Size Prediction of Directed Energy Deposited Cladding Tracks Based on Support Vector Regression

Fig. 4. Iterating process of searching optimal hyperparameters. (a) Iterating process of predicting the width; (b) iterating process of predicting the height

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Fig. 5. Comparison between actual and predicted values of cladding tracks size. (a) Actual values of width and predicted values of models; (b) actual values of height and predicted values of models

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Fig. 6. Effects of process parameters on cladding tracks size. (a) Effects of laser power on cladding tracks size; (b) effects of powder feeding rate on cladding tracks size; (c) effects of scanning speed on cladding tracks size

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Table 1. Quadratic regression general rotation design experiment schedule

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Table 1. Quadratic regression general rotation design experiment schedule

ExperimentNo.	P /W	v_p /(g·s^-1)	v_s /(mm·s^-1)	E /mm
1	425	0.17	7.5	9
2	425	0.17	7.5	11
3	425	0.17	10.5	9
4	425	0.17	10.5	11
5	425	0.27	7.5	9
6	425	0.27	7.5	11
7	425	0.27	10.5	9
8	425	0.27	10.5	11
9	675	0.17	7.5	9
10	675	0.17	7.5	11
11	675	0.17	10.5	9
12	675	0.17	10.5	11
13	675	0.27	7.5	9
14	675	0.27	7.5	11
15	675	0.27	10.5	9
16	675	0.27	10.5	11
17	300	0.22	9	10
18	800	0.22	9	10
19	550	0.12	9	10
20	550	0.32	9	10
21	550	0.22	6	10
22	550	0.22	12	10
23	550	0.22	9	8
24	550	0.22	9	12
25	550	0.22	9	10
26	550	0.22	9	10
27	550	0.22	9	10
28	550	0.22	9	10
29	550	0.22	9	10
30	550	0.22	9	10
31	550	0.22	9	10

Table 2. Analysis of the effectiveness of the process parameters
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Table 2. Analysis of the effectiveness of the process parameters
Removingparameter MRE ofpredictingwidth /% MRE ofpredictingheight /%
Laser power 12.45 14.36
Powder feeding rate 10.21 11.75
Scanning speed 10.97 9.28
Distance of nozzle 9.13 6.83
Without removing 8.51 6.06

Table 3. Selected optimal hyperparameters in different kernel function SVR models
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Table 3. Selected optimal hyperparameters in different kernel function SVR models
Model Predicting width of cladding tracks Predicting height of cladding tracks
C γ ε d C γ ε d
Polynomial-SVR 126 0.00561 0.1 3 217 0.00742 0.1 3
RBF-SVR 9 0.35 0.00122 - 9 0.25 0.0027 -
Sigmoid-SVR 16 0.03 0.1 - 16 0.05 0.1 -

Table 4. Experiment schedule of test set data
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Table 4. Experiment schedule of test set data
ExperimentNo. P /W v_p /(g·s^-1) v_s /(mm·s^-1) E /mm
1 600 0.22 9 10
2 400 0.14 10 11
3 500 0.18 10 11
4 600 0.24 8 10
5 700 0.16 12 10
6 700 0.26 6 10
7 400 0.16 10 11
8 700 0.16 7 10
9 600 0.20 9 11
10 650 0.26 8 10

Table 5. Error analysis of predicted results of different models%

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Table 5. Error analysis of predicted results of different models%

Model	Predicted width ofcladding tracks		Predicted height ofcladding tracks
Model	MRE	Maximum RE	MRE	Maximum RE
BP network	6.72	12.46	7.96	10.71
Polynomial-SVR	8.94	10.53	9.73	14.85
RBF-SVR	4.58	7.92	5.33	8.09
Sigmoid-SVR	6.68	8.78	8.34	10.16

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Yao Wang, Huang Yanlu, Yang Yongqiang. Size Prediction of Directed Energy Deposited Cladding Tracks Based on Support Vector Regression[J]. Chinese Journal of Lasers, 2020, 47(8): 802007

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