2017 journal article
Particle learning in online tool wear diagnosis and prognosis
JOURNAL OF MANUFACTURING PROCESSES, 28, 457–463.
author keywords: Particle learning; Tool wear; Intelligent manufacturing; Remaining useful life (RUL)
TL;DR:
A probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process to avoid developing a complex closed form formulation for a specific tool wear model.
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UN Sustainable Development Goal Categories
12. Responsible Consumption and Production
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
Automated Tool condition monitoring is critical in intelligent manufacturing to improve both productivity and sustainability of manufacturing operations. Estimation of tool wear in real-time for critical machining operations can improve part quality and reduce scrap rates. This paper proposes a probabilistic method based on a Particle Learning (PL) approach by building a linear system transition function whose parameters are updated through online in-process observations of the machining process. By applying PL, the method helps to avoid developing a complex closed form formulation for a specific tool wear model. It increases the robustness of the algorithm and reduces the time complexity of computation. The application of the PL approach is tested using experiments performed on a milling machine. We have demonstrated one-step and two-step look ahead tool wear state prediction using online indirect measurements obtained from vibration signals. Additionally, the study also estimates remaining useful life (RUL) of the cutting tool inserts.