Fatigue Optimization of Sell Extractor Skateboard in a High-firing-speed Automatic Gun Based on Kriging Model

doi:10.12382/bgxb.2023.0722

Abstract

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

CLC Number:

TJ303⁺.9

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.

Figures/Tables 25

Fig.1 Structural diagram of shell extractor mechanism

Fig.2 Structural diagram of shell extractor skateboard

Fig.3 Finite element mesh model of collision location

Fig.4 Finite element mesh model of shell extractor mechanism

Fig.5 Bore pressure curve

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

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

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

Fig.7 Nephogram of stress at collision position

Fig.8 Stress-time curve of the fillet position

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

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

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

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

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

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

Fig.12 Distribution map of Latin hypercube sampling points

Fig.13 Initial surrogate model diagram

Fig.14 Flowchart of surrogate-based optimization algorithm

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

Fig.15 Updated surrogate model diagram

Fig.16 Trend diagram of fitness change

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

Fig.18 Live ammunition shooting experimental site

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

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