Metal Fatigue Damage Assessment Based on Polarized Thermography

Fangbin Wang; Fan Sun; Darong Zhu; Tao Liu; Xue Wang; Feng Wang

doi:10.3788/AOS202040.1412002

Acta Optica Sinica, Volume. 40, Issue 14, 1412002(2020)

Metal Fatigue Damage Assessment Based on Polarized Thermography

Fangbin Wang^1,2,4、*, Fan Sun^1,2, Darong Zhu^1,2,4, Tao Liu^1,2,4, Xue Wang^1,2, and Feng Wang³

Author Affiliations

¹School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China

²Key Laboratory of Construction Machinery Fault Diagnosis and Early Warning Technology, Anhui Jianzhu University, Hefei, Anhui 230601, China

³Key Laboratory of Polarization Imaging Detection Technology in Anhui Province, Hefei, Anhui 230031, China

⁴Anhui Education Department Key Laboratory of Intelligent Manufacturing of Construction Machinery, Hefei, Anhui 230601, China

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Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Schematic diagram of polarized thermal imaging for fatigue damage

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Fig. 2. Fatigue testing machine (left) and infrared polarization camera (right)

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Fig. 3. Specimen parameters (unit: mm)

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Fig. 4. Surface morphology of metal specimen during fatigue

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$Evolution of roughness and correlation length during fatigue (before fracture). (a) Roughness; (b) correlation length$

Fig. 5. Evolution of roughness and correlation length during fatigue (before fracture). (a) Roughness; (b) correlation length

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Fig. 6. Polarized azimuth images and S₀ image of spontaneous emission after registration. (a) I(0°); (b) I(60°); (c) I(120°); (d) S₀

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Fig. 7. Stokes parameters and polarization images after analysis. (a) S₀ ; (b) S₁; (c) S₂ ; (d) D_OP; (e) A_OP

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Fig. 8. Infrared polarization feature extraction process

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Fig. 9. Feature changes of metal specimen during fatigue

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Fig. 10. Results of model training,validation,test and all datasets

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Table 1. Chemical composition of Q235
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Table 1. Chemical composition of Q235
C Si Mn Cr Co S P Fe
0.220 0.230 0.650 0.044 0.081 0.045 0.040 98.73

Table 2. Contribution rates of feature quantity variance and cumulative variance

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Table 2. Contribution rates of feature quantity variance and cumulative variance

Parameter	${\bar{S}}_{0}$	$S_{0}^{2}$	$\bar{P}$	P²	$\bar{θ}$	θ²	$\bar{I}$ (0°)	${\bar{I}}^{2}$ (0°)
w_i /%	32.50	27.29	11.23	7.37	5.30	4.54	4.23	3.01
ρ /%	32.50	59.79	71.02	78.39	83.69	88.23	92.46	95.47
Parameter	$\bar{I}$ (60°)	I²(60°)	$\bar{I}$ (120°)	I²(120°)	${\bar{S}}_{1}$	$S_{1}^{2}$	${\bar{S}}_{2}$	$S_{2}^{2}$
w_i /%	1.76	1.23	0.87	0.31	0.17	0.09	0.06	0.04
ρ /%	97.23	98.46	99.33	99.64	99.81	99.90	99.96	100

Table 3. Prediction results of fatigue damage for a tested piece

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Table 3. Prediction results of fatigue damage for a tested piece

Fatigue cycles	500	1000	1500	2000	2500	3000	3500	4000	4500
Predication cycles	644	765	1641	2107	2372	2904	4169	4654	4949
Error /%	28.8	23.5	9.3	5.3	5.0	3.2	19.1	16.3	9.9
Fatigue cycles	5000	5500	6000	6500	7000	7500	8000	8500	9000
Predication cycles	4284	5387	4801	5253	7073	8229	10023	8983	7372
Error /%	14.3	2.0	19.9	19.1	1.0	9.7	25.2	5.6	18.0
Fatigue cycles	9500	10000	10500	11000	11500	12000	12500	13000	13500
Predication cycles	8383	8348	7524	9030	10233	11229	10283	10520	10184
Error /%	11.7	16.5	28.3	17.9	11.1	6.4	17.7	19.0	24.5

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Fangbin Wang, Fan Sun, Darong Zhu, Tao Liu, Xue Wang, Feng Wang. Metal Fatigue Damage Assessment Based on Polarized Thermography[J]. Acta Optica Sinica, 2020, 40(14): 1412002

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