Please use this identifier to cite or link to this item:
|Title:||Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes||Authors:||Ge, H.
Torabi, Amin J.
Er, Meng Joo
Gan, Oon Peen
Zhai, Lian yin
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Source:||Li, X., Er, M. J., Ge, H., Gan, O. P., Huang, S., Zhai, L. Y., Linn, S., & Torabi, A. J. (2012). Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes. IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp.2821-2826.||Abstract:||In metal cutting processes, tool condition monitoring (TCM) plays an important role in maintaining the quality of surface finishing. Monitoring of tool wear in order to prevent surface damage is one of the difficult tasks in the context of TCM. Through early detection, high quality surface finishing and near-zero loss for potential failures can be ensured. Real-time/online tool degradation detection by using machine learning is highly desired. The ability to predict the tool wear, which is related to the remaining useful life of a tool, will improve efficiency and optimize tool usage while ensuring the quality of the work piece produced. In this paper, examine two popular methods of machine learning, namely the Adaptive Network Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are used to estimate the tool wear and correlation models for tool wear estimation using ANFIS and SVM are estimated. A case study for six sets of ball nose cutters in a high speed milling machining process of Inconel 718 is carried out. Comparative studies of the two methods are carried out and experimental results analysed and discussed. In turns out that the accuracy of the ANFIS is generally better than the SVM whereas SVM is much faster than ANFIS in terms of speed.||URI:||https://hdl.handle.net/10356/101096
|DOI:||10.1109/IECON.2012.6389448||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
Updated on Sep 6, 2020
Updated on Jan 20, 2021
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.