Zerrendatu honen arabera: egilea "Fernandez de Barrena, Telmo"
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A novel machine learning‐based methodology for tool wear prediction using acoustic emission signals
Saez de Buruaga, Mikel; Badiola, Xabier; Vicente, Javier (MDPI, 2021)There is an increasing trend in the industry of knowing in real-time the condition of their assets. In particular, tool wear is a critical aspect, which requires real-time monitoring to reduce costs and scrap in machining ... -
A novel methodology for the characterization of cutting conditions in turning processes using Machine Learning models and Acoustic Emission Signals
Fernandez de Barrena, Telmo; Ferrando, Juan Luis; García Gangoiti, Ander; ARRAZOLA, PEDRO JOSE; Abete, J.M.; Herrero Villalibre, Diego (Springer Nature, 2021)In the last few years, the industry requires to know in real-time the condition of their assets. Acoustic Emission (AE) technique has been widely used to understand the real-time condition of manufacturing processes such ... -
Tool remaining useful life prediction using bidirectional recurrent neural networks (BRNN)
Badiola, Xabier; Saez de Buruaga, Mikel; Vicente, Javier (Springer, 2023)Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance ...
