2026-04-28T12:56:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/38372024-05-13T07:27:34Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832

00925njm 22002777a 4500 dc Martín, Óscar author Pereda, María author Santos Martín, José Ignacio author Galán Ordax, José Manuel author 2014-11 Classification and regression tree (CART) and random forest techniques were proposed as pattern recognition tools for classification of ultrasonic oscillograms of resistance spot welding (RSW) joints. The results showed that CART models produced an acceptable error rate with high interpretability. These features may be used to understand and control the decision processes, instruct other human operators, compare margins of safety or modify them depending on the criticality of the industrial process. Compared with CART trees, random forests reduced the error rate at the cost of decreasing decision interpretability. The use of the agreement of the forest was proposed as a measure to reduce the workload of human operators, who would only have to focus on the analysis of ultrasonic oscillograms that are difficult to interpret 0924-0136 http://hdl.handle.net/10259/3837 10.1016/j.jmatprotec.2014.05.021 Resistance spot welding Non-destructive ultrasonic testing Random forest technique CART trees Classification Quality control Assessment of resistance spot welding quality based on ultrasonictesting and tree-based techniques