Título: Quality Prediction of Resistance Spot Welding with Preheating in Imbalanced Dataset
Autores: Thiago Domingos de Araújo Lemos, Roberta A de A Fagundes
Resumo: Resistance Spot Welding (RSW) holds significant economic importance across various industries, particularly in the automotive sector, where it plays a crucial role in ensuring the safety and structural integrity of manufactured automobiles. However, when working with galvanized metal sheets, RSW faces unique challenges due to the presence of zinc oxide layer on the surface, which can reduce weld quality. Therefore, a commonly adopted solution is the preheating technique, which helps disrupt this oxide layer and improve weld formation.
Despite its widespread use, preheating is often overlooked in predictive quality prediction models. In response, this research addresses that gap by investigating the ability of machine learning techniques to classify welding process data between ‘good’ or ‘defective’ welds with information from preheating phase. To further improve classification performance—especially in the presence of imbalanced datasets—this study proposes an ensemble-based approach combined with characteristics extraction from physical features of the dynamic resistance curve. An ensemble approach was explored by integrating various base classifiers within a Balanced Bagging Classifier, combined through a Voting Classifier to leverage their complementary strengths. The performance of the ensemble model was rigorously evaluated using cross-validation technique to ensure robustness and generalizability.
The results revealed that the use of balanced bagging classifiers improved the results of classifying defective and good spot welding with respect to default bagging classifiers in the context of imbalaced data.
Palavras-chave: RSW; Imbalance; Preheating; Quality Prediction; Classifier.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1176467
Artigo em PDF: CBIC_2025_paper1176467.pdf
Arquivo BibTeX:
CBIC_2025_1176467.bib