带筋整体壁板预应力喷丸成形数值模拟及变形预测

doi:10.7527/S1000-6893.2019.22847

材料工程与机械制造

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带筋整体壁板预应力喷丸成形数值模拟及变形预测

田硕^1,2,3, 尚建勤^1,2,3, 盖鹏涛^1,2,3, 陈福龙^1,2,3, 曾元松^1,2,3

1. 中国航空制造技术研究院, 北京 100024;
2. 塑性成形技术航空科技重点实验室, 北京 100024;
3. 数字化塑性成形技术及装备北京市重点实验室, 北京 100024

收稿日期:2018-12-11 修回日期:2018-12-27 出版日期:2019-10-15 发布日期:2019-02-22
通讯作者: 尚建勤 E-mail:jxry66s@126.com
基金资助:
国家级项目

Numerical simulation and deformation prediction of stress peen forming for integrally-stiffened panels

TIAN Shuo^1,2,3, SHANG Jianqin^1,2,3, GAI Pengtao^1,2,3, CHEN Fulong^1,2,3, ZENG Yuansong^1,2,3

1. AVIC Manufacturing Technology Institute, Beijing 100024, China;
2. Aeronautical Science and Technologies Key Laboratory for Plastic Forming, Beijing 100024, China;
3. Beijing Key Laboratory of Digital Plasticity Forming Technology and Equipment, Beijing 100024, China

Received:2018-12-11 Revised:2018-12-27 Online:2019-10-15 Published:2019-02-22
Supported by:
National Project

摘要/Abstract

摘要： 带筋整体壁板特别是高筋整体壁板具有优异的结构效率、减重效益和密封效果，在航空航天等领域备受青睐。预应力喷丸成形是大中型、长寿命、高性能复杂带筋整体壁板的一种不可多得的有效成形手段。数值模拟是促进带筋整体壁板预应力喷丸成形技术研究、进步与应用的一种极具潜力的途径。针对带筋整体壁板预应力喷丸成形数值模拟，建立了基于响应面函数的多弹丸撞击有限元模型、基于应变中性层内移的反弯曲应力场法模拟模型、预应力喷丸成形RBF神经网络预测模型，实现了喷丸成形应力场法数值模拟、预应力喷丸成形较高精度数值模拟与变形预测，为带筋整体壁板预应力喷丸成形技术研究和实际应用提供了一种更为便捷、高效、经济的途径。

关键词: 带筋整体壁板, 喷丸, 预应力喷丸成形, 应变中性层, 有限元方法, RBF人工神经网络, 变形预测

Abstract: The integrally-stiffened panel, especially high stiffened integral panel, is commonly used in the aerospace industry due to its high structural efficiency, obvious weight loss benefits and good sealing effect. Stress peen forming is an effective process to induce curvatures in large-medium, long-life, high-performance complex integrally-stiffened panel. Numerical simulation is a promising approach that promotes the investigation, development and application in stress peen forming of integrally-stiffened panel. In order to simulate the stress peen forming of integrally-stiffened panel, a multi-shot impact finite element model based on response surface function, an reverse bending stress field simulation model based on strain neutral layer shifting to inner compression region and a stress peen forming RBF neural network prediction model are established. By applying these three models, the numerical simulation of stress field method, the high-precision numerical simulation and deformation prediction of stress peen forming for integrally-stiffened panel are realized. The present study provides a more convenient, efficient and economical way for the research and application of the stress peen forming process for integrally-stiffened panel.

Key words: integrally-stiffened panel, shot peening, stress peen forming, strain neutral layer, finite element method, RBF artificial neural network, deformation prediction

中图分类号:

V261
TG30

田硕, 尚建勤, 盖鹏涛, 陈福龙, 曾元松. 带筋整体壁板预应力喷丸成形数值模拟及变形预测[J]. 航空学报, 2019, 40(10): 422847-422847.

TIAN Shuo, SHANG Jianqin, GAI Pengtao, CHEN Fulong, ZENG Yuansong. Numerical simulation and deformation prediction of stress peen forming for integrally-stiffened panels[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(10): 422847-422847.

参考文献 23

[1]	尚建勤, 曾元松. 喷丸成形技术及未来发展与思考[J]. 航空制造技术, 2010(16):26-29. SHANG J Q, ZENG Y S. Shot forming technology and its development trend and thinking[J]. Aeronautical Manufacturing Technology, 2010(16):26-29(in Chinese).
[2]	尚建勤. 预应力对喷丸成形的影响[J]. 锻压技术, 2001(1):42-43. SHANG J Q. Effect of pre-stress on peen-forming[J]. Forging & Stamping Technology, 2001(1):42-43(in Chinese).
[3]	曾元松, 尚建勤, 许春林, 等. ARJ21飞机大型超临界机翼整体壁板喷丸成形技术[J]. 航空制造技术, 2007(3):38-41. ZENG Y S, SHANG J Q, XU C L, et al. Shot peen forming technology for large supercritical wing integral panel of ARJ21 aircraft[J]. Aeronautical Manufacturing Technology, 2007(3):38-41(in Chinese).
[4]	XIAO X D, WANG Y J, ZHANG W, et al. Numerical research on stress peen forming with prestressed regular model[J]. Journal of Materials Processing Technology, 2016, 229:501-513.
[5]	曾元松, 黄遐. 大型整体壁板成形技术[J]. 航空学报, 2008, 29(3):721-727. ZENG Y S, HUANG X. Forming technologies of large integral panel[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(3):721-727(in Chinese).
[6]	MIAO H Y, DEMERS D, LAROSE S, et al. Experimental study of shot peening and stress peen forming[J]. Journal of Materials Processing Technology, 2010, 210(15):2089-2102.
[7]	MEYER R, RECCIUS H, SCHULWIN R. Shot peen-forming of nc-machined parts with integrated stringers using large balls[C]//3rd International Conference on Shot Peening. Garmisch-Partenkirchen:Deutsche Gesellschaft fur Metallkunde, 1987:327-334.
[8]	XIE L C, JIANG C H, LU W J, et al. Effect of stress peening on surface layer characteristics of (TiB+TiC)/Ti-6Al-4V composite[J]. Materials and Design, 2012, 33:64-68.
[9]	MIAO H Y, LAROSE S, PERRON C, et al. Numerical simulation of the stress peen forming process and experimental validation[J]. Advances in Engineering Software, 2011, 42(11):963-975.
[10]	XIAO X D, WANG Y J, ZHANG W, et al. Numerical simulation of stress peen forming with regular indentation[J]. Procedia Engineering, 2014, 81:867-872.
[11]	GARIEPY A, CYR J, LEVERS A, et al. Potential applications of peen forming finite element modelling[J]. Advances in Engineering Software, 2012, 52:60-71.
[12]	FAUCHEUX P A, GOSSELIN F P, LEVESQUE M. Simulating shot peen forming with eigenstrains[J]. Journal of Materials Processing Technology, 2018, 254:135-144.
[13]	MALEKI E, SHERAFATNIA K. Investigation of single and dual step shot peening effects on mechanical and metallurgical properties of 18CrNiMo7-6 steel using artificial neural net-work[J]. International Journal of Materials, Mechanics and Manufacturing, 2016, 4:100-105.
[14]	KARATAS C, SOZEN A, DULEK E. Modelling of residual stresses in the shot peened material C-1020 by artificial neural network[J]. Expert Systems with Applications, 2009, 36(2):3514-3521.
[15]	SUN L X, LI M Q, LI H M. Prediction model for surface layer microhardness of processed TC17 via high energy shot peening[J]. Transactions of Nonferrous Metals Society of China, 2017, 27:1956-1963.
[16]	ZHANG X J, WANG J B, WANG Y J, et al. Prediction of shot peen forming parameters of integral aircraft wing panels[J]. Materials Science Forum, 2006, 532:937-940.
[17]	王伟, 娄相芽, 杨永红, 等. 基于RBF人工神经网络的喷丸成形工艺参数预测方法[J]. 组合机床与自动化加工技术, 2008(8):43-49. WANG W, LOU X Y, YANG Y H, et al. Prediction of shot peening parameters based on the RBF artificial neural network[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2008(8):43-49(in Chinese).
[18]	AIOBAID Y F. Shot peening mechanics:experimental and theoretical analysis[J]. Mechanics of Materials, 1995, 19(2-3):251-260.
[19]	GARIEPY A, LAROSE S, PERRON C, et al. Shot peening and peen forming finite element modelling-Towards a quantitative method[J]. International Journal of Solids and Structures, 2011, 48(20):2859-2877.
[20]	王永军, 何俊杰, 肖旭东, 等. 大型机翼整体壁板喷丸延展量数值模拟[J]. 锻压技术, 2016, 41(8):63-69. WANG Y J, HE J J, XIAO X D, et al. Numerical simulation on elongation of large integral wing panel after shot peening[J]. Forging & Stamping Technology, 2016, 41(8):63-69(in Chinese).
[21]	李源, 雷丽萍, 曾攀. 弹丸束喷丸有限元模型数值模拟及试验研究[J]. 机械工程学报, 2011, 47(22):43-48. LI Y, LEI L P, ZENG P. Shot stream finite element model for shot peening numerical simulation and its experiment study[J]. Journal of Mechanical Engineering, 2011, 47(22):43-48(in Chinese).
[22]	赖松柏, 陈同祥, 于登云. 整体壁板结构弹塑性弯曲中性层位置分析[J]. 宇航材料工艺, 2012(1):35-37. LAI S B, CHEN T X, YU D Y. Analysis of neutral surface position of integrally stiffened panel in elasto-plastic bending[J]. Aerospace Materials & Technology, 2012(1):35-37(in Chinese).
[23]	李秀莲. 等效截面法求解异质双材料组合梁[J]. 青海大学学报(自然科学版), 2008, 26(6):93-96. LI X L. Equivalent section method for solving heterogeneous two-material composite beams[J]. Journal of Qinghai University(Natural Science Edition), 2008, 26(6):93-96(in Chinese).

E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

带筋整体壁板预应力喷丸成形数值模拟及变形预测

Numerical simulation and deformation prediction of stress peen forming for integrally-stiffened panels

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