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Page 6 of 28 Zhang et al. J Mater Inf 2024;4:34 https://dx.doi.org/10.20517/jmi.2024.64

Table 2. The technical parameters of pulsed TIG welding for each experimental group with the orthogonal experimental method
Group numbers I (A) V (mm/min) t (s) δ H (mm)
p
p
s
1 125 120 0.04 0.67 1.917
2 75 60 0.08 0.50 1.955
3 95 90 0.10 0.33 1.525
4 85 100 0.08 0.67 1.789
5 105 120 0.10 0.50 1.61
6 125 80 0.04 0.33 0.915
7 85 110 0.10 0.67 1.713
8 105 60 0.04 0.50 1.748
9 125 90 0.08 0.33 1.963
10 75 90 0.08 0.67 2.211
11 95 110 0.10 0.50 1.268
12 115 60 0.04 0.33 0.981
13 105 80 0.08 0.67 1.829
14 125 100 0.10 0.50 1.657
15 85 120 0.04 0.33 1.664
16 95 100 0.04 0.67 1.597
17 115 120 0.08 0.50 1.536
18 75 80 0.10 0.33 1.720
19 115 110 0.04 0.67 1.718
20 85 80 0.08 0.50 1.623
21 105 100 0.10 0.33 1.452
22 115 80 0.10 0.67 1.940
23 75 100 0.04 0.50 1.634
24 95 120 0.08 0.33 1.643
25 95 60 0.08 0.67 1.863
26 115 90 0.10 0.50 1.447
27 75 110 0.04 0.33 1.806
28 125 60 0.10 0.67 1.856
29 85 90 0.04 0.50 1.133
30 105 110 0.08 0.33 1.803
31 75 120 0.10 0.67 1.683
32 95 80 0.04 0.50 1.943
33 115 100 0.08 0.33 1.616
34 105 90 0.04 0.67 1.543
35 125 110 0.08 0.50 1.479
36 85 60 0.10 0.33 1.640
TIG: Tungsten inert gas.

thereby enabling the network to learn incremental refinements through the innovative residual structure .
[68]
Furthermore, the architecture substantially accelerates training and enhances convergence rates, particularly
in the context of large-scale datasets. ResNet-34, a variant of the ResNet architecture featuring 34 layers,
adeptly balances depth and computational efficiency, rendering it exceptionally effective for addressing
complex challenges in the welding domain .
[69]
MobileNetV2 architecture
MobileNetV2 is a streamlined neural network architecture optimized for efficient image classification on
mobile devices and embedded systems [70,71] . By employing depthwise separable convolutions, the