Fatigue Failure Analysis on Precracked 304 Stainless Steel Components Repaired by Laser with Addition of Nanocomposites

Wang, Jie; Jiang, Wei

doi:10.1590/0104-9224/SI24.08

Jie Wang

Dalian University of Technology, School of Mechanical Engineering, Dalian, P. R., China.

Wei Jiang

Dalian University of Technology, School of Mechanical Engineering, Dalian, P. R., China.

https://orcid.org/0000-0002-9108-1517

E-mail: jiangwei@dlut.edu.cn (JW)

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Figure 1
The dimensions of CT specimen.

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Figure 2
Experimental equipment and dimension of specimen. (a) Material test system: 1. MTS 810 material test system; 2. CT specimen; 3. measuring microscope; (b) Enlarged view of microcrack.

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Figure 3
Variation of correlation coefficient in one dimension search process. (a) Laser repair only; (b) Laser repair with addition of nanocomposite powder.

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Figure 4
Probabilistic S-N curves with C=50% at specified P levels, and the scatter points are test data. (a) C=99%, P=99%; (b) C=99%, P=50%.

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Figure 5
SEM microstructure of cross sections of repaired layer in the specimens and substrate (the scales are same). (a) laser repair with addition of nanocomposite powder; (b) laser repair only; (c) 304 stainless steel substrate; (d) The cross section of the specimen: the upper was repaired by laser only and the lower one repaired by laser with addition of powder.

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Table 1
Chemical composition of 304SS (wt.%).

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Table 2
Fatigue test results.

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Table 3
Parameter values of three parameters Weibull distribution.

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Table 4
Parameter values of S-N curves.

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Composition	C	Si	Mn	P	S	Cr	Ni	Mo	Fe
304SS substrate	0.033	0.480	1.324	0.037	0.005	19.500	7.370	0.313	Bal
304SS powder	0.08	0.50	1.46	0.03	0.005	19.00	9.50	0.5	Bal

No.	Load amplitude (N)		Cycles
1	3600	RLWNP	223969, 272367, 286143, 294206, 307681, 330031, 358528, 361605, 363052, 419484
1	3600	RLO	98309, 117816, 127877, 143843, 149979, 167946, 178772, 184088, 215410, 250784
2	4500	RLWNP	113933, 132204, 152820, 162765, 172525, 176492, 197026, 200861, 239210, 269679
2	4500	RLO	58768, 85103, 90700, 96341, 99392, 107441, 126596, 130719, 137663, 153250
3	5400	RLWNP	59335, 64832, 70956, 73904, 84484, 90970, 97711, 100205, 111347, 133404
3	5400	RLO	32531, 35297, 40778, 41048, 47673, 49168, 50185, 58135, 62814, 68785
4	6300	RLWNP	36746, 43815, 47142, 48258, 52443, 53945, 57767, 63661, 66476, 73942
4	6300	RLO	22866, 25708, 28985, 32867, 35352, 32399, 39904, 41394, 48547, 54545
5	7200	RLWNP	22523, 25463, 27484, 28363, 31908, 35000,36041, 38500, 43318, 49907
5	7200	RLO	14619, 16912, 17989, 20221, 22707, 25252, 28908, 30398, 31503, 38601

Load(N)		3600	4500	5400	6300	7200
$μ$	RLWNP	136396	86816	48357	28551	19099
$μ$	RLO	73240	22978	25862	18750	11183
$σ$	RLWNP	207253	109030	46197	29642	16924
$σ$	RLO	103537	96716	26168	20106	15526
$m$	RLWNP	3.2842	1.9667	1.7433	2.2094	1.6725
$m$	RLO	1.9175	2.9135	1.8581	1.6792	1.6957
$r$	RLWNP	0.9868	0.9918	0.9950	0.9955	0.9962
$r$	RLO	0.9964	0.9876	0.9914	0.9956	0.9936

Confidence C (%)	Probability P	Confidence limit	$α_{P - C}$		$β_{P - C}$ (lg)		$S_{0, P - C}$
Confidence C (%)	Probability P	Confidence limit	RLWNP	RLO	RLWNP	RLO	RLWNP	RLO
90	0.5	Upper limit	3.30	2.85	17.2565	15.3487	0	0
	0.5	Lower limit	3.37	2.87	17.5348	15.4443	0	0
	0.95	Upper limit	3.13	2.75	16.7877	15.1788	0	0
	0.95	Lower limit	3.20	2.75	17.0753	15.2328	0	0
	0.99	Upper limit	3.05	2.70	16.5513	15.0576	0	0
	0.99	Lower limit	3.12	2.70	16.8408	15.1013	0	0
95	0.5	Upper limit	3.29	2.85	17.2122	15.3327	0	0
	0.5	Lower limit	3.38	2.87	17.5828	15.4598	0	0
	0.95	Upper limit	3.12	2.75	16.7417	15.1696	0	0
	0.95	Lower limit	3.21	2.75	17.1245	15.2415	0	0
	0.99	Upper limit	3.04	2.70	16.5049	15.0501	0	0
	0.99	Lower limit	3.13	2.70	16.8902	15.1083	0	0
99	0.5	Upper limit	3.27	2.84	17.1151	15.2966	0	0
	0.5	Lower limit	3.41	2.88	17.6921	15.4945	0	0
	0.95	Upper limit	3.09	2.75	16.6402	15.1487	0	0
	0.95	Lower limit	3.24	2.76	17.2363	15.2608	0	0
	0.99	Upper limit	3.01	2.70	16.4025	15.0332	0	0
	0.99	Lower limit	3.16	2.70	17.0025	15.1238	0	0

B = \frac{\sum_{j = 1}^{n_{i}} x_{j} y_{j} - \frac{1}{n} (\sum_{i = 1}^{n_{i}} x_{j}) (\sum_{j = 1}^{n_{i}} y_{j})}{\sum_{j = 1}^{n_{i}} x_{j}^{2} - \frac{1}{n} {(\sum_{j = 1}^{n_{i}} x_{j}^{})}^{2}}

r = \frac{\sum_{j = 1}^{n} (P_{j} P_{j}^{'} - n \bar{P} {\bar{P}}^{'})}{\sqrt{(\sum_{j = 1}^{n} P_{j}^{2} - n {\bar{P}}^{2}) (\sum_{j = 1}^{n} P_{j} {^{'}}^{2} - n {\bar{P}}^{'}^{2})}}

[1] E-mail: jiangwei@dlut.edu.cn (JW)

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Fatigue Failure Analysis on Precracked 304 Stainless Steel Components Repaired by Laser with Addition of Nanocomposites

Abstract