Tool Condition Monitoring in Microturning of Aluminium Alloy using Multiple Sensors

被引:1
|
作者
Gopikrishnan, A. [1 ]
Kanthababu, M. [1 ]
Balasubramaniam, R. [2 ]
Ranjan, Prabhat [2 ]
机构
[1] Anna Univ, Dept Mfg Engn, CEG, Madras 600025, Tamil Nadu, India
[2] BARC, Precis Engn Div, Bombay 400085, Maharashtra, India
关键词
Microturning; Acoustic Emission; Accelerometer; Cutting Force Dynamometer; Time Domain; Frequency Domain; Chip Morphology;
D O I
10.4028/www.scientific.net/AMM.592-594.796
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In the present work, an attempt has been made to monitor the tool condition status during microturning of aluminium alloy (AA 6061) using multiple sensors such as cutting force dynamometer, acoustic emission (AE) and accelerometer. The tool wear (nose wear) is correlated with surface roughness (R-a), chip width, thrust force (F-x), tangential force (F-y), feed force (F-z), AERMS and vibration signals. It is observed that R-a, chip width and cutting forces are increased with increase in the tool wear. Among the cutting forces, the tangential force (F-y) is found to be more sensitive to the tool wear status compared to that of the thrust force (F-x) and feed force (F-z). From the signal analysis, it is observed that during machining with good tool condition, the dominant frequency of the AERMS and vibration signals are found to be 81 kHz-110 kHz and 2.07 kHz-3.84 kHz respectively, whereas with the worn out tool the dominant frequencies are shifted to higher levels. Chip morphological studies indicated that favourable type of chips are formed upto 40 th minute and unfavourable chips are observed from 41 st minute to 60 th minute.
引用
收藏
页码:796 / +
页数:2
相关论文
共 50 条
  • [31] Parametric study of FSSWof aluminium alloy 5754 using a pinless tool
    Klobcar, D.
    Tusek, J.
    Smolej, A.
    Simoncic, S.
    WELDING IN THE WORLD, 2015, 59 (02) : 269 - 281
  • [32] Parametric study of FSSW of aluminium alloy 5754 using a pinless tool
    D. Klobčar
    J. Tušek
    A. Smolej
    S. Simončič
    Welding in the World, 2015, 59 : 269 - 281
  • [33] Artificial Intelligence techniques and Internet of things sensors for tool condition monitoring in milling: A review
    Ferrisi, Stefania
    Ambrogio, Giuseppina
    Guido, Rosita
    Umbrello, Domenico
    MATERIAL FORMING, ESAFORM 2024, 2024, 41 : 2000 - 2010
  • [34] Multiple regression analysis based approach for condition monitoring of industrial rotating machinery using multi-sensors
    Wang, Xiaofeng
    Lu, Guoliang
    Yan, Peng
    2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-QINGDAO), 2019,
  • [35] A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
    Qin, Bo
    Wang, Yongqing
    Liu, Kuo
    Jiang, Shaowei
    Luo, Qi
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 199
  • [36] Tool wear monitoring sensors
    Hale, K.H.
    Jones, B.E.
    International Congress on Condition Monitoring and Diagnostic Engineering Management - COMADEM, 1990,
  • [37] Lubricant as a condition monitoring tool
    Riley, Niall
    Mining Technology, 1994, 76 (872):
  • [38] Tool condition monitoring in broaching
    Axinte, DA
    Gindy, N
    WEAR, 2003, 254 (3-4) : 370 - 382
  • [39] CONDITION MONITORING AS A PRODUCTION TOOL
    HERON, R
    CME-CHARTERED MECHANICAL ENGINEER, 1985, 32 (12): : 21 - 23
  • [40] Wearable Stress Monitoring System Using Multiple Sensors
    Lebepe, F.
    Niezen, G.
    Hancke, G. P.
    Ramotsoela, T. D.
    2016 IEEE 14TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2016, : 895 - 898