翰城. 高碳当量灰铸铁成分的遗传优化[J]. 铸造，2003，52(12)：1153~1156
夏伯才, 钱翰城, 董杰等. 冲天炉熔炼过程神经－模糊建模与遗传优化[J]. 重庆大学学报，2003, 26 (8): 86~88
左演声, 刘渤, 曾磊. 神经网络与遗传算法应用于离子镀膜性能预报与工艺优化[J]. 北京工业大学学报，2000，26(1)：76~79
H. J. Li, K. Y. Jiang, K. Z. Li. Optimizing parameters of CVI process for manufacturing carbon–carbon composites by genetic algorithms [J]. Materials Letters, 2003, 57: 2366~2370
T. S. Li, C. T. Su, T. L. Chiang. Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm [J]. Computers in Industry, 2003, 50 : 113~122
H. Zheng, L. X. Kong and S. Nahavandi, Automatic inspection of metallic surface defects using genetic algorithms [J], Journal of Materials Processing Technology, 2002, 125-126: 427~433.
郭立平, 成之绪, 韩甫田 等. 粉末衍射谱图分解的模拟退火法[J]. 物理学报, 2003, 52 (11):2842~2848
I. J. Graham, K. Case and R. L. Wood, Genetic algorithms in computer-aided design [J], Journal of Materials Processing Technology, 2001, 117(1-2): 216~221.
R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces [J]. J. Global Optimization, 1997,11: 341~359
T. Takagi, M. Sugeno. Fuzzy identification of systems and its application to modeling and
control [J]. IEEE Trans on Systems, Man, and Cybernetics, 1985, 15(1): 116~132
L. X. Wang. Adaptive fuzzy system and control——design and stability analysis[M]. New York: PTR Prentice Hall, 1994:208~211
B. Kosko, S. G. Kong. Adaptive fuzzy systems for backing up a tracking and trailer [J]. IEEE Trans. on NN. 1992, 3(2): 211~233
J.,-S. R. Jang. ANFIS: Adaptive-network-based fuzzy inference systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665~685
J. C. Bezdek. Cluster validity with fuzzy sets [J]. J. Cybern., 1974, 3(3): 58~72
J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms [J]. New York: Plenum, 1981: 236.
I. Gath, A. B. Geva. Unsupervised optimal fuzzy clustering [J]. IEEE Trans. Pattern Anal. Machine Intell., 1989, 11(7): 773~81
M. Sugeno, T. Yasukawa. A fuzzy-logic-based approach to qualitative modeling [J]. IEEE Trans. Fuzzy Syst., 1993, 1(2): 7~31
M. P. Windham. Cluster validity for the fuzzy c-means clustering algorithm [J]. IEEE Trans. Pattern Anal. Machine. Intell., 1982, PAMI-4(4): 357~363
X. L. Xie, G. A. Beni. Validity measure for funnt clustering [J]. IEEE Trans. Pattern Anal. Machine Intell., 1991, 13(8): 841~846
E. H. Mamdani. Application of fuzzy algorithms for control of a simple dynamic plant [J]. Proc. Inst. Elect. Eng., 1974, 121: 1585~1588
G. Drossel. Der einflusse von schmelzebehandlungen auf die dichttheit von gusskoerpern aus Al-Si-gusslegierungen [J]. Giessereitechnik, 1981, 27(1): 7-12
R. Vijayaraghayan, N. Pelle, J. Boilean, J. Zindel, R. Beals. A micromodel for Al-Si alloys [J]. Scripta Materialia, 1996, 35(7): 861-867
M. B. Djurdjievic, W. T. Kierkus, G. E Byczynski, J. H. Sokolowski. Calculation of liquidus temperature for aluminum 3XX series of alloys [J]. AFS Trans. 1998, 106:143-147]
M. B. Djurdjievic. Development of a mathematical model nnumerical determination of liquidus temperature of low and high alloy iron-based melts [Dissertation]. PhD Thesis, University of Belgrade, 1993
M. B. Djurdjievic, W. T. Kierkus, G. E Byczynski etc. Modeling of fraction solid for a319 aluminum alloy [J]. AFS Trans., 1999, 6: 173-179
G. J. Hopkin. Modeling anisothermal recrystallization austenitic stainless steels [Dissertation], PhD Thesis, University of Cambridge, 2001
F. H. Samuel, A. M. Samuel H. W. Doty. Factors controlling thr type and morphology of
Cu-containing phases in 319 Al alloy [J]. AFS. Trans., 1996, 104: 893~901
J. Gauthier, G. H. Samuel. Tensile properties and fracture behavior of solution-heat-treated 319.2 automotive alloy [J]. AFS. Trans., 1995, 103: 849~859
L. Backerud. Solidification characteristics of aluminium alloys[C]. Vol. 2, Foundry Alloys, AFS/SkanAluminium,1990 DesPlanes, IL, USA, 1990: 71~229
C. H. Cáceres. Material properties and quality index in Al-Si-Mg casting alloys [J]. AFS Trans., 1998, 106: 601~604
L. Nnantha Narayanan, F. H. Samuel, J.E.Gruzlesk. Dissolution of iron intermetallics in Al-Si alloys though nonequilibrium heat treatment [J]. Metallugical and materials transaction A, 1995, 26A(8): 2161~2174
F. H. Samuel, A. M. Samuel, H. Liu. Effect of mgnesium content on the ageing behaviour of water-chilled Al-Si-Cu-Mg-Fe-Mn (380) alloy castings [J]. Journal of Materials Science, 1995, 30: 2531~2540
Q. G. Wang, C. J. Davidson. Solidification and precipitation behaviour of Al-Si-Mg casting alloys [J]. Journal of Materials Science, 2001, 36:739~750
H. V. Guthy. Evolution of the eutectic microstructure in chemically modified and unmodified aluminum silicon alloys [Dissertation]. Degree of Master Thesis, Worcester Polytechnic Institute, April 2002
J. Kennedy, R. C. Eberhart. Particle swarm optimization [A]. Proceedings IEEE International Conference on Neural Networks[C], IEEE Service Center, Piscataway, NJ, 1995, IV: 1942~1948
K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control [J]. IEEE Control Systems Magazine, 2002(6): 52~67
Y. Liu, K. M. Passino. Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors [J]. Journal Of Optimization Theory And Applications, 2002, 115(3): 603~628
R. E. Bellman, L. A. Zadeh. Decision-making in fuzzy environment [J]. Mangement Science, 1970, 17(4): 141~164. 见：Timothy. J. Ross 著, 钱同惠，沈其聪，葛晓滨 译. 模糊逻辑及其工程应用. 北京：电子工业出版社，2001：437~441
钱翰城，陈万志，李蜀庆. 冲天炉网形图研究述评[J]. 现代铸铁. 1985(3): 23~26
H. Jungbluth, H. Korschan. Das schmelzen in kopolofen [J]. Techniche Mitteiun Krupp-Forschungsberichte, 1938(5): 79~100
S. C. Massari, R. W. Lindsay. Melting in cupola [J], AFS. Trans. 1941, 49: 94~116
C. C. Wright. Anthracite as cupola fuel [J]. American Foundryman, 1947,11: 40~47
W. Patterson, H. Siepmann, H. Pacyna. Die Stoffbilanz and warmbilanz des kalwindkupolofens [J]. Giesserei, 1961(10): 239~258
石富. 冲天炉网形图的数学模拟与诊断[J]. 中国铸机. 1987(6)：14~19
刘启阳. Al-Si合金变质机理及共晶硅形态控制的研究与发展[J]. 铸造，1988(6)：1~3
蔡惠民. ZL108合金中RE、Mg、Cu最佳配比的试验[J]. 特种铸造及有色合金，1991(2)：6~9
S. Shivkamar, S. Ricci. Jr., D. Apelian et al. An experimental study to optimize the heat treatment of A356 alloy [J]. AFS Tras. 1987, 97:791~809
S. Shivkamar, S. Ricci. Jr., D. Apelian. Influence of solution parameters and simplified supersaturation treatments on tensile properties of A356 alloy [J]. AFS Tarns., 1990, 98:913~921
M. M. Tutile, D. L. Mclellan. Silicon particle characteristics in Al-Si-Mg castings [J]. AFS Trans., 1982, 92: 13~23
L. F. Mondolfo 著, 王祝堂 译. 铝合金的组织与性能[M]. 北京: 冶金工业出版社, 1988: 218
C. H. Cáceres, Damage by particle cracking and the quality index of Al-Si-Mg casting alloys [J]. AFS Trans., 2000, 108: 709~712
C. H. Cáceres, J. Barresi. Selection of temper and Mg content to optimise the quality index of Al-7Si-Mg casting alloys [J]. Int. J. of Cast Metals Research, 2000, 12: 377~384
C. H. Cáceres, J. Sokolowski, P. Gallo. Effect of ageing and Mg content on the quality index of two model Al-Cu-Si-Mg alloys [J]. Materials Science and Engineering, 1999, A271: 53~61
陈念贻. 数据信息采掘与材料设计的半经验方法[C]. 见：熊家炯. 材料设计[M]. 天津：天津大学出版社，2000：238~266
L. A. Zadeh. Fuzzy Logic, Neural networks, and soft computing [J]. Communications of the ACM, 1994, 37(3): 77~84
B. Bouchon-Meunier, Yager. R., and L. A. Zadeh. Fuzzy logic and soft computing [M]. Singapore: World Scientific, 1995:74~89
P. Bonissone. Soft computing: the convergence of emerging reasoning technologies, In: Soft Computing A Fusion of Foundations, Methodologies and Applications [M], Springer- Verlag, 1997, 1 (1): 6~18
P. Bonissone, Y. T. Chen, K. Goebel, P. Khedkar. Hybrid soft computing systems: industrial and commercial applications[C], Proceedings of the IEEE, 1999, 87(9): 1641~1667
J,-S. R. Jang, C. T. Sun, E. Mizutani著 张平安，高春华 译. 神经－模糊和软计算[M]. 西安：西安交通大学出版社，Prentice-Hall, 2000：1～6，17，38，241～245
刘洪霖, 陈念贻, 伍晓玲等．模式识别预报与转变温度135K超导体的制备[J]．科学通报，1991,36(13)：991~993
严六明, 陈念贻. 模式识别和人工神经网络预报复合硫族元素化合物结构类型[J]. 稀有金属, 1996, 20(2): 92~95
蔡煜东, 许伟杰, 陈念贻. 自组织人工神经网络用于金属间化合物三元填隙D88结构形成条件的判别[J]. 金属学报，1995，31B(6)：208~283
陈念贻, 张未名. 模式识别-人工神经网络方法在钢铁冶金中的应用[J]. 钢铁, 1994, 29(10): 68~71:56
刘洪霖, 陈念贻，甄久林等．热轧硅钢退火工艺的ODP-ANN智能优化[J]．计算机与应用化学，1996；13(1)：25~28
徐荣军，姚忠卯，程大振等．热轧硅钢牌号用PLS-ANN人智能优化[J]．钢铁，1997, 32(1)：36~39
李军善，王守安，刘洪霖等．退火工艺对热轧硅钢片板形影响因素的模式识别[J]．钢铁研究学报，1997，9(2)：66~68
乔俊飞, 郭戈, 柴天佑, 王伟. 神经网络在板形检测中的应用[J]. 中国有色金属学报, 1998, 8(3): 551~556
H. L. Liu，J. Cuo，N. Y. Chen et al. A PLS-BPN pattern recognition method applied to
computer-aided materials design [J]. Analytical Letters，1996, 29(2)：341~350
P. Villars, K. Brandenburg, M. Berndt, S. LeClair, A. Jackson, Y.-H. Pao , B. Igelnik ,M. Oxley , B. Bakshi , P. Chen , S. Iwata. Binary, ternary and quaternary compound former/nonformer prediction via Mendeleev number [J]. Journal of Alloys and Compounds, 2001(317–318): 26~38
S. L. Thaler. Predicting ultra-hard binary compounds via cascaded auto- and heteroassociative neural networks [J]. Journal of Alloys and Compounds, 1998 (279): 47~59
F. U. Margrave, K. Algas, D. A. Braldey. The use of neural networks in ultrasonic flaw detection [J]. Measurement. 1999, 25 (2 ): 143 ~ 154
王有明, 孙国雄. 灰铸铁性能预测的自组织模型法[J]. 现代铸铁，1998(1)：34~36
徐建林, 王智平, 路阳, 陈超. 球墨铸铁铸件缺陷诊断的神经网络模型[J]. 铸造，2000，49(8)：481~483
D. M. Tsai, C. F. Tseng. Surface roughness classification for castings [J]. Pattern Recognition 1999 (32): 389~405
C. C. Lee, F. Mansfeld. Automatic classification of polymer coating quality using artificial neural networks [J]. Corrosion Science, 1999 (41): 328~350
D. J. C. MacKay. Bayesian interpolation [J], Neural Computation, 1992, 4(3): 415~447
K. Ichikawa, H. K. D. H. Bhadeshia, D. J. C. MacKay. Model for solidification cracking in low alloy steel weld metals [J]. Science and Technology of Welding and Joining, 1996, 1(1): 43-49
M. N. Gibbs, Bayesian Gaussian processes for regression and classification [Dissertation], PhD Thesis, Cambridge University, 1997
张晓燕, 林维正, 苏勇 等. 利用模糊神经网络识别墙体材料的种类[J]. 建筑材料学报, 1998, 1(3): 234~238
张晓燕，林维亚. 利用模糊神经网络识别墙体材料的种类[J]. 施工技术，1999，28(10)：16~17
V. den. Broek, W. H. A. M. Wienke. Plastic material identification with spectroscopic near infrared imaging and artificial neural networks [J]. Analytica Chimica Acta, 1998, (361): l ~ 2
D. C. Reddy, C. S. Reddy. Development of Neural Network Methodology to Predict TTT Diagrams [J]. AFS Transactions, 1996, 104:191~196.
J. J. Wang, P. J. wan der Wolk. Effects of carbon concentration and cooling rate on continuous cooling transformations predicted by artificial neural network. ISIJ International, 1999, 39(10): 1038~1046.
S. Malinov, W. Sha, Z. Guo. Application of artificial neural network for prediction of
time–temperature–transformation diagrams in titanium alloys [J]. Materials Science and Engineering 2000, (A283): 1~10
W. G. Vermeulen P. F. Morris, A. P. de Weijer. Prediction of martnesite start temperature using artificial neural networks [J]. Ironmaking and Steel making, 1996,23 (5): 433-437.
戴起勋, 戴希敏. 神经网络在奥氏体钢设计中的应用[J]. 钢铁研究学报，1997，9(6)：37~40.
L. Gavard, H. K. D. H. Bhadeshia, D. J. C. MacKay. Bayesian neural network model for austenite formation in steels [J]. Mater. Sci.Tech., 1996,12:453~462.
夏伯才, 钱翰城, Awad. Samir H. 铸造铝硅合金液相线自适应神经－模糊建模[J]. 重庆大学学报，2004, 27 (已接受)
L. A. Dobrzanski, W. Sikek. Gomparison of hardenability calculation methods of the heattreatable constructional steels [J]. Mater. Proc.Tech., 1997,64:117~126.
L. A. Dobrzanski, W. Sitek. Application of a neural network in modelling of hardenability of constructional steels [J]. J. Mater. Proc.Tech, 1998,78: 59-66.
L. A. Dobrzanski, W. Sitek. The modelling of hardenability of using neural networks [J]. J. Mater. Proc. Tech., 1999,92-92:8~14.
W. G. Vermeulen, P. J. van der Wolk, A. P. de Weijer. Prediction of Jominy Hardness Profiles of Steels Using Artificial Neural Networks [J]. J. Mater. Eng. Perform., 1996, 5 (1): 57-63.
牛济泰, 孙雷剑, 李海涛. 基于人工神经网络的微合金钢热轧奥氏体晶粒尺寸模型的研究[J]. 材料科学与工艺，1999，47(1)：12~16
M. Q. Li, D. J. Chen, A. M. Xiong, L. Long. An adaptive prediction model of grain size for the forging of Ti–6Al–4V alloy based on fuzzy neural networks [J]. J. Mater. Proc. Tech., 2002, 123: 377~381
M. A. Yescas, H. K. D. H. Bhadeshia, D.J. MacKay. Estimation of the amount of retained austenite in austempered ductile irons using neural networks [J]. Materials Science and Engineering, 2001A, 311: 162~173
宋新华, 陈 茁, 俞汝勤. 人工神经网络用于对位取代苯酚定量构效关系的研究[J]. 中国科学(B辑)，1993，23(3)：245~251
郭进，刘洪霖，黄铁生等．富镧N－MH电池阴极材料性能的最小二乘法 ——人工神经网络研究[J]. 中国稀土学报，1996, 14(4)：369~371
陆文聪, 陈念贻. 人工神经网络法用于V-PTC材料诸性能的预报[J]. 应用科学学报, 1996, 14(3): 375~378
刘洪霖, 包 宏. 化工冶金过程人工智能优化[M]. 北京：冶金工业出版社，1999: 24~143
王国栋, 刘相华．金属轧制过程人工智能优化[M]．北京：冶金工业出版社，2000: 67~185
刘振宇, 王昭东, 王国栋, 张强. 应用神经网络预测C-Mn钢力学性能[J]. 钢铁研究学报，1995，7(4)：61~66
R.B. Yao, C.X. Tang, G.X. Sun, Predicting grey cast iron properties with artificial neural network [J], AFS Trans., 1996, 104: 635~642.
张兆春, 吴 铸. 模式识别－人工神经网络在改善汽车发动机缸体铸件力学性能方面法人应用[J]. 金属学报，1998，34(10)：1068~1072
吴铸, 张兆春, 李重河, 钦佩, 陈念贻. 模式识别在缸体铸件缩裂缺陷原因分析方面的应用[J]. 计算机与应用化学. 1998, 15(4): 233~236
J. Voracek. Prediction of mechanical properties of cast irons [J]. Applied Soft Computing, 2001, 1: 119~125
M. Perzyk, A. Kochanski. Prediction of ductile cast iron quality by artificial neural networks[C], in: Proceedings of the International Conference on Advances in Production Engineering'98, Warsaw University of Technology, Warsaw, Poland, June 1-3, 1998, pp. 74-83.
M. Perzyk, A. W. Kochanski. Prediction of ductile cast iron quality by artificial neural networks [J]. Journal of Materials Processing Technology, 2001, 109: 305~307
S. Calcaterra, G. Campana, L. Tomesani. Prediction of mechanical properties in spheroidal cast iron by neural networks [J]. Journal of Materials Processing Technology, 2000,104: 74~80
M. A. Yescas. Modeling the microstructure and mechanical properties of austempered ductile irons [Dissertation]. PhD Thesis, University of Cambridage, Nov. 2001
T. Cool, H. K. D. H. Bhadeshia, D. J. C. Mackay. The yield and ultimate tensile strength of steel welds [J]. Materials Science & Enfineering, 1997, A223: 186~200
J. Jones. Neural network modelling of the tensile properties of nickel base superalloys [Dissertation]. PhD thesis, University of Cambridge, UK, 1997
R. J. Grylls. Mechanical properties of a high-strength cupronickel alloy-bayesian neural network analysis [J], Materials Science and Engineering, 1997, A234-236: 267-270.
F. Tancret, H.K.D.H. Bhadeshia, D.J.C Mackey. Comparison of artificial neural networks with gaussian processes to model the yield strength of nickel-base superalloys [J]. ISIJ International, 1999, 39 (10): 1020-1026
C. Dumortier, P. Lehert, Y. Riquier. Statistical modelling of mechanical tensile properties of steels by using neural networks and multivariate analysis [J]. ISIJ International, 1999, 39 (10): 986-990
F. Brun, T. Yoshida, J. D. Robson, V. Narayan, H. K. D. H. Bhadeshia, and D. J. C. MacKay.
Theoretical design of ferritic creep resistant steels using neural network, kinetic, and thermodynamic models [J]. Materials Science and Technology, 1999, 15(5): 547- 554
V. Narayan, R. Abad, B. Lopez, H.K.D.H. Bhadeshia and D.J.C. MacKay. Estimation of hot torsion stress strain curves in iron alloys using a neural network analysis [J]. ISIJ International, 1999, 39 (10): 999-1005
S. H. Lalam, H. K. D. H. Bhadeshia and D. J. C. MacKay. Estimation of mechanical properties of steel welds part 1: yield and tensile strength [J], Science and Technology of Welding, 2000, 5 (3): 135~147.
S. H. Lalam, H. K. D. H. Bhadeshia and D. J. C. MacKay. Estimation of mechanical properties of steel welds part 2: Elongation and Charpy toughness [J], Science and Technology of Welding, 2000, 5 (3) : 149~160.
G. J. Hopkin. Modeling anisothermal recrystallization austenitic stainless steels [Dissertation], PhD Thesis, University of Cambridage, Nov. 2001
E. A. Metzbower, J. J. Deloach, S. H. Lalam and H. K. D. H. Bhadeshia. Neural network analysis of strength and ductility of welding alloys for high strength low alloy ship building steels [J], Science and Technology of Welding, 2001, 6 (2): 116~124.
E. A. Metzbower, J. J. DeLoach, S. H. Lalam, and H. K. D. H. Bhadeshia. Neural network analysis of strength and ductility of welding alloys for high strength low alloy shipbuilding steels [J]. Science and Technology of Welding and Joining, 2001, 6 (2): 116~124
M. Marimuthu. Design of welding alloys creep and toughness [Dissertation]. PhD thesis, University of Cambridge, UK, 2002
F. Tancret, H. K. D. H. Bhadeshia and D. J. C. MacKay. Design of a creep resistant nickel base superalloy for power plant applications Part 1 – Mechanical properties modeling [J]. Materials Science and Technology, 2003, 19(3): 283~290
J. Warde, D. M. Knowles. Application of neural networks to mechanical property determination of Ni-base superalloys [J]. ISIJ International, 1999, 39 (10): 1006-1014
J. Warde, D.M. Knowles. Use of neural networks for alloy design [J]. ISIJ International, 1999, 39 (10): 1015~1019
张兴全，彭颖红，阮雪榆. Ti17合金本构关系的人工神经网络模型[J]. 中国有色金属学报,1999, 9(3): 590~595
L. Kong, P. D. Hodgson. The application of constitutive and artificial neural network models to predict the hot strength of steels [J]. ISIJ International, 1999, 39 (10): 991~998
S. Pernot, C. H. Lamarque. Application of neural networks to the modelling of some constitutive laws [J]. Neural Networks, 1999, 12: 371~392
J. Liu, H. B. Chang, T. Y. Hsu (Xu Zuyao), X. Y. Ruan. Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network [J]. Journal of Materials Processing Technology, 2000, 103: 200~205
A. Muliana, R. Steward, R. M. Hai-Ali, and A. Saxena. Artificial neural network and finite element modeling of nanoindentation tests [J]. Metallurgical and Materials Transactions, 2002, 33A: 1939~1945
J. Z. Wang, H. G. Ni, J. Y. He. The application of automatic acquisition of knowledge to mix design of concrete [J]. Cement and Concrete Research. 1999, 29: 1875~1880
王继宗，倪宏光. 基于BP神经网络的水泥抗压强度预测研究[J]. 硅酸盐学报. 1999,27(4): 408~414
E. Douglas, G. Pouskouleli. prediction of compressive strength of mortars made with protland cement-blast furnace slag-fly ash blends [J]. Cem. Conc. Res., 1999, 21(4): 523~534.
袁群, 赵国藩. 用神经网络方法预测新老混凝土的粘结强度. 建筑材料学报, 2001, 4(2):132~137
H. K. D. H. Bhadeshia. Neural networks in materials science [J]. ISIJ International, 1999, 39 (10): 966-979
Special Issue on "Application of neural network analysis in materials science". ISIJ International [J], 1999, 39(10)
Z. Zhang, K. Friedrich. Artificial neural networks applied to polymer composites: a review [J]. Composites Science and Technology, 2003, 63: 2029~2044
P. F. Bartelt N. G. Blist J.S. Moberley. Application of artificial intelligence to power input control in the modern foundry [J]. AFS Trans. 1995, 103: 221~225
P. F. Bartelt, J. Moberly. Applying artificial intelligence to the modern foundry [J]. Mod. Cast., 1996, 86(2): 52~55.
E. D. Larsen D. E. Clark H. B. Smart et al. Intelligent control of cupola melting [J]. AFS Trans., 1995(103): 215~219
姚瑞波, 汤崇熙, 孙国雄等. 冲天炉熔炼的人工神经网络模型[J]. 铸造，1997(2)：14~17
孙瑜, 姚瑞波, 孙国雄等. 利用人工神经网络分析熔炼工艺参数与铁液成分的关系[J]. 现代铸铁. 1998(4): 39~41
K. D. V. Prasad, Y. Eric, W. C. Cheng. A neural network system for the prediction of process parameters in pressure die casting [J]. Journal of Materials Processing Technology, 1999, 89–90: 583~590
罗蓬, 周锦进. 基于神经网络的压铸工艺参数设计[J]. 铸造，2000，49(5)：286~288
黄石生，李迪，宋永伦. 焊接过程的神经网络建模及控制研究[J]. 机械工程学报，1994，
30(3): 24~30
L. H. Qi, J. J. Hou, P. L. Cui, H. J. Li. Research on prediction of the processing parameters of liquid extrusion by BP network [J]. Journal of Materials Processing Technology, 1999, 95: 232~237
王德翔, 刘来英, 王振宝等. 基于人工神经网络技术的注塑成型工艺参数优化[J]. 模具技术. 2001(6): 1~4
王文成，吕俊伟，徐大干．基于神经网络的气体渗碳碳势测定[J]．金属热处理，1998(1)：1l~13
T. Malinova, S. Malinov, N. Pantev. Simulation of microhardness profiles for nitrocarburized surface layers by artificial neural network [J]. Surface and Coatings Technology, 20011, 35: 258~267
K. Genel. Use of artificial neural network for prediction of ion nitrided case depth in Fe-Cr alloys [J]. Materials and Design, 2003, 24: 203~207
申炳昕. 基于神经网络的烧结矿化学成分预报模型的研究[Dissertation]. 硕士学位论文，中南大学，2002
范小慧, 王海东. 烧结过程数学模型与人工智能[M]. 长沙：中南工业大学出版社，2002:10~185
I. J. Cox, R.W. Lewis, R.S. Ransing, H. Laszczewski, G. Berni. Application of neural computing in basic oxygen steelmaking [J]. Journal of Materials Processing Technology, 2002, 120: 310~315
J. Larkiola, P. Myllykoski, A. S. Korhonen, L. Cser.The role of neural networks in the optimisation of rolling processes [J]. Journal of Materials Processing Technology, 1998, 80–81: 16~23
姜义祥. 模糊聚类分析在型砂控制中的应用[J]. 工程数学学报, 2000,17(2): 136~138
金永红, 朱训生. 模糊综合评判法在链轮材料选择中的应用[J]. 机械工程师, 1997(5):7~9
S. V. Wong, A. M. S. Hamouda, M. A. El Baradie. Generalized fuzzy model for metal cutting data selection [J]. Journal of Materials Processing Technology, 1999, 89–90: 310~317
R. Grimberg, A. Savin, L. Iancu, S. Chifan. Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors [J]. Sensors and Actuators, 2000, 81: 248~250
胡定煜, 罗爱芹, 范丛林, 傅若农. 模糊聚类法用于聚乙烯材料的分类鉴别[J]. 北京理工大学学报，2000，20(3)：394~396
Y. J. Ryoo, Y. C. Lim, K. H. Kim. Cassification of material using temperature response curve fitting and fuzzy neural network [J]. Sensors and Actuators A, 2001, 94: 11~18
王守琰, 孔德英, 宋诗哲. 基于模糊模式识别的金属材料海水腐蚀形貌诊断系统[J]. 金属学报，2001，37(5):517~521
俞树荣，王志文. 材料力学性能的模糊模式识别[J]. 甘肃科学学报，1999，11(3)：1~4
F. Kojimaa, N. Kubot, S. Hashimoto. Identification of crack profiles using genetic programming and fuzzy inference [J]. Journal of Materials Processing Technology, 2001, 108: 263~267
范志康, 王贻青, 王家析. 模糊回归在改善高碳当量灰铁组织中的应用[J]. 铸造，1991(12)：9~12
黄汉英, 熊先安, 魏明新. 模糊线性规划在优化饲料配方软件中的应用[J]. 农业工程学报，2000，16(3)：107~110
艾景军, 刘大有, 曹东军, 姜丽. 模糊线性规划饲料配方模型的建立与应用[J]. 电子学报，2001，29(11)：1~3
O. P. Femminella, M.J. Starink, M. Brown, I. Sinclair, C.J. Harris and P. A. S. Reed. Data pre-processing/model initialisation in neurofuzzy modelling of structure-property relationships in Al-Zn-Mg-Cu-alloys [J]. ISIJ International, 1999, 39 (10): 1027~1037
L. Arafeh, H. Singh, and S. K. Putatunda. A neuro-fuzzy logic approach to material processing [C]. IEEE Trans. Syst., Man, Cybern. C, 1999, 29 (8): 362~370
M. Y. Chen, D.A. Linkens. Hybrid fuzzy modelling using simulated annealing and application to materials property prediction. Intelligent Processing and Manufacturing of Materials [C]. 1999. IPMM '99. Proceedings of the Second International Conference on fuzzy system, Volume: 1, 10~15 July 1999 Pages: 395~400 vol.1
M. Y. Chen. Material property prediction using neural-fuzzy network intelligent control and automation [C]. 2000. Proceedings of the 3rd World Congress on fuzzy system, Volume: 2, 28 June-2 July 2000 Pages: 1092~1097 vol.2
M. Y. Chen, D. A. Linkens. A systematic neuro-fuzzy modeling framework with application to material property prediction [J]. IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, 2001, 31(5): 781~790
M. Y. Chen, D.A. Linkens. Rule-base self-generation and simplification for data-driven fuzzy models [J]. Fuzzy Sets and Systems, 2004, 142: 243~265
M. F. Abbod, D.A. Linkens, Q. Zhu, M. Mahfouf. Physically based and neuro-fuzzy hybrid modelling of thermomechanical processing of aluminium alloys [J]. Materials Science and Engineering, 2002, A333: 397~408
Q. Zhu, M. F. Abbod, J. Talamantes-Silva, C.M. Sellars, D.A. Linkens, J.H. Beynon. Hybrid modelling of aluminium–magnesium alloys during thermomechanical processing in terms of
physically-based, neuro-fuzzy and finite element models [J]. Acta Materialia, 2003, 51: 5051~5062
夏伯才，汪法根，李明进 等. 灰铸铁强度自适应模糊神经网络预测[J]. 铸造，2000，49(6)：349~352
H. C. Qian, B. C. Xia(夏伯才), S. Z. Li, F. G. Wang. Fuzzy neural network modeling of material properties [J]. J. Mater. Process. Technol. 2002, 122(2-3): 196~200
Z. J. Luo, Q. Yang, D. Liu. A novel method for predicting the grain size of superalloy forgings based on the fuzzy method and the FEM [J]. Journal of Materials Processing Technology, 2000, 99: 246~249
夏伯才，钱翰城，汪法根 等. 冲天炉网形图重构的自适应模糊推理方法[J]. 中国机械工程，2002，14(8)：646~648
K. A. Tawab, A. K. Noor. Uncertainty analysis of welding residual stress fields [J]. Comput. Methods Appl. Mech. Engrg., 1999, 179: 327~344
R. Kovacevic, Y. M. Zhang. Neurofuzzy model-based weld fusion state estimation [J]. IEEE Control Systems Magazine, 1997, 17(2): 30~42
Y. M. Zhang, R. Kovacevic. Neurofuzzy model-based predictive control of weld fusion zone geometry [J]. IEEE Transactions On Fuzzy Systems, 1998, 6(3): 389~401
李迪, 宋永伦, 井上胜境. 基于模糊神经网络的弧焊熔池几何参数调节[J]. 焊接学报. 1999(12):131-137
E. Li, J. Li, J. S. Yu. A genetic neural fuzzy system-based quality prediction model for injection process [J]. Computers and Chemical Engineering, 2002, 26: 1253~1263
Y. H. Lu, F. H. Yeh, C.L. Li, M. T. Wu, C. H. Liu. Study of ductile fracture and preform design of upsetting process using adaptive network fuzzy inference system [J]. Journal of Materials Processing Technology, 2003, 140: 576~582
J. C. Chiou, J. Y. Yang. A CVD epitaxial deposition in a vertical barrel reactor: process modeling using cluster-based fuzzy logic models [J]. IEEE Transactions On Semiconductor Manufacturing, 1998, 11(4): 645~653
刘平，程翼宇. 辨识药物定量构效关系的模糊神经网络方法研究[J]. 高等学校化学学报，2000，21(10)：1473~1478
胡明玉，唐明述. 模糊神经网络在高强混凝土强度预测与配合比设计中的应用[J]. 计算机与应用化学, 2001,18(5): 423~428
胡明玉，唐明述. 降维网络及其在混凝土配合比优化设计中的应用[J].计算机与应用化学, 2001: 18(3): 241~246
江厚满, 张若棋, 张寿齐. 用遗传算法确定材料物态方程参数[J]. 高压物理学报. 1998,
12(1): 47~53
X. T. Feng, C. X. Yang. Genetic evolution of nonlinear material constitutive models [J]. Computer Methods in Applied Mechanics and Engineering, 2001, 190(45): 5957~5973.
郑延斌, 曹益林, 宋予民. 遗传算法用于结晶过程动力学参数辩识[J]. 计算机与应用化学. 1999,16(2): 121~132
S. Garcia. Experimental design optimization and thermophysical parameter estimation of composite materials using genetic algorithms [Dissertation], Ph.D. Thesis, Virginia Polytechnic Institute and State University, Nantes, France, 1999
G. R. Liu, W. B. Ma and X. Han, An inverse procedure for determination of material constants of composite laminates using elastic waves [J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191(33): 3543~3554.
M. Keser, S. I. Stupp. Genetic algorithms in computational materials science and engineering: simulation and design of self-assembling materials [J]. Comput. Methods Appl. Mech. Eng., 2000, 186: 373~385
N. Chakraborti, P. S. De and R. Prasad, Genetic algorithms based structure calculations for hydrogenated silicon clusters [J], Materials Letters, 2002, 55(1-2): 20~26.
K. Mizaki, T. Inoueya Genetic algorithm simulation for deposited structure of atoms [J]. Surface Science, 2002, 501: 93~101
L. R. Marim, M. R. Lemes, A. Dal Pino Jr. Ground-state of silicon clusters by neural network assisted genetic algorithm [J]. Journal of Molecular Structure (Theochem), 2003, 663: 159~165
U. Rodemerck, D. Wolf, O.V. Buyevskaya, P. Claus, S. Senkan, M. Baerns. High-throughput synthesis and screening of catalytic materials: case study on the search for a low-temperature catalyst for the oxidation of low-concentration propane [J]. Chemical Engineering Journal, 2001, 82: 3~11
U. Rodemerck, M. Baerns, M. Holena, D. Wolf. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials [J]. Applied Surface Science, 2004, 223: 168~174
S. Nandi, Y. Badhea, J. Lonari, U. Sridevi, B.S. Rao, S. S. Tambe, B.D. Kulkarni. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst [J]. Chemical Engineering Journal, 2004, 97: 115~129
韩力群. 催化剂配方的神经网络建模与遗传算法优化[J]．化工学报，1999，50(4)：58~61
赵善荣, 蒋华良, 刘东祥, 陈凯先. 遗传算法与药物分子设计[J]. 化学进展, 1997, 9(4):
405~415
M. Walker, R.E. Smith. A technique for the multiobjective optimization of laminated composite structures using genetic algorithms and finite element analysis [J]. Composite Structures, 2003, 62: 123~128
A. J. Kulkarni, K. Krishnamurthy, S. P. Deshmukh, R.S. Mishra. Microstructural optimization of alloys using a genetic algorithm [J]. Materials Science and Engineering, 2004, A 372: 213~220
张培新, 张奇志, 吴黎明等. 用神经网络－遗传算法优化MgO-B2O3-SiO2渣系组成[J]. 金属学报，1995，31B(6)：284~288
R.G. Song, Q. Z. Zhang, M. K. Tseng, B. J. Zhang. The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminum alloys [J]. Mater. Sci. Eng., 1995, C11: 39~41
R. G. Song, Q. Z. Zhang, Heat treatment optimization for 7175 aluminum alloy by genetic algorithm [J], Materials Science and Engineering, 2001, C17 (1-2): 133~137.
R. G. Song, Q. Z. Zhang, Heat treatment technique optimization for 7175 aluminum alloy by an artificial neural network and a genetic algorithm [J], Journal of Materials Processing Technology, 2001, 117(1-2): 84~88.
宋仁国, 董敏, 张奇志．7175铝合金工艺优化的遗传算法[J]．材料科学与工程，1998，16(1)：28~31
阎加强, 张培新, 伍卫琼等. 神经网络－遗传算法优化反应烧结ZrO2-SiC材料制备工艺[J]. 无机材料学报. 1998, 13(6): 937~940
潘清跃, 宋仁国, 张奇志等. 基于人工神经网络－遗传算法的1Gr18Ni9Ti钢激光表面熔凝工艺优化[J]. 材料研究学报. 1998, 12(3): 251~256
张国英, 刘贵立, 曾梅光. 模拟退火算法在无钴高强韧钢设计中的应用[J]. 材料科学与工程，2000，18(3)：19~22
张国英，刘贵立，曾海光．材料机械性能预测及模拟退火算法优化[J]．沈阳工业大学学报，2000，22(l)：55~58
王强, 邵惠鹤, 张钟俊. 神经网络的遗传设计及其在甲醛生产建模及优化中的应用[J]. 上海交通大学学报，1996，30(4)：143~150
张春慨, 邵惠鹤. 基于神经网络模型的混沌优化及其应用[J]. 化工自动化及仪表，2000，27(2)：19~22
蔡煜东, 程兆年, 陈念贻等．遗传程序设计用于工业调优——炼钢转炉炉龄预测[J]．自动化学报，1997, 23(1)：50~54
何耀华, 华贲, 杨道红. 系统模拟优化中的神经网络和遗传算法[J]. 石油炼制与化
工,1998, 29(9):53~57
周兆凯, 周毓菁. 遗传算法-优化设计-神经网络的耦合技术[J]. 武汉汽车工业大学学报. 1999, 21(2): 52~56
金蓉, 曹柳林. 遗传算法优化工业对象的RBF神经网络模型[J]. 北京化工大学学报, 2000, 27(3): 67~70
C. L. Karr, D. A. Stanley, B. McWhorter. Optimization of hydrocyclone operation using a geno-fuzzy algorithm [J]. Comput. Methods Appl. Mech. Engrg., 2000, 186: 517~530
C. H. Lim, Y. S. Yoon. Joong-Hoon Kim. Genetic algorithm in mix proportioning of high-performance concrete [J]. Cement and Concrete Research, 2004, 34: 409~420
H. Su, C. F. Yang, J. C. Shen, Z. L Tian. A systemic self-modelling method and its application to material design and optimization [J]. Modelling Simul. Mater. Sci. Eng., 2001, 9: 97~109
N. Chakraborti, R. Kumar, D. Jain. A study of the continuous casting mold using a pareto-converging genetic algorithm [J]. Applied Mathematical Modelling, 2001, 25: 287~297
N. Chakraborti, P. Mishra, A. Banerjee. Optimization of aluminum oxynitride compaction process using a Gray-coded genetic algorithm [J]. Materials Letters, 2003, 58: 136~141
B. J. Reardon. Inversion of micromechanical powder consolidation and sintering models using Bayesian inference and genetic algorithms [J]. Modelling Simul. Mater. Sci. Eng., 1999, 7: 1061~1081.
B. J. Reardon, S. R. Bingert. Inversion of tantalum micromechanical powder consolidation and sintering models using bayesian inference and genetic algorithms [J]. Acta mater., 2000, 48: 647~658
Y. X. Su, C. H. Zheng and B. Y. Duan, An optimal layout methodology for cold forming rectangular parts using genetic algorithms [J], Robotics and Computer-Integrated Manufacturing, 2001, 17(5): 429~433
罗仁平, 姚华, 彭颖红, 张永清. 微观遗传算法在预锻模优化设计中的应用[J]. 模具，2000(1)：53~55
J. C. Lin. Optimum cooling system design of a free-form injection mold using an abductive network [J]. Journal of Materials Processing Technology, 2002, 120: 226~236
J. S. Chung, S. M. Hwang. Application of a genetic algorithm to process optimal design in non-isothermal metal forming [J]. Journal of Materials Processing Technology, 1998, 80–81: 136~143
C. F. Castro, C. A. C. António, L.C. Sousa. Optimisation of shape and process parameters in metal forging using genetic algorithms [J]. Journal of Materials Processing Technology, 2004,
146: 356~364
A. G. Cunha, Modeling and optimization of single screw extrusion [Dissertation], Ph. D. Thesis, University of Minho, Guimaras, Portugal, 2000
齐乐华, 候俊杰, 杨茂奎 等. 液态挤压工艺ANN/GA建模与优化研究[J]. 西北工业大学学报，2001，19(1)：114~117
Y. S. Tarng, J. L. Wu, S. S. Yeh and S. C. Juang. Intelligent modeling and optimization of the gas tungsten arc welding process [J]. Journal of Intelligent Manufacturing, 1999, 10: 73~79
Y. S. Tarng, H. L. Tsai, S. S. Yeh. Modeling, optimization and classification of weld quality in tungsten inert gas welding [J]. International Journal of Machine Tools & Manufacture, 1999, 39: 1427~1438
余圣甫，李志远，王福德 等. 基于人工神经网络的药芯焊丝焊接工艺研究[J]. 华中理工大学学报，2000，28(4)：1~3
C. C. Tai, J. C. Lin. The optimal position for the injection gate of a die-casting die[J]. Journal of Materials Processing Technology, 1999, 86: 87~100
夏伯才, 钱翰城. 高碳当量灰铸铁成分的遗传优化[J]. 铸造，2003，52(12)：1153~1156
夏伯才, 钱翰城, 董杰等. 冲天炉熔炼过程神经－模糊建模与遗传优化[J]. 重庆大学学报，2003, 26 (8): 86~88
左演声, 刘渤, 曾磊. 神经网络与遗传算法应用于离子镀膜性能预报与工艺优化[J]. 北京工业大学学报，2000，26(1)：76~79
H. J. Li, K. Y. Jiang, K. Z. Li. Optimizing parameters of CVI process for manufacturing carbon–carbon composites by genetic algorithms [J]. Materials Letters, 2003, 57: 2366~2370
T. S. Li, C. T. Su, T. L. Chiang. Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm [J]. Computers in Industry, 2003, 50 : 113~122
H. Zheng, L. X. Kong and S. Nahavandi, Automatic inspection of metallic surface defects using genetic algorithms [J], Journal of Materials Processing Technology, 2002, 125-126: 427~433.
郭立平, 成之绪, 韩甫田 等. 粉末衍射谱图分解的模拟退火法[J]. 物理学报, 2003, 52 (11):2842~2848
I. J. Graham, K. Case and R. L. Wood, Genetic algorithms in computer-aided design [J], Journal of Materials Processing Technology, 2001, 117(1-2): 216~221.
R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces [J]. J. Global Optimization, 1997,11: 341~359
T. Takagi, M. Sugeno. Fuzzy identification of systems and its application to modeling and
control [J]. IEEE Trans on Systems, Man, and Cybernetics, 1985, 15(1): 116~132
L. X. Wang. Adaptive fuzzy system and control——design and stability analysis[M]. New York: PTR Prentice Hall, 1994:208~211
B. Kosko, S. G. Kong. Adaptive fuzzy systems for backing up a tracking and trailer [J]. IEEE Trans. on NN. 1992, 3(2): 211~233
J.,-S. R. Jang. ANFIS: Adaptive-network-based fuzzy inference systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665~685
J. C. Bezdek. Cluster validity with fuzzy sets [J]. J. Cybern., 1974, 3(3): 58~72
J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms [J]. New York: Plenum, 1981: 236.
I. Gath, A. B. Geva. Unsupervised optimal fuzzy clustering [J]. IEEE Trans. Pattern Anal. Machine Intell., 1989, 11(7): 773~81
M. Sugeno, T. Yasukawa. A fuzzy-logic-based approach to qualitative modeling [J]. IEEE Trans. Fuzzy Syst., 1993, 1(2): 7~31
M. P. Windham. Cluster validity for the fuzzy c-means clustering algorithm [J]. IEEE Trans. Pattern Anal. Machine. Intell., 1982, PAMI-4(4): 357~363
X. L. Xie, G. A. Beni. Validity measure for funnt clustering [J]. IEEE Trans. Pattern Anal. Machine Intell., 1991, 13(8): 841~846
E. H. Mamdani. Application of fuzzy algorithms for control of a simple dynamic plant [J]. Proc. Inst. Elect. Eng., 1974, 121: 1585~1588
G. Drossel. Der einflusse von schmelzebehandlungen auf die dichttheit von gusskoerpern aus Al-Si-gusslegierungen [J]. Giessereitechnik, 1981, 27(1): 7-12
R. Vijayaraghayan, N. Pelle, J. Boilean, J. Zindel, R. Beals. A micromodel for Al-Si alloys [J]. Scripta Materialia, 1996, 35(7): 861-867
M. B. Djurdjievic, W. T. Kierkus, G. E Byczynski, J. H. Sokolowski. Calculation of liquidus temperature for aluminum 3XX series of alloys [J]. AFS Trans. 1998, 106:143-147]
M. B. Djurdjievic. Development of a mathematical model nnumerical determination of liquidus temperature of low and high alloy iron-based melts [Dissertation]. PhD Thesis, University of Belgrade, 1993
M. B. Djurdjievic, W. T. Kierkus, G. E Byczynski etc. Modeling of fraction solid for a319 aluminum alloy [J]. AFS Trans., 1999, 6: 173-179
G. J. Hopkin. Modeling anisothermal recrystallization austenitic stainless steels [Dissertation], PhD Thesis, University of Cambridge, 2001
F. H. Samuel, A. M. Samuel H. W. Doty. Factors controlling thr type and morphology of
Cu-containing phases in 319 Al alloy [J]. AFS. Trans., 1996, 104: 893~901
J. Gauthier, G. H. Samuel. Tensile properties and fracture behavior of solution-heat-treated 319.2 automotive alloy [J]. AFS. Trans., 1995, 103: 849~859
L. Backerud. Solidification characteristics of aluminium alloys[C]. Vol. 2, Foundry Alloys, AFS/SkanAluminium,1990 DesPlanes, IL, USA, 1990: 71~229
C. H. Cáceres. Material properties and quality index in Al-Si-Mg casting alloys [J]. AFS Trans., 1998, 106: 601~604
L. Nnantha Narayanan, F. H. Samuel, J.E.Gruzlesk. Dissolution of iron intermetallics in Al-Si alloys though nonequilibrium heat treatment [J]. Metallugical and materials transaction A, 1995, 26A(8): 2161~2174
F. H. Samuel, A. M. Samuel, H. Liu. Effect of mgnesium content on the ageing behaviour of water-chilled Al-Si-Cu-Mg-Fe-Mn (380) alloy castings [J]. Journal of Materials Science, 1995, 30: 2531~2540
Q. G. Wang, C. J. Davidson. Solidification and precipitation behaviour of Al-Si-Mg casting alloys [J]. Journal of Materials Science, 2001, 36:739~750
H. V. Guthy. Evolution of the eutectic microstructure in chemically modified and unmodified aluminum silicon alloys [Dissertation]. Degree of Master Thesis, Worcester Polytechnic Institute, April 2002
J. Kennedy, R. C. Eberhart. Particle swarm optimization [A]. Proceedings IEEE International Conference on Neural Networks[C], IEEE Service Center, Piscataway, NJ, 1995, IV: 1942~1948
K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control [J]. IEEE Control Systems Magazine, 2002(6): 52~67
Y. Liu, K. M. Passino. Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors [J]. Journal Of Optimization Theory And Applications, 2002, 115(3): 603~628
R. E. Bellman, L. A. Zadeh. Decision-making in fuzzy environment [J]. Mangement Science, 1970, 17(4): 141~164. 见：Timothy. J. Ross 著, 钱同惠，沈其聪，葛晓滨 译. 模糊逻辑及其工程应用. 北京：电子工业出版社，2001：437~441
钱翰城，陈万志，李蜀庆. 冲天炉网形图研究述评[J]. 现代铸铁. 1985(3): 23~26
H. Jungbluth, H. Korschan. Das schmelzen in kopolofen [J]. Techniche Mitteiun Krupp-Forschungsberichte, 1938(5): 79~100
S. C. Massari, R. W. Lindsay. Melting in cupola [J], AFS. Trans. 1941, 49: 94~116
C. C. Wright. Anthracite as cupola fuel [J]. American Foundryman, 1947,11: 40~47
W. Patterson, H. Siepmann, H. Pacyna. Die Stoffbilanz and warmbilanz des kalwindkupolofens [J]. Giesserei, 1961(10): 239~258
石富. 冲天炉网形图的数学模拟与诊断[J]. 中国铸机. 1987(6)：14~19
刘启阳. Al-Si合金变质机理及共晶硅形态控制的研究与发展[J]. 铸造，1988(6)：1~3
蔡惠民. ZL108合金中RE、Mg、Cu最佳配比的试验[J]. 特种铸造及有色合金，1991(2)：6~9
S. Shivkamar, S. Ricci. Jr., D. Apelian et al. An experimental study to optimize the heat treatment of A356 alloy [J]. AFS Tras. 1987, 97:791~809
S. Shivkamar, S. Ricci. Jr., D. Apelian. Influence of solution parameters and simplified supersaturation treatments on tensile properties of A356 alloy [J]. AFS Tarns., 1990, 98:913~921
M. M. Tutile, D. L. Mclellan. Silicon particle characteristics in Al-Si-Mg castings [J]. AFS Trans., 1982, 92: 13~23
L. F. Mondolfo 著, 王祝堂 译. 铝合金的组织与性能[M]. 北京: 冶金工业出版社, 1988: 218
C. H. Cáceres, Damage by particle cracking and the quality index of Al-Si-Mg casting alloys [J]. AFS Trans., 2000, 108: 709~712
C. H. Cáceres, J. Barresi. Selection of temper and Mg content to optimise the quality index of Al-7Si-Mg casting alloys [J]. Int. J. of Cast Metals Research, 2000, 12: 377~384
C. H. Cáceres, J. Sokolowski, P. Gallo. Effect of ageing and Mg content on the quality index of two model Al-Cu-Si-Mg alloys [J]. Materials Science and Engineering, 1999, A271: 53~61