Prediction of Resistance Spot Weld Quality of 780 MPa Grade Steel Using Adaptive Resonance Theory Artificial Neural Networks

Insung Hwang,Hyeonsang Yun,Dongcheol Kim,Munjin Kang,Young-Min Kim,Jinyoung Yoon

doi:10.3390/met8060453

Insung Hwang, Hyeonsang Yun + Show 4 more

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https://doi.org/10.3390/met8060453

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Abstract

In this study, the weld quality of 780 MPa grade dual phase (DP) steel with 1.0 mm thickness was predicted using adaptive resonance theory (ART) artificial neural networks. The welding voltage and current signals measured during resistance spot welding (RSW) were used as the input layer data, and the tensile shear strength, nugget size, and fracture shape of the weld were used as the output layer data. The learning was performed by the ART artificial neural networks using the input layer and output layer data, and the patterns of learning result were classified by the setting of vigilance parameter, ρ. When the vigilance parameter is 0.8, the best-predicted results were obtained for the tensile shear strength, nugget size, and fracture shape of welds.

Highlights

There has been an increasing demand for lightweight automobiles to improve fuel efficiency worldwide, and regulations for crashworthiness are being strengthened [1,2]
Between two layers of 780 MPa grade dual phase (DP) steel with a 1.0 mm thickness under the electrode force of 300 kgf, the welding current of 7 kA and welding time of 333 ms, respectively. These signals were used as the input layer data of the adaptive resonance theory (ART) artificial neural network
A model was developed that can predict the quality of resistance spot welding (RSW) using ART artificial neural networks, which is one of the several methods of artificial neural network

Summary

Introduction

There has been an increasing demand for lightweight automobiles to improve fuel efficiency worldwide, and regulations for crashworthiness are being strengthened [1,2]. In order to enhance the crashworthiness, the thickness of the material for the automobile’s body must be increased. As the thickness increases, the effect of weight reduction is reduced. Studies have been carried out to obtain the crashworthiness and the effect of weight reduction, simultaneously. The application of advanced high strength steel (AHSS, 590–780 MPa grade) or ultra-high strength steel (UHSS, 980 MPa grade or more) has been expanded for automobile body materials. To increase the strength of high strength steel (HSS), many alloying elements are added or a heat treatment process is performed during the cooling process [3,4,5,6]

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