The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process

Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE; Aperribay Zubia, Javier; CUESTA ZABALAJAUREGUI, MIKEL

The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process

Detalhes bibliográficos
Autores:	Duo, Aitor, Basagoiti, Rosa, ARRAZOLA, PEDRO JOSE, Aperribay Zubia, Javier, CUESTA ZABALAJAUREGUI, MIKEL
Formato:	artículo
Fecha de publicación:	2019
País:	España
Recursos:	Mondragon Unibertsitatea (UMON)
Repositorio:	eBiltegia. Repositorio Institucional de Mondragon Unibertsitatea
Idioma:	inglés
OAI Identifier:	oai:ebiltegia.mondragon.edu:20.500.11984/1476
Acesso em linha:	http://hdl.handle.net/20.500.11984/1476
Access Level:	acceso abierto
Palavra-chave:	tool wear Drilling Machine learning Tool condition monitoring

Descrição
Descrição não disponível.

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The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process

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