Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM

被引：24
作者：
Babu, Mulpur Sarat [1 ]
Rao, Thella Babu [1 ]
机构：
[1] Natl Inst Technol Andhra Pradesh, Dept Mech Engn, Tadepalligudem 534 101, Andhra Pradesh, India
来源：
MEASUREMENT | 2022年 / 199卷
关键词：
multiple sensor signals (MSS); Multiple sensor heterogeneous data; optimization grey model; tool health monitoring (THM); Three-dimensional grey level co-occurrence; matrix (3DGLCM); MACHINED SURFACE IMAGES; DATA FUSION APPROACH; VECTOR MACHINE; PREDICTION; WEAR; ALGORITHM; NETWORK;
D O I：
10.1016/j.measurement.2022.111501
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Cutting tool health monitoring (THM) is of great practical significance to estimate the tool life to enhance productivity, machining efficiency and reduce machine tool downtime. Therefore, the present study aimed to develop an online THM system using an optimization grey model (OGM (1, N)) and support vector machine (SVM) by integrating multiple sensor signals (MSS) in situ. The MSS was acquired using a vibration sensor and a complementary metal-oxidesemiconductor (CMOS) camera at optimum cutting parameters during Inconel718 milling. The valuable features of tool vibration are extracted in time, frequency, and time-frequency domains, and machined surface texture characteristics are extracted using a three-dimensional grey level co-occurrence matrix (3DGLCM). Finally, an OGM (1, N) and SVM models are employed to realise the preceding methodology, which predicts flank wear in real-time based on extracted features of multiple sensor heterogeneous data with an average error of 4.07% and 7.38% compared to the experimental results.
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页数：15
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