Effect of Retained Austenite on the Corrosion Resistance of High-Strength Low-Carbon Steel in Artificial Seawater

doi:10.1007/s40195-024-01803-z

Abstract

Abstract:

Corrosion is an essential issue limiting the application of high-strength low-carbon steel in seawater environment. The impact of retained austenite on its corrosion behavior with immersion experiments and related corrosion sensor technology was explored. A model that clarifies the micro-galvanic effect and the heat-induced changes to the shape and composition of retained austenite was used to discuss the findings. The results indicated that retained austenite was generated following an intercritical process and demonstrated approximately 48 mV higher Volta potential than the matrix. The retained austenite content first increased and then decreased with increasing intercritical temperatures, while reaching the maximum value of 8.5% at 660 °C. With the increase in retained austenite content, the corrosion rate was increased by up to 32.8% compared to “quenching + tempering” (QT) specimen. The interfaces between the retained austenite and matrix were the priority nucleation sites for corrosion. Moreover, the retained austenite reduced the corrosion resistance of the steel by increasing the micro-galvanic effect and reducing rust layer compactness.

Key words: High-strength low-carbon steel, Intercritical heat treatment, Retained austenite, Corrosion resistance, Microgalvanic effect

Chao Hai, Yuetong Zhu, Cuiwei Du, Xiaogang Li. Effect of Retained Austenite on the Corrosion Resistance of High-Strength Low-Carbon Steel in Artificial Seawater[J]. Acta Metallurgica Sinica (English Letters), 2025, 38(4): 657-671.

Figures/Tables 17

Fig. 1 Schedule of different heat treatments: a QT, b QLT. WQ: water quenching; AC: air cooling

Table 1 Mechanical properties of four heat treatment specimens

Specimens	Yield strength (MPa)	Tensile strength (MPa)	Elongation (%)	Reduction of area (%)
QT	959	1028	14.4	42.9
QL₆₆₀T	870	950	16.0	45.7
QL₆₉₀T	935	1001	13.9	39.6
QL₇₂₀T	1017	1060	14.8	43.3

Fig. 2 XRD patterns of the tested steel

Fig. 3 Combined band contrast and grain boundary misorientation: a QT, b QL660T, c QL690T, d QL720T, e the distribution of effective grain size, f misorientation angle distribution

Fig. 4 Dynamic potential polarization curves a, Tafel fitting results b of four steels

Fig. 5 EIS curves of four specimens: a Nyquist, b bode plots

Table 2 Fitting results of EIS of four specimens

Steel	R_s (Ω·cm⁻²)	Q₁ × 10^-4 (Ω⁻¹·cm⁻²·sⁿ)	n₁	R_p (Ω·cm⁻²)	Q_dl × 10^-4 (Ω⁻¹·cm⁻²·sⁿ)	n₂	R_ct (Ω·cm⁻²)	C_eff (μF·cm⁻²)	d (nm)
QT	9.475	2.517	0.8169	30.53	1.358	0.7738	2015	65.1	0.21
QL₆₆₀T	8.596	2.225	0.8374	32.79	1.271	0.8371	1628	66.0	0.21
QL₆₉₀T	8.515	2.030	0.8572	25.5	1.407	0.8271	1994	70.3	0.20
QL₇₂₀T	8.833	2.172	0.8368	26.69	1.557	0.8093	2009	64.1	0.22

Fig. 6 Corrosion kinetics curves of four specimens in artificial seawater

Fig. 7 Phase composition of the rust layer in the four specimens after the immersion test: a QT, b QL660T, c QL690T, d QL720T

Fig. 8 Proportion of different constituting phases in the rust layer

Fig. 9 XPS spectra of the corrosion product layer on the surface of four specimens: a-d QT, QL660T, QL690T and QL720T. Among (1) core-level Fe 2p3/2 spectra; (2) core-level Ni 2p3/2 spectra; (3) core-level Mo 3d5/2 spectra

Fig. 10. 3D topography of four specimens after corrosion in artificial seawater for various periods. The rust layer was chemically removed

Fig. 11 AFM analysis of four specimens

Fig. 12 Polar diagram of the instantaneous current intensity over 15-day immersion time for four specimens: a QT; b QL660T; c QL690T; d QL720T

Fig. 13 Accumulated galvanic current curves of four type steels

Table 3 Microstructural factors of the four heat-treated specimens

Specimen	Corrosion rate (mm/y)	E_corr (mV)	I_corr (μA)	Corrosion sensor (C)	Grain size (μm)	HAGBs (%)	KAM (°)	RA%	∆E_couple (mV%)
QT	0.064	− 636	3.96	0.06	7	43.4	0.81	0	434
QL₆₆₀T	0.085	− 625	6.22	0.18	7.5	44.9	0.87	8.5	857
QL₆₉₀T	0.079	− 655	3.71	0.11	8.5	47.0	0.83	5.6	776
QL₇₂₀T	0.063	− 632	4.08	0.08	5	52.1	1.03	2.6	577

Fig. 14 a Importance sequence of input features to corrosion rate measured by random forest; b Person correlation map of microstructural features. The person correlation coefficients indicating extremely strong correlation (≥ 0.90) are indicated by red squares

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