## 5 Conclusions  
In this study, an artificial neural network (ANN)-based model was developed to predict the yield strength of a weld metal using austenitic stainless steel-based welding materials. The following conclusions were drawn:  
1. Among the training models developed using multiple regression analysis (MRA), machine learning (ML), and an ANN with 160 datasets, the ANN (\(10\times 5\times 1\)) model with five nodes in one hidden layer emerged as the best prediction model, exhibiting the highest R\({}^{2}\) value (0.94) and the lowest MAPE (2.29%). 2. In the model verification, the ANN (\(10\times 5\times 1\)) model also demonstrated a superior prediction performance compared with the MRA and ML models, achieving the highest R\({}^{2}\) value, i.e., 0.8644, and the lowest MAPE, i.e., 3.06%. These results confirm that ANN models minimize overfitting and enhance prediction performance compared to the MRA and ML models. 3. Despite simultaneously considering changes in the number of nodes and adding two or more hidden layers, the prediction accuracy of the validation model tends to decrease because of overfitting during the training process. The yield strength prediction model for weld metals based on austenitic stainless steel confirmed that the artificial neural network model with a shallow neural network structure of \(10\times 5\times 1\) was the most effective. The ANN model developed in this study can effectively forecast variations in the yield strength and microstructure resulting from the thermal history and dilution during the welding of 3.5-9% Ni steels with stainless steel-based welding consumables. The prediction model optimized the chemical composition of austenitic stainless steel-based welding consumables in welding 9% Ni steel. Hence, this study lays the groundwork for the development of a prediction model leveraging not only machine learning (ML) but also the multiple regression analysis (MRA) for predicting the yield strength of stainless-steel welds using artificial intelligence, such as artificial neural networks (ANNs). Unlike MRA, which struggles with non-linear problems as the dataset grows, ANN excels in solving such complexities, thus enabling the creation of more accurate prediction models. With the acquisition of reliable and comprehensive data across various materials, this approach holds promise for predicting the physical properties of diverse welds. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dilution (\%)**} & \multicolumn{8}{c}{**Predicted Yield Strength (MPa)**} & \multicolumn{8}{c}{**Remark**} \\ \cline{2-11}  & \multicolumn{2}{c}{**Fully**} & \multicolumn{2}{c}{**Modified Case 1**} & \multicolumn{2}{c}{**Modified Case 2**} & \multicolumn{2}{c}{**Modified Case 3**} & \multicolumn{2}{c}{**Welding**} \\  & \multicolumn{2}{c}{**Austenitic**} & & & & & & & & **Consumable** \\ \cline{2-11}  & **30** & **30** & **40** & **50** & **30** & **40** & **50** & **30** & **40** & **50** & **Heat Input** (kJ/cm)** \\ \hline
0 & 407 & 490 & 456 & 415 & 569 & 528 & 494 & 625 & 587 & 554 & \\
5 & 399 & 487 & 445 & 404 & 554 & 521 & 479 & 609 & 577 & 536 & \\
10 & 390 & 476 & 434 & 393 & 539 & 506 & 464 & 592 & 560 & 518 & \\
15 & 380 & 465 & 423 & 382 & 524 & 491 & 449 & 574 & 542 & 500 & \\
20 & 371 & 453 & 412 & 371 & 517 & 475 & 433 & 565 & 524 & 482 & \\
25 & 362 & 442 & 401 & 361 & 502 & 460 & 418 & 547 & 505 & 463 & \\
30 & 352 & 431 & 390 & 350 & 487 & 445 & 403 & 529 & 487 & 445 & \\
35 & 342 & 420 & 379 & 340 & 471 & 430 & 388 & 511 & 469 & 427 & \\
40 & 331 & 409 & 368 & 329 & 456 & 414 & 374 & 492 & 450 & 409 & \\
45 & 321 & 390 & 358 & 319 & 441 & 400 & 359 & 474 & 432 & 391 & \\
50 & 312 & 373 & 342 & 309 & 421 & 385 & 345 & 456 & 414 & 374 & \\
55 & 304 & 362 & 331 & 295 & 404 & 366 & 331 & 432 & 396 & 356 & \\
60 & 296 & 351 & 313 & 285 & 382 & 351 & 313 & 414 & 374 & 339 & \\ \hline \hline \end{tabular}
\end{table}