改进粒子群神经网络融合有限元分析的铸锻双控动态成型多目标优化

doi:10.13229/j.cnki.jdxbgxb20210108

Abstract

Abstract:

To address the optimal selection problem of multi-objective parameters in casting-forging dual-control forming process， a novel method based on Back Propagation neural network optimized by Improved Particle Swarm Optimization （IPSO-BP） fusion with Finite Element Analysis （FEA） is proposed. Firstly， considering the characteristics of molding process， five processing parameters， including casting temperature of the molten metal， mold preheat temperature， mold filling velocity， casting-forging pressure and dwell time， are taken as input factors， and three parameters such as casting weight， surface defects， and tensile strength， are used as output indexes. The orthogonal latin hypercube design is adopted in order to obtain processing parameters for training samples， then the nonlinear functional relationship between the influencing factors and the optimization targets is constructed through the Back Propagation neural network （BP）. Secondly， the output error of neural network is utilized as the particle fitness， and the weight and threshold of BP are optimized by the improved particle swarm optimization algorithm， to establish the prediction model of processing parameters for multi-parameter optimization. The results from CAE finite element simulation show that this method can efficiently and accurately obtain the best combination of processing parameters in molding process， and provide guidance for adjusting and optimizing the processing parameters in casting-forging dual-control forming.

Key words: improved particle swarm algorithm, casting-forging dual-control forming, orthogonal latin hypercube experiment, FEM simulation, multi-objective optimization

CLC Number:

TP183

Zhao-ming CHEN,Jin-song ZOU,Wei WANG,Ming-quan SHI. Multi-objective optimization of casting-forging dynamic forming based on improved particle swarm neural network and finite element analysis[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1524-1533.

Figures/Tables 12

Fig.1

Fig.2

Table 1

Orthogonal latin hypercube test factors and its value"

试验因素	工艺参数名称	取值范围
试验因素	工艺参数名称	$X m i n$	$X m a x$
$X 1$	金属液浇注温度/℃	1450	1520
$X 2$	模具预热温度/℃	200	350
$X 3$	充型速度/（m·s^-1）	1	10
$X 4$	铸锻压力/MPa	10	30
$X 5$	保压时间/s	1	3

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Parameter property of casting material"

参数名称	取值	参数名称	取值
密度/（g?cm^-3）	7.2	弹性模量/GPa	113
抗拉强度/MPa	200	泊松比 $ν$	0.26
材料厚度/mm	2.0	线膨胀系数/［μm·m^-1?K^-1］	10.05
剪切模量/GPa	46.0	导热率/（W?m^-1?K^-1）	45

Table 2

Table 3

Table 4

Thermal physical parameters of QRO-90"

参数名称	取值	参数名称	取值
密度/（g?cm^-3）	7.7	热膨胀系数/10^-6 ℃	12.6
导热率/（W?m^-1?K^-1）	33.0	弹性模量/GPa	180
抗拉强度/MPa	1296	泊松比 $ν$	0.3

Table 4

Table 5

Orthogonal latin hypercube design samples and test results"

试验序号	试验因素					试验结果
试验序号	$X 1$ /℃	$X 2$ /℃	$X 3$ / （m?s^-1）	$X 4$ /MPa	$X 5$ /s	$Y 1$ /kg	$Y 2$ / ［cc?（100g）^-1］	$Y 3$ /MPa	统一性能F
1	1452.86	301.02	1.37	18.16	1.78	3.18	2.47	202.06	39.99
2	1454.29	209.18	5.78	15.71	1.53	2.96	4.29	200.17	37.44
3	1500.00	248.98	4.12	30.00	1.12	3.10	0.04	210.78	39.00
4	1511.43	307.14	9.63	20.61	1.69	2.81	4.29	203.88	35.69
5	1487.14	206.12	4.31	27.55	2.06	3.09	1.13	209.18	38.92
6	1508.57	313.27	8.71	23.06	2.88	2.85	3.94	206.01	36.17
7	1455.71	264.29	9.82	20.20	1.49	2.81	4.37	203.52	35.59
8	1490.00	230.61	7.06	23.88	3.00	2.95	3.29	206.66	37.34
9	1477.14	346.94	3.20	22.65	1.33	3.14	1.69	205.32	39.50
10	1468.57	310.20	7.43	28.37	2.84	2.94	2.21	209.99	37.20
11	1467.14	212.24	7.24	27.14	1.25	2.95	1.75	208.67	37.28
12	1482.86	273.47	5.59	19.39	1.94	3.04	3.50	203.03	38.38
…	…	…	…	…	…	…	…	…	…
46	1465.71	218.37	3.02	17.35	2.63	3.15	3.55	201.67	39.68
47	1487.14	255.10	8.90	29.59	2.06	2.84	1.99	210.71	36.00
48	1462.86	343.88	2.84	26.33	2.43	3.15	1.16	208.35	39.63
49	1460.00	279.59	3.94	29.18	1.61	3.12	0.41	210.29	39.25
50	1497.14	288.78	1.18	25.92	1.90	3.18	0.53	207.91	39.95

Table 5

Fig.6

Fig.7

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成分	质量分数/%	成分	质量分数/%
C	0.38	Mn	0.75
Si	0.30	Cr	2.60
V	0.90	Mo	2.25
P	0.01	S	0.009

Multi-objective optimization of casting-forging dynamic forming based on improved particle swarm neural network and finite element analysis

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