基于Kriging模型的某高射速自动机内抽壳滑板疲劳优化

doi:10.12382/bgxb.2023.0722

摘要/Abstract

摘要：

为提高抽壳滑板的疲劳寿命,进而满足射速在1500发/min左右的某10连发高射速自动机寿命达1000发的最低寿命要求,提出一种基于Kriging回归的代理模型用于抽壳滑板的疲劳优化。在与试验结果相符的有限元模型基础上,通过拉丁超立方采样设置初始样本点,构建与样本点相对应的结构模型,并计算每一组样本的理论寿命。根据初始样本点构建Kriging代理模型的同时,采用以改善期望准则为加点准则、遗传算法为子优化求解算法的代理优化算法来对目标函数进行寻优。优化后抽壳滑板的疲劳寿命提高到1193发,且经试验验证,优化结果满足自动机战术技术指标。研究结果表明,通过基于Kriging和遗传算法的代理优化算法能够快速有效地寻优得到全局最优解,适用于高射速自动机内破断零部件的疲劳优化,对工程应用具有一定的借鉴意义。

关键词: 有限元模拟, 疲劳分析, Kriging回归, 改善期望准则, 遗传算法

Abstract:

In order to improve the fatigue life of shell extractor skateboard and meet the minimum life requirement of a certain 10-shot-high-firing-speed automatic gun with a firing rate of around 1500 shots per minute up to 1000 rounds, a surrogate model based on Kriging regression is proposed for the fatigue optimization of shell extractor skateboard. On the basis of the finite element model consistent with the experimental results, the initial sample points are set by Latin hypercube sampling, a structural model corresponding to the sample points is constructed, and the theoretical life of each group of samples is calculated. A Kriging surrogate model is constructed according to the initial sample points, and the surrogate optimization algorithm, which takes the expected improvement (EI) criterion as the addition point criterion and the genetic algorithm as the sub-optimization algorithm, is used to optimize the objective function. The fatigue life of shell extractor skateboard is increased to 1193 rounds after optimization, and the optimized result meets the tactical technical index of the automatic gun after experimental verification. The research results show that the surrogate optimization algorithm based on Kriging and genetic algorithm can quickly and effectively find the global optimal solution, which is applicable to the fatigue optimization of broken parts in the high-firing-speed automatic gun, and has certain reference significance for engineering applications.

Key words: finite element simulation, fatigue analysis, Kriging regression, expected improvement criterion, genetic algorithm

中图分类号:

TJ303⁺.9

田恒旭, 林圣业, 李浩, 巫英豪, 王茂森, 戴劲松. 基于Kriging模型的某高射速自动机内抽壳滑板疲劳优化[J]. 兵工学报, 2024, 45(10): 3585-3595.

TIAN Hengxu, LIN Shengye, LI Hao, WU Yinghao, WANG Maosen, DAI Jinsong. Fatigue Optimization of Sell Extractor Skateboard in a High-firing-speed Automatic Gun Based on Kriging Model[J]. Acta Armamentarii, 2024, 45(10): 3585-3595.

图/表 25

图1 抽壳机构结构简图

Fig.1 Structural diagram of shell extractor mechanism

图2 抽壳滑板结构

Fig.2 Structural diagram of shell extractor skateboard

图3 碰撞位置处有限元网格模型

Fig.3 Finite element mesh model of collision location

图4 抽壳机构有限元模型

Fig.4 Finite element mesh model of shell extractor mechanism

图5 膛压曲线

Fig.5 Bore pressure curve

表1 弹壳材料参数

Table 1 Shell material parameters

参数	数值
密度/(kg·m^-3)	7820
弹性模量/MPa	206000
泊松比	0.3
屈服强度/MPa	400

表2 某高强度合金钢的力学性能参数[12]

Table 2 Mechanical properties of a high-strength alloy steel [12]

参数	数值
密度/(kg·m^-3)	7800
弹性模量/MPa	213000
泊松比	0.3
弹性极限/MPa	1482
屈服强度/MPa	1620
强度极限/MPa	1705

图6 抽壳力与抽壳滑板位移的关系曲线

Fig.6 Relationship curve between the shell extraction force and the displacement of shell extractor skateboard

图7 碰撞位置处应力云图

Fig.7 Nephogram of stress at collision position

图8 圆角位置处应力-时间曲线

Fig.8 Stress-time curve of the fillet position

表3 E-N曲线参数值

Table 3 E-N curve parameter values

参数	数值	参数	数值
σ'_f/MPa	1705	ε'_f	0.221
b	-0.087	c	-0.58
E_f/MPa	213000

图9 某高强度合金钢的E-N曲线

Fig.9 E-N curve of a high-strength alloy steel

图10 抽壳滑板疲劳断裂实物图

Fig.10 Physical diagram of fatigue fracture of shell extractor skateboard

图11 小凸台薄弱位置疲劳寿命云图

Fig.11 Fatigue life nephogram of weak position of a small boss

表4 各设计变量参数设置

Table 4 Parameter setting of each design variable

参数	数值	参数	数值
θ/(°)	90~110	B/mm	6~10
h/mm	10~18	R/mm	0.4~4

表5 样本点试验设计及对应仿真结果

Table 5 Sample point test design and corresponding simulated results

序号	θ/(°)	h/mm	R/mm	B/1	T/发
1	93.11	10.83	1.64	6.05	707
2	92.23	13.04	1.90	6.97	500
3	93.97	14.76	2.81	7.73	855
4	90.44	16.28	3.75	8.63	417
5	91.34	17.01	1.07	9.83	205
6	95.56	10.01	0.68	6.56	414
7	94.76	12.14	2.08	7.18	476
8	97.16	13.79	2.66	8.35	363
9	97.94	15.64	4.00	8.82	609
10	96.39	17.54	1.38	9.25	392
11	101.79	11.60	2.44	6.22	1054
12	99.69	11.82	3.47	7.59	1124
13	100.42	14.32	1.43	8.07	323
14	98.00	15.30	0.79	8.97	403
15	99.32	16.75	2.14	9.61	425
16	105.79	11.15	0.51	6.37	412
17	102.82	12.59	3.14	6.85	613
18	104.15	14.13	0.92	8.23	293
19	104.74	14.95	1.23	8.49	253
20	102.31	17.83	2.30	9.39	426
21	107.26	12.42	1.77	7.40	493
22	108.99	13.22	2.88	7.88	415
23	109.62	15.98	3.59	9.15	603
24	108.27	16.44	3.37	9.94	444

图12 LHS点分布图

Fig.12 Distribution map of Latin hypercube sampling points

图13 初始代理模型图

Fig.13 Initial surrogate model diagram

图14 代理优化算法流程框图

Fig.14 Flowchart of surrogate-based optimization algorithm

表6 新添样本点及对应仿真结果

Table 6 Newly added sample points and corresponding simulated results

序号	θ/(°)	h/mm	R/mm	b/mm	T/发
1	100.71	11.55	3.07	7.08	1011
2	100.67	11.07	3.27	7.03	851
3	101.94	11.83	3.74	7.13	794
4	100.79	11.89	2.81	7.10	502
5	99.87	11.57	3.45	7.49	1052

图15 更新后代理模型图

Fig.15 Updated surrogate model diagram

图16 适应度变化趋势

Fig.16 Trend diagram of fitness change

图17 优化后薄弱位置处疲劳寿命云图

Fig.17 Fatigue life nephogram of shell extractor skateboard at weak position after optimization

图18 实弹射击试验现场

Fig.18 Live ammunition shooting experimental site

图19 试验后改进位置处实物图

Fig.19 The physical image of the improved shell extractor skateboard after experiment

参考文献 25

[1]	翟剑锋, 戴劲松, 王茂森, 等. 某高速自动机不同工作模式条件下断裂底火运动轨迹分析[J]. 兵工自动化, 2021, 40(3): 90-96.
	ZHAI J F, DAI J S, WANG M S, et al. Trajectory analysis of broken primer under different working modes of certain type high-speed automatic gun[J]. Ordnance Industry Automation, 2021, 40(3): 90-96. (in Chinese)
[2]	戴劲松, 王茂森, 苏晓鹏, 等. 现代火炮自动机设计理论[M]. 北京: 国防工业出版社, 2018.
	DAI J S, WANG M S, SU X P, et al. Design theory of modern automatic cannon[M]. Beijing: National Defense Industry Press, 2018. (in Chinese)
[3]	翟啸雷, 戴劲松, 王茂森, 等. 某新型转膛自动机换向器的疲劳寿命分析[J]. 机械制造与自动化, 2021, 50(4): 40-42, 52.
	ZHAI X L, DAI J S, WANG M S, et al. Fatigue life analysis of new type of revolving automaton commutator[J]. Machine Building & Automation, 2021, 50(4): 40-42, 52. (in Chinese)
[4]	易当祥, 刘春和, 吕国志, 等. 自行火炮行动系统关重件的疲劳寿命仿真[J]. 兵工学报, 2007, 28(2): 138-143.
	YI D X, LIU C H, LÜ G Z, et al. Fatigue life simulation of key components of running gear for a self-propelled gun[J]. Acta Armamentarii, 2007, 28(2): 138-143. (in Chinese)
[5]	邓威, 毛保全, 冯帅, 等. 基于Kriging模型的武器站炮口扰动优化[J]. 兵工学报, 2016, 37(10): 1795-1802. doi: 10.3969/j.issn.1000-1093.2016.10.005
	DENG W, MAO B Q, FENG S, et al. Optimization of muzzle disturbance of overhead weapon station based on Kriging model[J]. Acta Armamentarii, 2016, 37(10): 1795-1802. (in Chinese) doi: 10.3969/j.issn.1000-1093.2016.10.005
[6]	LEE K D, KIM K Y. Surrogate based optimization of a laidback fan-shaped hole for film-cooling[J]. International Journal of Heat and Fluid Flow, 2011, 32(1): 226-238.
[7]	苏伟, 高正红, 夏露. 一种代理遗传算法及其在气动优化设计中的应用[J]. 西北工业大学学报, 2008(3): 303-307.
	SU W, GAO Z H, XIA L. An effective surrogate-assisted genetic algorithm for airfoil aerodynamic optimization design[J]. Journal of Northwestern Polytechnical University, 2008(3): 303-307. (in Chinese)
[8]	孟凡念, 张子琦, 苏晓龙, 等. 基于Kriging代理模型的离心通风机叶片优化[J]. 液压与气动, 2023, 47(5): 85-91. doi: 10.11832/j.issn.1000-4858.2023.05.011
	MENG F N, ZHANG Z Q, SU X L, et al. Centrifugal fan blade optimization based on Kriging surrogate model[J]. Chinese Hydraulics & Pneumatics, 2023, 47(5): 85-91. (in Chinese)
[9]	张纯, 何君儒, 周宇轩, 等. 基于深度神经网络代理模型的盾构隧道密封垫断面优化[J]. 工程力学, 2023(7): 137-144.
	ZHANG C, HE J R, ZHOU Y X, et al. Shield tunnel gasket section optimization based on deep neural network surrogate model[J]. Engineering Mechanics, 2023(7): 137-144. (in Chinese)
[10]	谭添. 某新型低后坐力自动机动态特性分析[D]. 南京: 南京理工大学, 2019.
	TAN T. The dynamic characteristic analysis of a new type low recoil automaton[D]. Nanjing: Nanjing University of Science and Technology, 2019. (in Chinese)
[11]	谭波, 侯健, 可学为, 等. 舰炮抽壳机构抽壳过程仿真分析[J]. 火炮发射与控制学报, 2014, 35(4): 49-52.
	TAN B, HOU J, KE X W, et al. Simulation analysis of naval gun extractor mechanism in process of cartridge case extraction[J]. Journal of Gun Launch & Control, 2014, 35(4): 49-52. (in Chinese)
[12]	罗海文, 沈国慧. 超高强高韧化钢的研究进展和展望[J]. 金属学报, 2020, 56(4): 494-512. doi: 10.11900/0412.1961.2019.00328
	LUO H W, SHEN G H. Progress and perspective of ultra-high strength steels having high toughness[J]. Acta Metallurgica Sinica, 2020, 56(4): 494-512. (in Chinese) doi: 10.11900/0412.1961.2019.00328
[13]	张福明, 路明, 郝佳胜. 发动机工况对缸盖低周疲劳影响分析与研究[J]. 内燃机与配件, 2023(4): 49-51.
	ZHANG F M, LU M, HAO J S. Analysis and research on the influence of engine condition on low cycle fatigue of cylinder head[J]. Internal Combustion Engine & Parts, 2023(4): 49-51. (in Chinese)
[14]	SKIBICKI D, PEJKOWSKI U. Low-cycle multiaxial fatigue behaviour and fatigue life prediction for CuZn37 brass using the stress-strain models[J]. International Journal of Fatigue, 2017, 102: 18-36.
[15]	张发平, 张书畅, 武锴, 等. 基于代理模型进化的履带车辆动力学参数优化[J]. 兵工学报, 2023, 44(1): 27-39. doi: 10.12382/bgxb.2022.0266
	ZHANG F P, ZHANG S C, WU K, et al. Dynamics parameter optimization for tracked vehicle based on surrogate model evolution[J]. Acta Armamentarii, 2023, 44(1): 27-39. (in Chinese) doi: 10.12382/bgxb.2022.0266
[16]	徐杰, 鲁海燕, 赵金金, 等. 拉丁超立方抽样的自适应高斯小孔成像蝴蝶优化算法[J]. 计算机应用研究, 2022, 39(9): 2701-2708, 2751.
	XU J, LU H Y, ZHAO J J, et al. Self-adaptive Gaussian keyhole imaging butterfly optimization algorithm based on Latin hypercube sampling[J]. Application Research of Computers, 2022, 39(9): 2701-2708, 2751. (in Chinese)
[17]	YANG X F, CHENG X, LIU Z Q, et al. A novel active learning method for profust reliability analysis based on the Kriging model[J]. Engineering with Computers, 2021, 38: 3111-3124.
[18]	毛星波, 盛冬发, 李忠君, 等. 基于Kriging-SBO算法的拱桥拱轴线优化设计[J]. 中山大学学报(自然科学版), 2023, 62(4): 139-146.
	MAO X B, SHENG D F, LI Z J, et al. Arch bridge axis optimization design based on Kriging-SBO[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2023, 62(4): 139-146. (in Chinese)
[19]	张建侠, 马义中, 欧阳林寒, 等. 基于Kriging模型的多点加点准则和并行代理优化算法[J]. 系统工程理论与实践, 2020, 40(1): 251-261. doi: 10.12011/1000-6788-2018-0757-11
	ZHANG J X, MA Y Z, OUYANG L H, et al. A multi-points infill sampling criterion and parallel surrogate-based optimization algorithm based on Kriging model[J]. Systems Engineering-Theory & Practice, 2020, 40(1): 251-261. (in Chinese)
[20]	孙泽刚, 肖世德, 王德华, 等. 液压滑阀V型节流槽气穴流仿真分析及结构优化研究[J]. 兵工学报, 2015, 36(2): 345-354. doi: 10.3969/j.issn.1000-1093.2015.02.023
	SUN Z G, XIAO S D, WANG D H, et al. Cavitation flow simulation and structural optimization of hydraulic spool value V-throttle groove[J]. Acta Armamentarii, 2015, 36(2): 345-354. (in Chinese)
[21]	薛小锋, 王远卓, 路成. 基于改进Kriging模型的舰载机着舰下沉速度影响性分析研究[J]. 西北工业大学学报, 2019, 37(2): 218-224.
	XUE X F, WANG Y Z, LU C. Sinking velocity compact-analysis of carrier-based aircraft based on improved Kriging model[J]. Journal of Northwestern Polytechnical University, 2019, 37(2): 218-224. (in Chinese)
[22]	CUI D, WANG G Q, LU Y P, et al. Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model[J]. Reliability Engineering & System Safety, 2020, 203: 107074.
[23]	WANG Y Q, LIU Y, MA X Y. Updated Kriging-assisted shape optimization of a gravity dam[J]. Water, 2021, 13(1): 87.
[24]	许耀峰, 王军, 刘朋科, 等. 不同持续射击发数对火炮身管寿命的影响[J]. 兵工学报, 2023, 44(4): 1034-1040.
	XU Y F, WANG J, LIU P K, et al. Influence of different numbers of rounds under continuous firing on gun barrel life[J]. Acta Armamentarii, 2023, 44(4): 1034-1040. (in Chinese) doi: 10.12382/bgxb.2021.0852
[25]	黄进峰, 张津, 陈俊宇, 等. 火炮身管失效机理与炮钢的发展[J]. 火炮发射与控制学报, 2023, 44(1): 10-18, 29.
	HUANG J F, ZHANG J, CHEN J Y, et al. Failure Mechanisms of gun barrels and the development of gun steel[J]. Journal of Gun Launch & Control, 2023, 44(1): 10-18, 29. (in Chinese)