Journal
IEEE TRANSACTIONS ON RELIABILITY
Volume 67, Issue 3, Pages 1294-1303Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2018.2831256
Keywords
Data-driven model; hybrid prognostics; physics-based model; remaining useful life (RUL); Wiener process
Categories
Funding
- Natural Science Basic Research Plan in Shaanxi Province of China [2013JM7001]
- National Natural Science Foundation of China [50805122]
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Accurate remaining useful life prediction is meaningful for cutting tool usability evaluation. Over the years, experience-based models, data-driven models, and physics-based models have been used individually to predict cutting tool remaining useful lives. In order to improve prediction performances, different prognostics models can be combined to leverage their advantages. In this paper, a hybrid cutting tool remaining useful life prediction approach is proposed by combining a data-driven model and a physics-based model. By using force, vibration and acoustic emission signals, the data-driven model monitors cutting tool wear conditions based on empirical mode decomposition and back propagation neural network. On the basis of the Wiener process, the physics-based model builds a cutting tool condition degradation model to predict cutting tool remaining useful lives. Experimental study verifies the approach's effectiveness, accuracy, and robustness. Then, cutting tool remaining useful lives can be predicted more accurately during the machining process.
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