An exTS based Neuro-Fuzzy algorithm for prognostics and tool condition monitoring.

Olivier Massol (1) , Xiang Li (2) , Rafael Gouriveau (1) , Junhong Zhou (2) , Oon Peen Gan (2)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 SIMTech - Singapore Institute of Manufacturing Technology

The growing interest in predictive maintenance makes industrials and researchers turning themselves to artificial intelligence methods for fulfilling the tasks of condition monitoring and prognostics. Within this frame, the general purpose of this paper is to investigate the capabilities of an Evolving eXtended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed machining conditions. The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Multiple Regression Model (MRM) in term of accuracy and reliability through a case study of tool condition monitoring. The reliability of exTS also investigated.

Mots clés: tool condition monitoring; Tool wear estimation; evolving eXtended Takagi Sugeno Neuro-Fuzzy algorithm; prognostics; tool condition monitoring.

Domaines: Automatique / Robotique

Olivier Massol, Xiang Li, Rafael Gouriveau, Junhong Zhou, Oon Peen Gan. An exTS based Neuro-Fuzzy algorithm for prognostics and tool condition monitoring.. 11th International Conference on Control, Automation, Robotics and VIsion, ICARCV'10., Dec 2010, Singapour, Singapore. pp.1-6. ⟨hal-00546382⟩