2026-04-28T12:57:03Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/38372024-05-13T07:27:34Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832
Repositorio Institucional de la Universidad de Burgos
author
Martín, Óscar
author
Pereda, María
author
Santos Martín, José Ignacio
author
Galán Ordax, José Manuel
2015-09-08T09:37:52Z
2015-09-08T09:37:52Z
2014-11
0924-0136
http://hdl.handle.net/10259/3837
10.1016/j.jmatprotec.2014.05.021
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
eng
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
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/3837/1/Mart%c3%adn-JMPT_2014.pdf
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Martín-JMPT_2014.pdf.txt