数据驱动的自洽聚类分析方法及塑性成形高效跨尺度模拟

doi:10.3901/JME.2025.04.074

摘要/Abstract

摘要： 材料微观变形决定了构件宏观成形性能，如何高效地联系宏观成形工艺和材料微观结构演化一直是塑性成形领域亟须解决的难题。通过将构件不同位置的宏观力学性能需求映射到材料的微观结构，才能根据目标微观结构的特点与分布准确设计和实施制造工艺，破解传统反复“试错”的制造模式，获得“形性”兼备的高性能构件。基于数据驱动的自洽聚类分析方法，提出跨尺度模拟单元和传统宏观唯象本构单元混合使用策略方法，开发了塑性成形高效跨尺度模拟技术。实现DP980双相钢U形件拉弯以及AA2219铝合金带筋筒体旋压的跨尺度成形分析，在能够获取成形零件关键位置微观信息演化的前提下，保证与传统宏观仿真相当的计算效率，相关研究为跨尺度模拟在工程大型构件成形中的实际应用提供了新思路。

关键词: 塑性成形, 高效跨尺度模拟, 数据驱动, 自洽聚类分析

Abstract: Material microstructure deformation determines macroscopic forming performance. How to efficiently link macroscopic forming processes with material microstructure evolution has always been a pressing problem in the field of plastic forming. By mapping the macroscopic mechanical performance requirements of different component positions to the material microstructures, manufacturing processes can be accurately designed and implemented based on the characteristics and distribution of the target microstructures to break the traditional trial-and-error mode and obtain high-performance components with the required shape and performance. Therefore, the strategy of mixed use of cross-scale elements and traditional macro-elements based on data-driven self-consistent clustering analysis is proposed, and the efficient cross-scale simulation technology for plastic forming is developed. The cross-scale forming analysis of DP980 U-shaped components and AA2219 ribbed cylinder is efficiently realized. The material microstructure deformation at key positions during the forming process is predicted, and the computational efficiency can be close to that of traditional macro models. This research provides new ideas for the practical application of cross-scale methods in the process design of large engineering structures.

Key words: plastic forming, high-efficiency cross-scale simulation, data-driven, self-consistent clustering analysis

中图分类号:

TG156

江晟达, 何霁, 郭聪, 李淑慧, 钱昌明. 数据驱动的自洽聚类分析方法及塑性成形高效跨尺度模拟[J]. 机械工程学报, 2025, 61(4): 74-85.

JIANG Shengda, HE Ji, GUO Cong, LI Shuhui, QIAN Changming. Data-driven Self-consistent Clustering Analysis and High-efficiency Cross-scale Simulation of Plastic Forming Process[J]. Journal of Mechanical Engineering, 2025, 61(4): 74-85.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2025.04.074

http://www.cjmenet.com.cn/CN/Y2025/V61/I4/74

参考文献

[1] 朱继宏，曹吟锋，翟星玥，等. 增材制造316钢高周疲劳性能的微观力学研究[J]. 力学学报，2021，53(12):3181-3189. ZHU Jihong，CAO Yinfeng，ZHAI Xingyue，et al. Micromechanical study of the high cycle fatigue property of additive-manufactured 316 steel[J]. Chinese Journal of Theoretical and Applied Mechanics，2021，53(12):3181-3189.
[2] 徐竹田，孙磊，姜天豪，等. 钛板微成形断裂尺度效应及细观损伤准则[J]. 机械工程学报，2022，58(16):51-57. XU Zhutian，SUN Lei，JIANG Tianhao，et al. Fracture size effect of titanium sheets in microforming and its meso damage criterion[J]. Journal of Mechanical Engineering，2022，58(16):51-57.
[3] 李琦，许亚坤，庄新村，等. QP钢低频振动辅助拉伸力学行为与微观组织演变研究[J]. 机械工程学报，2022，58(20):242-249. LI Qi，XU Yakun，ZHUANG Xincun，et al. Study on mechanical behavior and microstructure evolution of QP steel under low-frequency vibration assisted tension[J]. Journal of Mechanical Engineering，2022，58(20):242-249.
[4] 闵峻英，卢嘉昕，魏一凡. 工艺参数对热冲压成形AA7075力学性能的影响[J]. 塑性工程学报，2021，28(6):96-103. MIN Junying，LU Jiaxin，WEI Yifan. Effect of process parameters on mechanical properties of hot-stamped AA7075[J]. Journal of Plasticity Engineering，2021，28(6):96-103.
[5] 刘晓立，曹建国，柴雪婷，等. 变弹性模量对高强钢辊弯成型回弹预测的影响[J]. 哈尔滨工业大学学报，2018，50(7):137-143. LIU Xiaoli，CAO Jianguo，CHAI Xueting，et al. Springback prediction of DP980 steel considering nonlinear elastic modulus in cold roll forming[J]. Journal of Harbin Institute of Technology，2018，50(7):137-143.
[6] 刘晓立. 复杂截面超高强钢连续辊弯成型回弹预测与控制研究[D]. 北京:北京科技大学，2018. LIU Xiaoli. Research on springback prediction and control method of complex cross-sectional ultra high strength steel in cold roll forming[D]. Beijing:University of Science and Technology Beijing，2018.
[7] ZHU J E，XIA Y，LUO H L，et al. Influence of flow rule and calibration approach on plasticity characterization of DP780 steel sheets using Hill48 model[J]. International Journal of Mechanical Sciences，2014，89:148-157.
[8] BARLAT F，ARETZ H，YOON J W，et al. Linear transfomation-based anisotropic yield functions[J]. International Journal of Plasticity，2005，21(5):1009-1039.
[9] 曹建国，阮康，王雪松，等. 邻角精确控制的异型管辊弯成形过程数值模拟[J]. 哈尔滨工业大学学报，2023，55(1):98-105. CAO Jianguo，RUAN Kang，WANG Xuesong，et al. Numerical simulation of special-shaped tube roll forming process based on precise control of adjacent angles[J]. Journal of Harbin Institute of Technology，2023，55(1):98-105.
[10] NANDA T，SINGH V，SINGH V，et al. Third generation of advanced high-strength steels:Processing routes and properties[J]. Proceedings of the Institution of Mechanical Engineers Part L:Journal of Materials:Design and Applications，2019，233(2):209-238.
[11] KHOKHLATOVA L B，KOLOBNEV N I，OGLODKOV M S，et al. Aluminum-lithium alloys for aircraft building[J]. Metallurgist，2012，56(5-6):336-341.
[12] ZHANG E K，XU X P，CHEN Y，et al. Third-generation thermodynamic descriptions for Ta-Cr and Ta-V binary systems[J]. Materials，2022，15(6):2074.
[13] 蔡圣，刘欣梅，陈军. 薄板渐进成形数值仿真研究进展[J]. 塑性工程学报，2020，27(4):1-12. CAI Sheng，LIU Xinmei，CHEN Jun. Research progress on numerical simulation of incremental sheet forming[J]. Journal of Plasticity Engineering，2020，27(4):1-12.
[14] SU T H，HUANG S J，JEAN J G，et al. Multiscale computational solid mechanics:Data and machine learning[J]. Journal of Mechanics，2022，38:568-585.
[15] LIU X，HE J，HUANG S Y. Mechanistically in-formed artificial neural network model for discovering anisotropic path-dependent plasticity of metals[J]. Materials & Design，2023，226:111697.
[16] 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. FENG Yishuang，HE Ji，HAN Guofeng，et al. Deep learning predicting method and modeling of plastic constitutive relation of sheet metal[J]. Journal of Plasticity Engineering，2021，28(6):34-46.
[17] SPAHN J，ANDRÄ H，KABEL M，et al. A multiscale approach for modeling progressive damage of composite materials using fast Fourier transforms[J]. Computer Methods in Applied Mechanics and Engineering，2014，268:871-883.
[18] GIERDEN C，KOCHMANN J，WAIMANN J，et al. A review of FE-FFT-based two-scale methods for computational modeling of microstructure evolution and macroscopic material behavior[J]. Archives of Computational Methods in Engineering，2022，29(6):4115-4135.
[19] ELMASRY A，AZOTI W，EL-SAFTY S A，et al. A comparative review of multiscale models for effective properties of nano-and micro-composites[J]. Progress in Materials Science，2023，132:101022.
[20] KIRCHDOERFER T，ORTIZ M. Data-driven computational mechanics[J]. Computer Methods in Applied Mechanics and Engineering，2016，304:81-101.
[21] 詹梅，董赟达，翟卓蕾，等. 塑性成形快速数值仿真方法的研究进展[J]. 机械工程学报，2022，58(16):2-20. ZHAN Mei，DONG Yunda，ZHAI Zhuolei，et al. Review on fast numerical simulation method for plastic forming[J]. Journal of Mechanical Engineering，2022，58(16):2-20.
[22] LIU Z L，BESSA M A，LIU W K. Self-consistent clustering analysis:An efficient multi-scale scheme for inelastic heterogeneous materials[J]. Computer Methods in Applied Mechanics and Engineering，2016，306:319-341.
[23] YU C，KAFKA O L，LIU W K. Self-consistent clustering analysis for multiscale modeling at finite strains[J]. Computer Methods in Applied Mechanics and Engineering，2019，349:339-359.
[24] TANG S Q，ZHANG L，LIU W K. From virtual clustering analysis to self-consistent clustering analysis:A mathematical study[J]. Computational Mechanics，2018，62(6):1443-1460.
[25] LIU Z L，FLEMING M，LIU W K. Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials[J]. Computer Methods in Applied Mechanics and Engineering，2018，330:547-577.
[26] FENG Y X，YONG H D，ZHOU Y H. Efficient two-scale analysis with thermal residual stresses and strains based on self-consistent clustering analysis[J]. European Journal of Mechanics-A/Solids，2022，92:104477.
[27] HE J，GUO C，LI W K. Understanding the helicoidal damage behavior of bio-inspired laminates by conducting multiscale concurrent simulation and experimental analysis[J]. Composite Structures，2023，314:116972.
[28] GUO C，HE J. Multi-scale concurrent analysis for bio-inspired helicoidal CFRP laminates and experimental investigation[J]. Composite Structures，2022，296:115886.
[29] WEI H Y，WU C T，HU W，et al. LS-DYNA machine learning-based multiscale method for nonlinear modeling of short fiber-reinforced composites[J]. Journal of Engineering Mechanics，2023，149(3):04023003.
[30] JAIN A K. Data clustering:50 years beyond K-means[J]. Pattern Recognition Letters，2010，31(8):651-666.
[31] MOULINEC H，SUQUET P. A numerical method for computing the overall response of nonlinear composites with complex microstructure[J]. Computer Methods in Applied Mechanics and Engineering，1998，157(1-2):69-94.
[32] GU B，HE J，LI S H，et al. Anisotropic fracture modeling of sheet metals:From in-plane to out-of-plane[J]. International Journal of Solids and Structures，2020，182-183:112-140.
[33] 顾彬. 先进高强钢板各向异性断裂模型及预测研究[D]. 上海:上海交通大学，2019. GU Bin. Anisotropic fracture model for the failure prediction of advanced high strength steel sheets[D]. Shanghai:Shanghai Jiao Tong University，2019.
[34] Mehari Z A. 高强钢/超高强钢局部加热辊弯成形技术研究[D]. 北京:北京科技大学，2022. Mehari Z A. Effects of the partial heating roll forming process on HSS and UHSS profiles[D]. Beijing:University of Science and Technology Beijing，2022.
[35] PAUL S K. Effect of material inhomogeneity on the cyclic plastic deformation behavior at the microstructural level:Micromechanics-based modeling of dual-phase steel[J]. Modelling and Simulation in Materials Science and Engineering，2013，21(5):055001.
[36] SODJIT S，UTHAISANGSUK V. Microstructure based prediction of strain hardening behavior of dual phase steels[J]. Materials & Design，2012，41:370-379.
[37] 李宏伟，孙新新，詹梅，等. 多晶体材料微观组织三维有限元建模方法[J]. 塑性工程学报，2020，27(7):6-12. LI Hongwei，SUN Xinxin，ZHAN Mei，et al. Microstructure based 3D finite element modeling methods of polycrystalline materials[J]. Journal of Plasticity Engineering，2020，27(7):6-12.
[38] 杨浩，汪华苗，李大永. QP980钢拉伸过程的晶体塑性模拟[J]. 上海交通大学学报，2021，55(11):1476-1482. YANG Hao，WANG Huamiao，LI Dayong. Crystal plasticity modeling of tension process of QP980 steel[J]. Journal of Shanghai Jiaotong University，2021，55(11):1476-1482.
[39] SALVATI E，KORSUNSKY A M. An analysis of macro-and micro-scale residual stresses of Type I，II and III using FIB-DIC micro-ring-core milling and crystal plasticity FE modelling[J]. International Journal of Plasticity，2017，98:123-138.
[40] LI W N，CAI H N，ZHENG J. Characterization of strength of carbon fiber reinforced polymer composite based on micromechanics[J]. Polymers & Polymer Composites，2014，22(2):105-116.
[41] 周清春，鞠玉涛，周长省. 基于Hooke-Jeeves算法的挠性粘接件的高效内聚反演分析[J]. 工程力学，2015，32(4):1-7. ZHOU Qingchun，JU Yutao，ZHOU Changsheng. An effective inverse analysis of cohesive parameters of flexible adhesive joints based on the Hooke-Jeeves algorithm[J]. Engineering Mechanics，2015，32(4):1-7.
[42] 甘甜. 2219铝合金构件多道次热旋压仿真建模及工艺轨迹规划研究[D]. 上海:上海交通大学，2021.