Prediction of M-A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds

doi:10.1007/s40195-022-01495-3

Abstract

Abstract:

This study explored a strategy for predicting the proportion of martensite-austenite (M-A) constituents and impact toughness of stir zone (SZ) on X80 pipeline steel joints welded by friction stir welding (FSW). It is found that the welding forces, including the traverse force (F_x), the lateral force (F_y) and the plunge force (F_z), are the key variables related to the change of welding parameters and influence remarkably the characteristics of M-A constituents and impact toughness of SZ. The impact toughness of SZ is commonly lower than that of the base material due to the formation of lath bainite and coarsening of austenite. The characteristics of M-A constituents in SZ are sensitive to the variation of welding parameters and respond well to the change of welding forces. The proportion of small island M-A constituents increases with the decrease in rotational speed and the increase in F_z. The increase in the amount of island M-A constituents is beneficial to improve the impact toughness of SZ. Based on the above findings, a machine learning (ML) model for predicting the M-A constituents and impact toughness is constructed using the force features as the input data set. The force data-driven ML model can predict the M-A constituents and impact toughness precisely and exhibits higher accuracy than ML built with welding parameters. It is believed that the high accuracy is achieved because the force features include more details of FSW process, such as the heat generation, material flow, plastic deformation, and so on, which govern the microstructural evolution of SZ during FSW.

Key words: Friction stir welding, Pipeline steel, Welding force, M-A constituents, Impact toughness

Xueli Wang, Xin Ji, Bin He, Dongpo Wang, Chengning Li, Yongchang Liu, Wei Guan, Lei Cui. Prediction of M-A Constituents and Impact Toughness in Stir Zone of X80 Pipeline Steel Friction Stir Welds[J]. Acta Metallurgica Sinica (English Letters), 2023, 36(4): 573-585.

Figures/Tables 14

Table 1 Chemical composition of X80 (wt%)

C	Si	Mn	Cr	Ni	S	P	Nb	Fe
0.04	0.30	1.76	0.02	0.22	0.001	0.008	0.08	Bal

Fig. 1 FSW experiments: a welding force measuring system, b locations and size of metallographic and impact specimens

Table 2 Performance indicators of the force measuring system

	Load rating (kN)	Sensitivity (‰)	Repeatability error (‰)	Hysteresis error (‰)
F_x	40	1.0011	1.0	0.15
F_y	40	1.0014	1.0	0.15
F_z	60	0.9018	1.0	0.15

Table 3 Summary of conditions for input and output variables

	Eigenvalues	Abbreviation	Unit
Input 1	Rotation speed	n	r/min
	Welding speed	v	mm/min
Input 2	Average of F_x	F_x_ave	kN
	Amplitude of the 1st harmonic of F_x	A₁_Fx	kN
	Amplitude of the 2st harmonic of F_x	A₂_Fx	kN
	Amplitude of the 3st harmonic of F_x	A₃_Fx	kN
	Average of F_y	F_y_ave	kN
	Amplitude of the 1st harmonic of F_y	A₁_Fy	kN
	Amplitude of the 2st harmonic of F_y	A₂_Fy	kN
	Amplitude of the 3st harmonic of F_y	A₃_Fy	kN
	Average of F_z	F_z_ave	kN
	Amplitude of the 1st harmonic of F_z	A₁_Fz	kN
	Amplitude of the 2st harmonic of F_z	A₂_Fz	kN
	Amplitude of the 3st harmonic of F_z	A₃_Fz	kN
Output 1	Proportion of island M-A constituents	P_i	%
Output 2	Proportion of coarse slender M-A constituents	P_cs	%
Output 3	Impact toughness at − 40 °C	A_k	J/cm²

Fig. 2 Flow chart of machine learning model construction

Fig. 3 a-f Cross sections of the joints welded at the six different parameters. g-h Typical welding force for the joint welded at 700 r/min, 150 mm/min, i-k average of Fx, Fy, Fz for the joints welded at six welding parameters

Fig. 4 Microstructures of the joint welded with the parameter of 800 r/min and 100 mm/min: a BM b RS-HAZ, c RS-TZ, d SZ, e AS-TZ, f AS-HAZ

Fig. 5 SZ microstructures: a coarse prior austenite and lath bainite, b island M-A constituent, c blocky M-A constituent, d fine slender M-A constituent, e coarse slender M-A constituent. f-h Morphology, phase structure and stress distribution of M-A constituents

Fig. 6 Statistical results of the proportions for different kinds of M-A constituents observed in SZs of the welds: a 600 r/min 100 mm/min, b 700 r/min 100 mm/min, c 800 r/min 100 mm/min, d 600 r/min 150 mm/min, e 700 r/min 150 mm/min, f 800 r/min 150 mm/min

Fig. 7 Variations M-A constituents with welding parameters and welding force: a proportion of island M-A with welding parameters, b proportion of island M-A with Fz, c proportion of coarse slender M-A with welding parameters, d proportion of coarse slender M-A with Fz

Fig. 8 a Impact toughness of the three typical zones on the joint welded with 800 r/min and 100 mm/min. Variations of SZ impact toughness with welding parameters b and Fz c

Fig. 9 Correlations of SZ impact toughness with island M-A a, blocky M-A b, fine slender M-A c, and coarse slender M-A d

Fig. 10 Predicted results of the island M-A proportion with welding parameters as inputs a, the force features as inputs b, correlations between the inputs and the outputs c. Predicted results of the coarse slender M-A proportion d-f, impact toughness g-i

Table 4 Prediction performance of the ML models

	Input variables	R²	MSE	RMSE	MAE
P_i	Welding parameters	0.53	1.22	1.10	0.86
	Force features	0.89	0.31	0.55	0.32
P_cs	Welding parameters	0.51	0.48	0.69	0.52
	Force features	0.87	0.13	0.36	0.22
A_k	Welding parameters	0.46	46.86	6.85	6.04
	Force features	0.89	10.13	3.18	1.55

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