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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (1): 40-47    DOI: 10.3785/j.issn.1008-973X.2020.01.005
Mechanical Engineering     
Surface roughness prediction of 6061 aluminum alloy based on GA-WPT-ELM
Fang-fang TAN1(),Jun-jiang ZHU1,Tian-hong YAN1,*(),Zhi-qiang GAO2,Ling-song HE2
1. College of Mechanical and Electronic Engineering, China Jiliang University, Hangzhou 310018, China
2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract  

The problem of multi-parameter simultaneous optimization and large error of empirical tuning should be solved in the vibration signal feature recognition and surface roughness prediction processes in order to improve the on-line prediction accuracy of workpiece surface roughness. The optimization method of signal feature recognition and surface roughness prediction modeling was proposed based on genetic algorithm (GA). GA was used to choose mother wavelet and feature quantities when signal features of milling 6061 aluminum alloy was recognized based on wavelet packet transform (WPT). The number of neurons in hidden layer was selected by GA when signal features and surface roughness were used to train extreme learning machine (ELM). The prediction modeling which has been trained was tested. The experimental results show that the three types of parameters for signal recognition and surface roughness prediction were simultaneously optimized by GA. GA-WPT-ELM not only obtains the best features of signal and the higher prediction accuracy of surface roughness, but also reduces analytical-computational cost in the modeling processes.

Key words： on-line vibration signal      genetic algorithm (GA)      wavelet packet transform      extreme learning machine (ELM)      surface roughness predicting     
Received: 03 December 2018      Published: 05 January 2020
CLC:  TP 18  
  TH 113  
Corresponding Authors: Tian-hong YAN     E-mail: tff097443@yeah.net;thyan@163.com
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Fang-fang TAN
Jun-jiang ZHU
Tian-hong YAN
Zhi-qiang GAO
Ling-song HE
Cite this article:

Fang-fang TAN,Jun-jiang ZHU,Tian-hong YAN,Zhi-qiang GAO,Ling-song HE. Surface roughness prediction of 6061 aluminum alloy based on GA-WPT-ELM. Journal of ZheJiang University (Engineering Science), 2020, 54(1): 40-47.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.01.005     OR     http://www.zjujournals.com/eng/Y2020/V54/I1/40


基于GA-WPT-ELM的6061铝合金表面粗糙度预测

为了提高工件表面粗糙度预测的准确性，针对振动信号特征识别和表面粗糙度预测建模时多个参数难以同步优化和人工经验调优误差较大的问题，提出基于遗传算法（GA）的信号特征识别和表面粗糙度预测的优化算法. 对采集的6061铝合金铣削振动信号进行小波包变换（WPT）和多个特征提取，利用GA优化WPT母小波和特征向量；将信号特征向量和表面粗糙度分别作为极限学习机（ELM）的输入和输出，对预测模型训练的同时，利用GA优化ELM隐含层的神经元个数；对训练好的预测模型进行测试. 实验结果表明，通过GA对振动信号识别和表面粗糙度预测的3类参数同步优化，获得了最佳的信号特征和较高的表面粗糙度预测精度，节省了建模分析计算成本.


关键词： 在线振动信号,  遗传算法（GA）,  小波包变换,  极限学习机（ELM）,  表面粗糙度预测 
Fig.1 Structure diagram of WPT
Fig.2 Network structure diagram of extreme learning machine
Fig.3 Optimization method flowchart of feature extraction
Fig.4 Signal acquisition and analysis system of milling vibration
n/（103 r·min?1） f/（103 mm·min?1） d/mm
6 1 1.0
9 1.65 0.7
12 2.3 0.5
15 3 0.3
18 — —
Tab.1 Milling parameters
Fig.5 Spindle milling experiment and sensor measurement
类型 族系 Ψ
正交 Daubechies db2,db3,db4,db5,db6,db7,db8,db9,
db10,db11,db12,db13,db14
正交 Haar Haar
正交 Coiflets coif1,coif2,coif3,coif4,coif5
正交 Symmlets sym2,sym3,sym4,sym5,sym6,sym7, sym8
双正交 Biorthogonal bior1.3,bior1.5,bior2.2,bior2.4,bior2.6,
bior2.8,bior3.1,bior3.3,bior3.5,bior3.7,
bior3.9,bior4.4,bior5.5,bior6.8
Tab.2 Mother wavelets of wavelet packet decomposition
特征 v 特征 v
均值 $J_{_{Gj}}^{{\lambda ^i}}$ 偏态 $P_{_{Gj}}^{{\lambda ^i}}$
标准差 $B_{_{Gj}}^{{\lambda ^i}}$ 峰态 $F_{_{Gj}}^{{\lambda ^i}}$
峰峰值 ${\rm{FF}}_{_{Gj}}^{{\lambda ^i}}$ 能量熵 $H_{_{Gj}}^{{\lambda ^i}}$
Tab.3 Statistical feature extraction of vibration signals
Fig.6 Optimization results of mother wavelets
Fig.7 Scale function and wavelet function for mother wavelet bior5.5
振动信号组别 Ψ v l σ/%
Gx bior5.5 $H_{G_{x}}^{\rm{DAAAA}}$ 76 88.95
Gx db8 $H_{G_{x}}^{\rm{DAAA}}$ 62 83.46
Gx Haar $P_{G_{x}}^{\rm{DAD}}$ 49 80.53
Gx coif3 $F_{G_{x}}^{\rm{DADAA}}$ 96 80.22
Gy db14 $H_{G_{y}}^{\rm{ADA}}$ 86 83.82
Gy db5 $H_{G_{y}}^{\rm{ADAA}}$ 64 82.22
Gy bior1.5 $H_{G_{y}}^{\rm{AAADA}}$ 31 81.24
Gz db6 $H_{G_{z}}^{\rm{AADAD}}$ 26 80.69
Gz sym6 $H_{G_{z}}^{\rm{DADA}}$ 38 82.92
Tab.4 Training results of GA options algorithm
Fig.8 Optimization curve and individual selection of GA
Fig.9 Fitting curve of surface roughness
组序号 n/（103
r·min?1） 		f/（103
mm·min?1） 		d/
mm 		$ \hat R_{\rm{a}} $/μm Ra/μm Δe/
μm
1 18 1.65 0.3 0.122 8 0.137 1 0.014 4
2 12 1.65 0.3 0.146 3 0.176 9 0.030 7
3 15 2.3 0.3 0.173 0 0.132 4 ?0.040 6
4 6 1 1.0 0.187 5 0.206 5 0.019 0
5 12 2.3 1.0 0.216 5 0.242 2 0.025 7
6 15 3 1.0 0.262 5 0.242 2 ?0.020 3
7 9 2.3 0.5 0.350 5 0.419 8 0.069 3
8 12 3 0.3 0.356 3 0.367 6 0.011 4
9 9 2.3 0.7 0.396 8 0.347 3 ?0.049 5
10 6 1.65 0.5 0.506 5 0.496 5 ?0.010 0
11 9 3 1.0 0.624 0 0.629 7 0.005 7
12 9 3 0.5 0.694 3 0.700 2 0.005 9
13 6 2.3 1.0 0.772 3 0.770 0 ?0.002 2
14 6 2.3 0.5 0.849 3 0.837 5 ?0.011 8
15 6 3 0.5 0.967 3 0.977 9 0.010 6
Tab.5 Prediction results of surface roughness
比较对象 信号源 样本数 方法 v σ/%
本文研究 振动信号 960 GA-WPT $H_{G_{x}}^{\rm{DAAAA}}$ 88.95
文献[14] 振动信号 360 SSA $a_y^{{\lambda _i}}$ 85.4
文献[13] 振动信号 20 TDA ax, v,f,d 80.0
文献[15] 切削力信号 360 G-WPT $X_{{F_X}}^{\rm{AAAA}}$ 88.1
Tab.6 Comparison of surface roughness prediction results for different methods
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