Volume 7, No. 5, September 2018

General Information

  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
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International Journal of Mechanical Engineering and Robotics Research
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Monitoring Tool Wear in Drilling Process Using Spindle Noise Features

Supakorn Charoenprasit 1, Nopparat Seemuang 1, and Tom Slatter 2
1. Department of Production Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
2. Department of Mechanical Engineering, University of Sheffield, Sheffield, UK

Abstract—The use of worn cutting tools has a detrimental effect on the surface finish of a workpiece, tool precision and internal machine stress. Worn tools also decrease productivity through unplanned stops, tool changes and increase the production of scrap material. This study investigates an inexpensive and non-intrusive method of inferring tool wear by measuring the audible sounds emitted during a drilling process. A microphone was used to record the machining operation sound of S50C steel, which was drilled using a computer numerical control (CNC) milling machine in wet conditions. The audio signature was examined using a spectrogram, and the extracted sound features of the rotating spindle motor in the frequency domain were used to correlate with tool wear. The results indicated that the frequency of spindle noise was unrelated to tool wear, but although the magnitude of spindle noise significantly increased in accordance with tool wear progression.

Index Terms—tool wear monitoring, wear monitoring, drilling, spindle noise

Cite: Supakorn Charoenprasit, Nopparat Seemuang, and Tom Slatter, "Monitoring Tool Wear in Drilling Process Using Spindle Noise Features," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 5, pp. 564-568, September 2018. DOI: 10.18178/ijmerr.7.5.564-568