Detection of accelerated tool wear in turning

被引:24
作者
Bombinski, Sebastian [1 ]
Kossakowska, Joanna [2 ]
Jemielniak, Krzysztof [2 ]
机构
[1] Kazimierz Pulaski Univ Technol & Humanities Radom, Ul Stasieckiego 54, PL-26600 Radom, Poland
[2] Warsaw Univ Technol, Narbutta 86, PL-02524 Warsaw, Poland
关键词
Turning; Accelerated tool wear detection; Cutting force; Hierarchical time windows; ONLINE; FORCE; MODEL; PREDICTION; MACHINE; NETWORK; SIGNALS;
D O I
10.1016/j.ymssp.2021.108021
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
There are many algorithms for the identification of gradual tool wear (GTW) which usually counts in minutes and the detection of catastrophic tool failure (CTF) which counts in milliseconds. However, the tool may lose its cutting ability by accelerated tool wear (ATW), which may last several seconds and cannot be sensed either by GTW or CTF detection algorithms. The paper presents an innovative algorithm for early detection of ATW and CTF. It consists in comparing the waveforms of the cutting force sensor signal in hierarchical time windows. The compared waveforms are independent of the absolute value of the signal. This allows the detection of ATWs of different intensity and duration. Tests proved that successful detection of ATW allows for prevention of CTF.
引用
收藏
页数:16
相关论文
共 49 条
[1]   Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system [J].
Aliustaoglu, Cuneyt ;
Ertunc, H. Metin ;
Ocak, Hasan .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2009, 23 (02) :539-546
[2]   Current rise criterion: a process-independent method for tool-condition monitoring and prognostics [J].
Ammouri, A. H. ;
Hamade, R. F. .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 72 (1-4) :509-519
[3]   Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion [J].
Binsaeid, Sultan ;
Asfour, Shihab ;
Cho, Sohyung ;
Onar, Arzu .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2009, 209 (10) :4728-4738
[4]   Sensor signal segmentation for tool condition monitoring [J].
Bombinski, Sebastian ;
Blazejak, Krzysztof ;
Nejman, Miroslaw ;
Jemielniak, Krzysztof .
7TH HPC 2016 - CIRP CONFERENCE ON HIGH PERFORMANCE CUTTING, 2016, 46 :155-160
[5]   Study of using cutting chip color to the tool wear prediction [J].
Chen, Shao-Hsien ;
Luo, Zhi-Rong .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 109 (3-4) :823-839
[6]   Data fusion neural network for tool condition monitoring in CNC milling machining [J].
Chen, SL ;
Jen, YW .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2000, 40 (03) :381-400
[7]   Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys [J].
Chen, X. Q. ;
Li, H. Z. .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2009, 45 (7-8) :786-800
[8]   Fuzzy logic based tool condition monitoring for end-milling [J].
Cuka, Besmir ;
Kim, Dong-Won .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2017, 47 :22-36
[9]   On-line metal cutting tool condition monitoring. I: force and vibration analyses [J].
Dimla, DE ;
Lister, PM .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2000, 40 (05) :739-768
[10]   An unsupervised online monitoring method for tool wear using a sparse auto-encoder [J].
Dou, Jianming ;
Xu, Chuangwen ;
Jiao, Shengjie ;
Li, Baodong ;
Zhang, Jilin ;
Xu, Xinxin .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 106 (5-6) :2493-2507