有关金属材料论文参考文献范例

金属材料论文参考文献范例如下

参考文献

[1] 陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述[J]. 自动化学报, 2021, 47(5): 1017-1034.

[2] Tabernik D, Šela S, Skvarč J, et al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of Intelligent Manufacturing, 2020, 31(3): 759-776.

[3] Kumar A. Computer-vision-based fabric defect detection: A survey[J]. IEEE transactions on industrial electronics, 2008, 55(1): 348-363.

[4] Abdi H, Williams L J. Principal component analysis[J]. Wiley interdisciplinary reviews: computational statistics, 2010, 2(4): 433-459.

[5] 虞祖耀, 王洪元, 张继. 基于机器视觉的织布瑕疵在线检测[J]. 计算机工程与设计, 2016 (10): 2851-2856.

[6] Fukushima K, Miyake S. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition[M]//Competition and cooperation in neural nets. Springer, Berlin, Heidelberg, 1982: 267-285.

[7] Park J K, Kwon B K, Park J H, et al. Machine learning-based imaging system for surface defect inspection[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 2016, 3(3): 303-310.

[8] Kyeong K, Kim H. Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks[J]. IEEE Transactions on Semiconductor Manufacturing, 2018, 31(3): 395-402.

[9] He Y, Song K, Meng Q, et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(4): 1493-1504.

[10] Li F, Xi Q G. DefectNet: toward fast and effective defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-9.

[11] Ge C, Wang J, Wang J, et al. Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection[J]. Computers in Industry, 2020, 121: 103232.

[12] Li Q, Arnab A, Torr P H S. Weakly-and semi-supervised panoptic segmentation[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 102-118.

[13] Saleh F, Aliakbarian M S, Salzmann M, et al. Built-in foreground/background prior for weakly-supervised semantic segmentation[C]//European conference on computer vision. Springer, Cham, 2016: 413-432.

[14] Minhas M S, Zelek J. Semi-supervised anomaly detection using autoencoders[J]. arXiv preprint arXiv:2001.03674, 2020.

[15] Bergmann P, Löwe S, Fauser M, et al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders[J]. arXiv preprint arXiv:1807.02011, 2018.

[16] Bergmann P, Fauser M, Sattlegger D, et al. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 4183-4192.

[17] X. Zihao, W. Hongyuan, Q. Pengyu et al. "Printed surface defect detection model based on positive samples[J]. Computers, Materials & Continua, 2022,72,5925-5938.

[18] Schlegl T, Seeböck P, Waldstein S M, et al. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks[J]. Medical image analysis, 2019, 54: 30-44.

[19] Božič J, Tabernik D, Skočaj D. Mixed supervision for surface-defect detection: From weakly to fully supervised learning[J]. Computers in Industry, 2021, 129: 103459.

[20] Mnih V, Heess N, Graves A. Recurrent models of visual attention[J]. Advances in neural information processing systems, 2014, 27.

[21] Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International conference on machine learning. PMLR, 2015: 2048-2057.

[22] Wang X, Girshick R, Gupta A, et al. Non-local neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7794-7803.

[23] Don H, song K He , et aL.PGANet Pyramidfeature fusion and glotal context attnio eworkefor automaledsurfa e defec detecion]J. E TransSacions on lnduisral lfomatics,2019,16(12):7448-7458.

参考文献

参考文献

[1] 张绍东, 卢红成, 康强, 黄艳, 付雪峰. 中国科学: 化学, 2021, 51 (5), 538.

[2] 戴安邦. 实验室研究与探索, 1994, No. 3, 1.

[3] 张树永, 朱亚先, 张剑荣. 大学化学, 2018, 33 (10), 1.

[4] 李厚金, 陈六平, 张树永. 大学化学, 2022, 37 (2), 2108010.

[5] 罗雁波, 李关芳. 贵金属, 1997, 18 (4), 53.

[6] 周全法, 刘玉海, 李锋, 王琪. 黄金, 2003, 24 (8), 5.

[7] Haruta, M.; Yamada, N.; Kobayashi, T.; Iijima, S. J. Catal. 1989, 115, 301.

[8] 林晓, 曹宏斌, 李玉平, 郑诗礼, 张懿. 现代化工, 2006, 26 (6), 12.

[9] Syed, S. Hydrometallurgy 2012, 115–116, 30.

[10] 吴骏, 陈亮, 邱丽娟, 陈东辉. 黄金, 2008, 29 (6), 55.

[11] 朱经发, 林辉东, 王德汉, 彭俊杰. 土壤与环境, 2002, 11 (3), 307.

[12] 周衍波, 代淑娟, 朱巨建. 有色矿冶, 2016, 32 (2), 28.

[13] Konyratbekova, S. S.; Baikonurova, A.; Akcil, A. Min. Proc. Ext. Met. Rev. 2015, 36 (3), 198.

[14] Zhang, Y.; Li, Q.; Liu, X.; Jiang, T. Miner. Eng. 2022, 180, 107511.

[15] 吴松, 张朝, 龙明昊, 刘永杰. 大学化学, 2020, 35 (4), 32.

[16] Yue, C.; Sun, H.; Liu, W.; Guan, B.; Deng, X.; Zhang, X.; Yang, P. Angew. Chem. Int. Ed. 2017, 129 (32), 9331.

[17] Cram, D. J.; Cram, J. M. Science 1974, 183, 803.

[18] Liu, Z.; Frasconi, M.; Lei, J.; Brown, Z. J.; Zhu, Z.; Cao, D.; Lehl, J.; Liu, G.; Fahrenbach, A. C.; Botros, Y. Y.; et al. Nat. Commun. 2013, 4, 1855.

[19] Rao，B.V.; Jagadeshkumar,T.; Radha Menon, V.G. Fresenius Z.Anal.Chem.1981,309,406.

参考文献

[1] PARK J M，MOON J，BAE J W，et al. Strain rate effects of dynamic compressive deformation on mechanical properties and microstructure of CoCrFeMnNi high-entropy alloy[J]. Materials Science and Engineering：A，2018，719：155-163.

[2] 胡道春，王蕾，王红军. 基于修正Johnson-Cook模型的C5191-H磷青铜高速冲裁本构关系[J]. 塑性工程学报，2019，26(4)：234-240.

[3] 高帅，纪玉杰. 6063铝合金BP神经网络动态力学本构模型[J]. 沈阳理工大学学报，2020，39(4)：48-52.

[4] 周古昕，郎玉婧，杜秀征，等. 高强7A62铝合金动态力学响应及其J-C本构关系[J]. 中国有色金属学报，2021，31(1)：21-29.

[5] 惠旭龙，白春玉，刘小川，等. 宽应变率范围下2A16-T4铝合金动态力学性能[J]. 爆炸与冲击，2017，37(5)：871-878.

[6] 苏楠，陈明和，谢兰生，等. TC2钛合金的动态力学特征及其本构模型[J]. 材料研究学报，2021，35(3)：201-208.

[7] 陈斐洋，郭鹏程，胡泽豪，等. 不同温度下AM80镁合金的动态力学响应及本构建模[J]. 材料导报，2021，35(16)：16093-16098.

[8] 朱志武，张光瀚，卢也森. 42CrMo钢的动态力学行为及高应变率效应的本构模型[J]. 中国科学(技术科学)，2021，51(3)：249-258.

[9] HUH H，LIM J H，PARK S H. High speed tensile test of steel sheets for the stress-strain curve at the intermediate strain rate[J]. International Journal of Automotive Technology，2009，10(2)：195-204.

[10] 沙桂英，徐永波，于涛，等. AZ91镁合金的动态应力-应变行为及其应变率效应[J]. 材料热处理学报，2006，27(4)：77-81.

[11] 邓云飞，张永，吴华鹏，等. 6061-T651铝合金动态力学性能及J-C本构模型的修正[J]. 机械工程学报，2020，56(20)：74-81.

[12] 张连生，黄风雷，段卓平，等. 材料动态强度的应变率效应及其唯象本构模型[C]//第十届全国冲击动力学学术会议. 太原：中国力学学会，2011.

[13] 朱俊儿. 应变率相关的高强钢板材屈服准则与失效模型研究及应用[D]. 北京：清华大学，2015.

[14] 白春玉，葛宇静，惠旭龙，等. 金属材料的中低应变率动态拉伸试验方法研究与应用[J]. 航空科学技术，2020，31(12)：33-41.

[15] 高宁，朱志武. 铝合金应变率效应综述及其机理研究[J]. 应用数学和力学，2014，35(S1)：208-212.

[16] 刘旭红，黄西成，陈裕泽，等. 强动载荷下金属材料塑性变形本构模型评述[J]. 力学进展，2007，37(3)：361-374.

[17] FAN H D，WANG Q Y，EL-AWADY J A，et al. Strain rate dependency of dislocation plasticity[J]. Nature Communications，2021，12(1)：1845.

[18] 唐长国，朱金华，周惠久. 金属材料屈服强度的应变率效应和热激活理论[J]. 金属学报，1995，31(6)：248-253.

[19] 闫洪霞. 基于位错物理的金属塑性变形本构关系的研究[D]. 杭州：浙江大学，2011.

[20] 闫洪霞，高重阳. BCC金属物理型动态本构关系及在钽中的应用[J]. 兵工学报，2010，31(S1)：149-153.

[21] 郭伟国，田宏伟. 几种典型铝合金应变率敏感性及其塑性流动本构模型[J]. 中国有色金属学报，2009，19(1)：56-61.

[22] PéREZ-BERGQUIST S J，GRAY G T，CERRETA E K，et al. The dynamic and quasi-static mechanical response of three aluminum armor alloys：5059，5083 and 7039[J]. Materials Science & Engineering A，2011，528(29)：8733-8741.

[23] TSAI S P，TSAI Y T，CHEN Y W，et al. High-entropy CoCrFeMnNi alloy subjected to high-strain-rate compressive deformation[J].Materials Characterization，2019，147：193-198.

[24] 夏雨，王快社，胡平，等. 纯钼金属高温塑性变形行为研究进展[J]. 材料导报，2019，33(19)：3277-3289.

[25] VOYIADJIS G Z，ABED F H. A coupled temperature and strain rate dependent yield function for dynamic deformations of bcc metals[J]. International Journal of Plasticity，2006，22(8)：1398-1431.

[26] 王运，张昌明，张昱. 航空Al7050合金的静动态力学特性研究及JC本构模型构建[J]. 材料导报，2021，35(10)：10096-10102.

[27] 包志强，张勇，张柱柱，等. 38CrMoAl高强度钢动态力学性能及其J-C本构模型[J]. 机械工程材料，2021，45(5)：76-83.

[28] HUH H，YOON J H，PARK C G，et al. Correlation of microscopic structures to the strain rate hardening of SPCC steel[J]. International Journal of Mechanical Sciences，2010，52(5)：745-753.

[29] BAIK S I，GUPTA R K，KUMAR K S，et al. Temperature increases and thermoplastic microstructural evolution in adiabatic shear-bands in a high-strength and high-toughness 10 wt.% Ni Steel[J]. Social Science Electronic Publishing，2021，205：15.

[30] 武永甫，李淑慧，侯波，等. 铝合金7075-T651动态流变应力特征及本构模型[J]. 中国有色金属学报，2013，23(3)：658-665.

[31] WANG M，XU X Y，WANG H Y，et al. Evolution of dislocation and twin densities in a Mg alloy at quasi-static and high strain rates[J]. Acta Materialia，2020，201：102-113.

[32] ZHAO S，LI Z，ZHU C，et al. Amorphization in extreme deformation of the CrMnFeCoNi high-entropy alloy[J]. Science Advances，2021，7(5)：3108-3137.

[33] 毛泽宁. 纯铜塑性行为的晶粒尺寸与应变速率效应研究[D]. 南京：南京理工大学，2018.

[34] 雷经发，许孟，刘涛，等. 高应变率下6061铝合金力学性能及本构模型研究[J]. 兵器材料科学与工程，2019，42(1)：74-78.

[35] 彭建祥，李英雷，李大红. 纯钽动态本构关系的实验研究[J]. 爆炸与冲击，2003，23(2)：183-187.

[36] 郭伟国. 锻造钽的性能及动态流动本构关系[J]. 稀有金属材料与工程，2007，36(1)：23-27.

[37] 高宁. 5083铝合金宽应变率下拉压力学性能及其本构模型描述[D]. 成都：西南交通大学，2016.

[38] 刘建秀，高红霞，韩长生，等. 高应变率下Cu-P/M摩擦材料正向和反向应变率效应[J]. 机械科学与技术，2005，24(2)：230-232+247.

[39] BRUSCHI S，ALTAN T，BANABIC D，et al. Testing and modelling of material behaviour and formability in sheet metal forming[J]. CIRP Annals-Manufacturing Technology，2014，63(2)：727-749.

[40] 任冀宾，汪存显，张欣玥，等. 2A97铝锂合金的Johnson-Cook本构模型及失效参数[J]. 华南理工大学学报(自然科学版)，2019，47(8)：136-144.

[41] 张磊. 板料虚拟成形应用技术研究[D]. 济南：山东大学，2006.

[42] CHEN S R，GRAY G T. Constitutive behavior of tantalum and tantalum-tungsten alloys[J]. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science，1996，27(10)：2994-3006.

[43] LEE B J，VECCHIO K S，AHZI S，et al. Modeling the mechanical behavior of tantalum[J]. Metallurgical and Materials Transactions a-Physical Metallurgy and Materials Science，1997，28(1)：113-122.

[44] 崔奎虎. 航空钛合金高应变率条件下SHPB压杆实验与仿真[D]. 天津：天津大学，2013.

[45] 彭鸿博，张宏建. 金属材料本构模型的研究进展[J]. 机械工程材料，2012，36(3)：5-10+75.

[46] JOHNSON G R，COOK W H. A constitutive model and data for metals subjected to large strains，high strain rates and high temperatures[J]. Engineering Fracture Mechanics，1983，21：541-548.

[47] KHAN A S，HUANG S. Experimental and theoretical study of mechanical behavior of 1100 aluminum in the strain rate range 10^-5 -10⁴ s-¹[J]. International Journal of Plasticity，1992，8(4)：397-424.

[48] KHAN A S，ZHANG H，TAKACS L. Mechanical response and modeling of fully compacted nanocrystalline iron and copper[J]. International Journal of Plasticity，2000，16(12)：1459-1476.

[49] FARROKH B，KHAN A S. Grain size，strain rate，and temperature dependence of flow stress in ultra-fine grained and nanocrystalline Cu and Al：Synthesis，experiment，and constitutive modeling[J]. International Journal of Plasticity，2009，25(5)：715-732.

[50] KHAN A S，LIU H. A new approach for ductile fracture prediction on Al 2024-T351 alloy[J]. International Journal of Plasticity，2012，35：1-12.

[51] MOLINARI A，RAVICHANDRAN G. Constitutive modeling of high-strain-rate deformation in metals based on the evolution of an effective microstructural length[J]. Mechanics of Materials，2005，37(7)：737-752.

[52] FIELDS D S，BACKOFEN W A. Determination of strain hardening characteristics by torsion testing[J]. ASTM，Proc. Am. Soc. Test Mater.，1957，57：1259-1272.

[53] 李双蓓，吴园，赵璇，等. 金属材料宽应变率下动态本构模型及其扩展有限元应用[J]. 广西大学学报(自然科学版)，2021，46(4)：905-916.

[54] LIN Y C，CHEN X M，LIU G. A modified Johnson–Cook model for tensile behaviors of typical high-strength alloy steel[J]. Materials Science & Engineering A，2010，527(26)：6980-6986.

[55] KHAN A S，LIANG R. Behaviors of three BCC metal over a wide range of strain rates and temperatures：experiments and modeling[J]. International Journal of Plasticity，1999，15(10)：1089-1109.

[56] ZHANG Y B，YAO S，HONG X，et al. A modified Johnson-Cook model for 7N01 aluminum alloy under dynamic condition[J]. Journal of Central South University，2017，24(11)：2550-2555.

[57] TAN J，ZHAN M，LIU S，et al. A modified Johnson-Cook model for tensile flow behaviors of 7050-T7451 aluminum alloy at high strain rates[J]. Materials Science and Engineering：A，2015，631(1)：214-219.

[58] ZHANG H，WEN W，CUI H. Behaviors of IC10 alloy over a wide range of strain rates and temperatures：Experiments and modeling[J]. Materials Science & Engineering A，2008，504(1-2)：99-103.

[59] MEYERS M A，CHEN Y J，MARQUIS F D S. High-strain，high-strain-rate behavior of tantalum[J]. Metallurgical and Materials Transactions A，1995，26：2493–2501.

[60] 李恒奎，张光瀚，赵晓春，等. 基于改进Johnson-Cook模型的5083P-0铝合金动态本构关系研究[J]. 宇航材料工艺，2021，51(3)：17-24.

[61] YU W，LI Y，CAO J，et al. The dynamic compressive behavior and constitutive models of a near α TA23 titanium alloy[J]. Materials Today Communications，2021，29：102863.

[62] 徐俊瑞，文智生，苏继爱，等. 镁合金板材高速率Johnson-Cook本构模型的建立[C]// 创新塑性加工技术，推动智能制造发展——第十五届全国塑性工程学会年会暨第七届全球华人塑性加工技术交流会学术会议论文集. 济南：中国机械工程学会，2017：336-339.

[63] MIRONE G，BARBAGALLO R. How sensitivity of metals to strain，strain rate and temperature affects necking onset and hardening in dynamic tests[J]. International Journal of Mechanical Sciences，2021，195：106249.

[64] GAO N，ZHU Z，XIAO S，et al. A constitutive model research based on dislocation mechanism of 5083 aluminum alloy[J]. Journal of Mechanics，2019，35(2)：145-152.

[65] ZERILLI F J，ARMSTRONG R W. Dislocation-mechanics-based constitutive relations for material dynamics calculations[J]. Journal of Applied Physics，1987，61(5)：1816-1825.

[66] ZERILLI F J，ARMSTRONG R W. Description of tantalum deformation behavior by dislocation mechanics based constitutive relations[J]. Journal of Applied Physics，1990，68(4)：1580-1591.

[67] ZHANG H，WEN W，CUI H，et al. A modified Zerilli–Armstrong model for alloy IC10 over a wide range of temperatures and strain rates[J]. Materials Science & Engineering A，2009，527(1-2)：328-333.

[68] ZHANG H，WEN W，CUI H，et al. A study on flow behaviors of alloy IC10 over a wide range of temperatures and strain rates[J]. TMS Annual Meeting，2009，1：219-226.

[69] SAMANTARAY D，MANDAL S，BORAH U，et al. A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel[J]. Materials Science & Engineering A，2009，526(1-2)：1-6.

[70] FOLLANSBEE P S，KOCKS U F. A constitutive description of the deformation of copper based on the use of the mechanical threshold stress as an internal state variable[J]. Acta Metallurgica，1988，36(1)：81-93.

[71] VOYIADJIS G Z，ABED F H. Microstructural based models for bcc and fcc metals with temperature and strain rate dependency[J]. Mechanics of Materials，2005，37(2-3)：355-378.

[72] VOYIADJIS G Z，ALMASRI A H. A physically based constitutive model for fcc metals with applications to dynamic hardness[J]. Mechanics of Materials，2008，40(6)：549-563.

[73] TABEI A，ABED F H，VOYIADJIS G Z，et al. Constitutive modeling of Ti-6Al-4V at a wide range of temperatures and strain rates[J]. European Journal of Mechanics / A Solids，2017，63：128-135.

[74] CAI M C，NIU L S，MA X F，et al. A constitutive description of the strain rate and temperature effects on the mechanical behavior of materials[J]. Mechanics of Materials，2010，42(8)：774-781.

[75] NEMAT-NASSER S，ISACS J B. Direct measurement of isothermal flow stress of metals at elevated temperatures and high strain rates with application to Ta and TaW alloys[J]. Acta Materialia，1997，45(3)：907-919.

[76] NEMAT-NASSER S，LI Y. Flow stress of f.c.c. polycrystals with application to OFHC Cu[J]. Acta Materialia，1998，46(2)：565-577.

[77] NEMAT-NASSER S，GUO W，LIU M. Experimentally-based micromechanical modeling of dynamic response of molybdenum[J]. Scripta Materialia，1999，40(7)：859-872.

[78] NEMAT-NASSER S，GUO W G，CHENG J Y. Mechanical properties and deformation mechanisms of a commercially pure titanium[J]. Acta Materialia，1999，47(13)：3705-3720.

[79] PENG Z S，JI C H，PEI W C，et al. Constitutive relationship of TC4 titanium alloy based on back propagating (BP) neural network (NN)[J]. Metalurgija，2021，60(3-4)：277-280.

[80] 钟明君，王克鲁，鲁世强，等. MoNb合金高温变形行为及BP神经网络本构模型研究[J]. 塑性工程学报，2020，27(12)：177-182.

[81] 张小波，王克鲁，鲁世强，等. 基于BP神经网络的Ti3Al基合金本构关系模型的建立[J]. 特种铸造及有色合金，2013，33(3)：224-226.

[82] 孙宇，曾卫东，赵永庆，等. 基于BP神经网络Ti600合金本构关系模型的建立[J]. 稀有金属材料与工程，2011，40(2)：220-224 .Modeling of constitutive relationship of ti600 alloy using BP artificial neural network[J]. Rare Metal Materials and Engineering，2011，40(2)：220-224.

[83] YAN J，PAN Q L，LI A D，et al. Flow behavior of Al–6.2Zn-0.70Mg-0.30Mn-0.17Zr alloy during hot compressive deformation based on Arrhenius and ANN models[J]. Transactions of Nonferrous Metals Society of China，2017，27(3)：638-647.

[84] 冯怡爽，何霁，韩国丰，等. 金属板材塑性本构关系的深度学习预测方法及建模[J]. 塑性工程学报，2021，28(6)：34-46.

[85] 罗锐，曹赟，邱宇，等. 基于BP人工神经网络喷射成形7055铝合金的本构模型[J]. 航空材料学报，2021，41(1)：35-44.

[86] 唐学峰，黄振，温红宁，等. 基于深度神经网络的TA15高温拉伸变形行为精确预测[J]. 锻压技术，2021，46(9)：67-76.

[87] GAO T J，ZHAO D，ZHANG T W，et al. Strain-rate-sensitive mechanical response，twinning，and texture features of NiCoCrFe high-entropy alloy：Experiments，multi-level crystal plasticity and artificial neural networks modeling[J]. Journal of Alloys and Compounds，2020，845：14.

[88] 陈明和，王宁. 高强铝合金热塑性变形本构关系研究现状及发展趋势[J]. 中国机械工程，2020，31(8)：997-1007.

[89] LIU X，TIAN S，TAO F，et al. A review of artificial neural networks in the constitutive modeling of composite materials[J]. Composites Part B Engineering，2021，224(3)：109152.