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

吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (7): 1524-1533.doi: 10.13229/j.cnki.jdxbgxb20210108

• 车辆工程·机械工程 • 上一篇

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

陈昭明^1,²(),邹劲松³,王伟⁴,石明全³

^1.重庆大学机械工程学院，重庆 400044
^2.中国科学院大学重庆学院人工智能学院，重庆 400714
^3.中国科学院重庆绿色智能技术研究院智能制造技术研究所，重庆 400714
^4.重庆理工大学计算机科学与工程学院，重庆 400054

收稿日期:2021-01-06 出版日期:2022-07-01 发布日期:2022-08-08
作者简介:陈昭明（1985-），男，高级工程师，博士研究生.研究方向：产品数字化设计制造；智能制造系统与装备. E-mail：zhaomingc_sc@163.com
基金资助:
国家自然科学基金项目(61605205);国家重大科研仪器研制项目(51727812);重庆理工大学科研基金项目(0103191147)

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

Zhao-ming CHEN^1,²(),Jin-song ZOU³,Wei WANG⁴,Ming-quan SHI³

^1.College of Mechanical Engineering，Chongqing University，Chongqing 400044，China
^2.School of Artificial Intelligence，Chongqing School of University of Chinese Academy of Sciences，Chongqing 400714，China
^3.Intelligent Manufacturing Technology Institute，Chongqing Institute of Green and Intelligent Technology，Chinese Academy of Sciences，Chongqing 400714，China
^4.College of Computer Science and Engineering，Chongqing University of Technology，Chongqing 400054，China

Received:2021-01-06 Online:2022-07-01 Published:2022-08-08

摘要/Abstract

摘要：

针对铸锻双控成型过程中多工艺参数的优选问题，提出一种改进粒子群算法优化神经网络融合有限元分析的成型工艺参数优选方法。首先根据成型工艺的特点，以金属液浇注温度、模具预热温度、充型速度、铸锻压力、保压时间5个工艺参数为输入因素，以铸件重量、表面缺陷、抗拉强度3个参数为输出指标，采用正交拉丁超立方设计进行试验，并将所得工艺参数作为训练样本，通过神经网络构建影响因素与优化目标间的非线性函数关系。再以神经网络的输出误差值作为粒子适应度，并采用改进粒子群算法优化BP神经网络的权值和阈值，构建工艺参数预测模型进行多参数寻优。通过CAE有限元仿真验证表明，该方法能够准确地获得成型过程中的最佳工艺参数组合。研究结果可为铸锻双控过程的工艺参数调整与优化提供参考。

关键词: 改进粒子群算法, 铸锻双控成型, 正交拉丁超立方试验, 有限元仿真, 多目标优化

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

中图分类号:

TP183

陈昭明,邹劲松,王伟,石明全. 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[J]. 吉林大学学报(工学版), 2022, 52(7): 1524-1533.

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.

图/表 12

图1

图2

表1

正交拉丁超立方试验因素及取值范围"

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

表1

图3

图4

图5

表2

铸件材料参数属性表"

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

表2

表3

表4

QRO-90模具钢的热物性参数"

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

表4

表5

正交拉丁超立方设计样本及试验结果"

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

表5

图6

图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

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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

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

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