Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.
Neural Networks Tool Condition Monitoring in Single-point Dressing Operations / D'Addona, DORIANA MARILENA; Matarazzo, Davide; de Aguiar, Paulo R.; Bianchi, Eduardo C.; Martins, Cesar H. R.. - 41:(2016), pp. 431-436. (Intervento presentato al convegno 48th CIRP International Conference on Manufacturing Systems, CIRP CMS 2015 tenutosi a Ischia, Naples, Italy nel 24-26june 2015) [10.1016/j.procir.2016.01.001].
Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
D'ADDONA, DORIANA MARILENA;MATARAZZO, DAVIDE;
2016
Abstract
Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
