###### Abstract  
This study aimed to develop an artificial neural network (ANN) model for predicting the yield strength of a weld metal composed of austenitic stainless steel and compare its performance with that of conventional multiple regression and machine learning models. The input parameters included the chemical composition of the nine effective elements (C, Si, Mn, P, S, Ni, Cr, Mo, and Cu) and the heat input per unit length. The ANN model (comprising five nodes in one hidden layer), which was constructed and trained using 60 data points, yielded an R\({}^{2}\) value of 0.94 and a mean average percent error (MAPE) of 2.29%. During model verification, the ANN model exhibited superior prediction performance compared with the multiple regression and machine learning models, achieving an R\({}^{2}\) value of 0.8644 and a MAPE of 3.06%. Consequently, the ANN model effectively predicted the variation 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. Furthermore, the application of the prediction model was demonstrated in the design of welding consumables and heat input for 9% Ni steel.  
artificial neural network; yield strength; weld metal; austenitic stainless steel; dilution; microstructure +
Footnote †: journal:  
stainless steel, as well as 9% Ni steel and high-manganese steel, to reduce welding material costs, while also ensuring weldability [19; 20; 21; 22; 23; 24; 25].  
When austenitic welding consumables are used with various base metals, the chemical composition of the weld metal is affected by the degree of dilution between the base metal and the welding consumables [26; 27]. Consequently, there is a growing demand for predicting the mechanical properties of weld metals based on their chemical composition and applied welding heat input per unit length, which varies with the use of austenitic stainless steel-based welding consumables through dilution with various base metals.  
Recent research has led to the development of models to predict the yield strength of austenitic stainless steel-based weld metals using MRA and ML [28; 29]. Interestingly, the accuracy of the model developed using conventional MRA was slightly higher than that of the ML model. To overcome these limitations of ML, it is necessary to utilize ANNs, which are already reported to have a better prediction performance than MRA. While mechanical property prediction using ANNs for various steel-based metals [30; 31; 32; 33; 34; 35; 36] is being actively researched, ANNs have been applied in non-ferrous weld metals and special processes such as friction stir welding (FSW), but there has been comparatively less exploration in steel weld metals [37; 38; 39].  
Therefore, this study aimed to develop ANN models for predicting the yield strength of weld metals based on austenitic stainless steel and to evaluate their performance, particularly focusing on predicting the yield strength based on the chemical composition of the weld metal and the welding heat input per unit length. A total of 200 data points were used for the model training and verification. A dataset consisting of 160 data points with various chemical compositions and heat inputs was used to train the prediction models, while the additional 40 data points were used for model verification. To evaluate the yield strength prediction model using the ANN, its accuracy was compared and analyzed against prediction models developed using MRA and ML with the same dataset.