EFFECTS OF CUTTING PARAMETERS ON ACOUSTIC FREQUENCY CREATED IN MACHINING OF COLD WORK TOOL STEELS
Abstract
The objective of this study is to investigate the effects of machining parameters on the acoustic frequency developed during the machining of cold work tool steels. The machining tests were carried out through a milling method with different machining parameters. Analogue acoustic signals obtained via the microphone were converted and digitized. During idle operation and cutting operation, the obtained spindle sound recordings were subjected to Fast Fourier Transform (FFT) and converted from the time domain to the frequency domain, and the statistical effects of cutting parameters were investigated by use of analysis of variance. Cutting speed was found to be the only influential factor for the acoustic frequency in idle time. In the starting of machining, the feed rate, cutting speed, and depth of cut were seen to affect the acoustic frequency, while the cutting speed, insert material, insert radius, depth of cut, and feed rate were seen to be effective in the later stages of machining.
Keywords
References
- [1] Cook PR., Sound Production and Modeling, IEEE Computer Graphics and Application, 22, 23-27, (2002).
- [2] Li X., “A brief review: acoustic emission method for tool wears monitoring during turning”, Int. J. of Machine Tools & Manufacture, 42, 157–165, (2002).
- [3] Dimla E, Snr. Dimla, “Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods”, Int. J. of Machine Tools & Manufacture, 40, 1073–1098, (2000). [4] Liang S. and D. Dornfeld, “Tool wear detection using time series analysis of acoustic emission”, J. of Engineering for Industry Transactions of the ASME Series B, 111, 199–205, (1989). [5] Li X. and Z. Yuan, “Tool wear monitoring with wavelet packet transform-fuzzy clustering method”, Wear , 219, 145–154, (1998).
- [6] Tekiner Z and S. Yeşilyurt, “Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel”, Materials & Design, 25, 507–513 (2004).
- [7] Ravindra, H.V., Srinivasa Y.G., Krishnamurthy R., “Acoustic emission for tool condition monitoring in metal cutting”, Wear, 212, 78-84 (1997).
- [8] Srinivasa P., Nagabhushana T. N., Rao RBKN., “Tool condition monitoring using acoustic emission, surface roughness and growing cell structures neural network”, Machining Science and Technology, 16, 653–676 (2012).
- [9] Du R., “Signal understanding and tool condition monitoring”, Engineering Applications of Artificial Intelligence, 12, 585-597 (1999).
- [10] Mathew MT., Srinivasa P., Rocha LA., “An effective sensor for tool wear monitoring in face milling: Acoustic emission”, Sadhana, 33, 227–233, (2008).
Details
Primary Language
English
Subjects
-
Journal Section
Research Article
Authors
Hüseyin Polat
GAZI UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF COMPUTER ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING
0000-0003-4128-2625
Türkiye
Muammer Nalbant
GAZI UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF MANUFACTURING ENGINEERING, DEPARTMENT OF MANUFACTURING ENGINEERING
Türkiye
Hasan Basri Ulaş
GAZI UNIVERSITY, FACULTY OF TECHNOLOGY, DEPARTMENT OF MANUFACTURING ENGINEERING, DEPARTMENT OF MANUFACTURING ENGINEERING
Türkiye
Publication Date
June 29, 2018
Submission Date
October 4, 2017
Acceptance Date
May 24, 2018
Published in Issue
Year 2018 Volume: 5 Number: 2