Sponsor
This work was supported by the Technology Innovation Program (Grant No. 20022454 and 20010778), funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).
Published In
Applied Sciences
Document Type
Article
Publication Date
5-24-2024
Subjects
Welding -- Metal
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 R2 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 R2 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.
Rights
Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Locate the Document
DOI
10.3390/app14104224
Persistent Identifier
https://archives.pdx.edu/ds/psu/41843
Citation Details
Park, S., Kim, C., & Kang, N. (2024). Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds. Applied Sciences, 14(10), 4224.