[1]
Liao J, Ikeuchi K, Matsuda F. Nucl Eng Des 1998; 183: 9.
Google Scholar
[2]
Nakao Y, Oshige H, Noi S. Q. J. Jpn. Weld. Soc. 1985; 3–4: 773.
Google Scholar
[3]
Mizuno R, Brziak P, Lomozik M, Matsuda F. 30th MPA-Seminar in conjunction with the 9th German-Japanese Seminar Stuttgart, 2004, 7.1-7.9.
Google Scholar
[4]
Deng D, Murakawa H. Comput Mater Sci 2006; 37: 209.
Google Scholar
[5]
Yurioka N, Horii Y. Sci Technol Weld Joining 2006; 11: 255.
Google Scholar
[6]
Viswanathan R, Gandy DW, Findlan SJ. Temperbead Welding of P-Nos. 4 and 5 Materials. EPRI TR-111757, Final Report, December 1998.
Google Scholar
[7]
Matsuda F, Ikeuchi K, Liao J. Trans JWRI 1993; 22: 271.
Google Scholar
[8]
Matsuda F, Ikeuchi K, Liao J. Trans JWRI 1994; 23: 49.
Google Scholar
[9]
Liao J, Ikeuchi K, Matsuda F. Trans JWRI 1994; 23: 223.
Google Scholar
[10]
Vasudevan M, Venkadesan S, Sivaprasad PV, Mannan SL. J Nucl Mater 1994; 211: 251.
Google Scholar
[11]
YU L, Nishimoto K, ect. Q. J. Jpn. Weld. Soc. 2011; 29:107.
Google Scholar
[12]
Casasent D, Chen X. Neural Networks 2003; 16: 529. Fig. 1 Iron-carbon phase diagram showing the transition points relating to a weld HAZ (b) (a) Fig. 2 Schematic illustration of temper bead welding produced by consistent layer technique: (a) overlap of HAZ, (b) overlap section of multi-pass welding (a) Specimen for thermal cycle simulation test (b) specimen for Charpy impact test (c) (d) (a) Fig. 3 Shape of specimens to simulate welding thermal cycles and side-notch Charpy impact test (b) Fig. 4 Four types of thermal cycles to simulate the thermal cycles in temper bead welding produced by consistent layer technique: (a) 1-cycle, (b) 2-cycle, (c) 1-cycle+temper, (d) 2-cycle+temper. Fig. 5 Schematic illustration of the first layer of temper bead welding: (a) overlap of HAZ, (b) section distribution in HAZ Fig. 6 Radial basis function neural network model Fig. 7 Results of toughness prediction system of 1-cycle: (a) 3D figure, (b) 2D-Contour figure. Fig. 8 Results of toughness prediction system of 1-cycle+temper with a constant Tp1=1350°C: (a) 3D figure, (b) 2D-contour figure. Fig. 9 Results of toughness prediction system of 2-cycle when Tp1=1350 °C and CR1=3 °C/s: (a) 3D figure; (b) 2D-contour figure. Fig. 10 Results of toughness prediction system of 2-cycle+temper when Tp1=1350°C, CR1=100°C /s, Tp2=1000°C: (a) 3D figure, (b) 2D-contour figure. Fig. 11 Comparison between calculated toughness and measured toughness after arbitrary thermal cycles Table 1 Chemical composition of A533B low-alloy steel Material
Chemical composition (mass %)
C
Si
Mn
P
S
Ni
Cu
Cr
Mo
Al
Fe
A533B
0.12
0.26
1.43
0.006
0.002
0.53
0.02
0.01
0.51
0.038
Bal.
Table 2 Thermal cycle conditions for 4 types of simulation thermal cycle test in Fig. 2 Thermal cycle
1st
2nd
Temper cycle
Peak Temperature, Tp (℃)
400~1350
670~1000
400~650
Cooling rate, CR (℃/s)
3, 30,60,100
3, 30,60,100
3, 30,60,100
Table 3 Validity range of input parameters in 4 kinds of toughness prediction systems for neural network Parameters
Tp1(℃)
CR1(℃/s)
Tp2(℃)
CR2(℃/s)
TCTP
1-cycle
400~1350
3~100
2-cycle
1350
3~100
670~1000
3~100
1-cycle+temper
1350
3~100
13900~22000
2-cycle+temper
1350
3~100
670~1000
3~100
13900~22000
Table 4 Thermal cycle conditions of 4 types of arbitrary thermal cycle Parameters
Tp1 (℃)
CR1 (℃/s)
Tp2 (℃)
CR2 (℃/s)
Tempering cycle
Tte3 (℃)
Holding time (s)
1-cycle
1350
80
1200
70
600
90
1000
90
1300
50
2-cycle
1350
80
1000
80
1350
40
950
30
1350
70
850
50
1350
60
750
60
1350
90
700
70
1-cycle+temper
1350
50
600
10
1350
80
550
20
1350
90
500
30
1350
70
650
10
1350
60
500
30
2-cycle+temper
1350
80
1000
80
600
10
1350
40
950
30
550
20
1350
70
850
50
500
20
1350
60
750
60
650
30
1350
90
700
60
500
30
Google Scholar