Abstract
A tool condition monitoring system can increase the competitiveness of a machining process by increasing the utilised tool life and decreasing instances of part damage from excessive tool wear or tool breakage. This article describes an inexpensive and non-intrusive method of inferring tool condition by measuring the audible sound emitted during machining. The audio signature recorded can be used to develop an effective in-process tool wear monitoring system where digital filters retain the signal associated with the cutting process but remove audio characteristics associated with the operation of the machine spindle. This study used a microphone to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The audio signal is compared to the flank wear development on the cutting inserts for several different surface speed and cutting feed combinations. The results show that there is no relationship between the frequency of spindle noise and tool wear, but varying cutting speed and feed rate have an influence on the magnitude of spindle noise and this could be used to indicate the tool wear state during the process.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7–8):693–710. doi:10.1007/s00170-004-2038-2
Astakhov VP (2004) The assessment of cutting tool wear. Int J Mach Tools Manuf 44(6):637–647. doi:10.1016/j.ijmachtools.2003.11.006
Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1–4):371–393. doi:10.1007/s00170-012-4177-1
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739. doi:10.1016/j.cirp.2010.05.010
Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2015) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol :1–13. doi:10.1007/s00170-015-7317-6
Worden K, Staszewski WJ, Hensman JJ (2011) Natural computing for mechanical systems research: a tutorial overview. Mech Syst Signal Process 25(1):4–111. doi:10.1016/j.ymssp.2010.07.013
Abellan-Nebot J, Romero Subirón F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257. doi:10.1007/s00170-009-2191-8
Raja JE, Kiong LC, Soong LW (2013) Hilbert-Huang transform-based emitted sound signal analysis for tool flank wear monitoring. Arab J Sci Eng 38(8):2219–2226. doi:10.1007/s13369-013-0580-7
Kopac J, Sali S (2001) Tool wear monitoring during the turning process. J Mater Process Technol 113(1–3):312–316. doi:10.1016/s0924-0136(01)00621-5
Mannan MA, Kassim AA, Jing M (2000) Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recogn Lett 21(11):969–979. doi:10.1016/S0167-8655(00)00050-7
Tekıner Z, Yeşılyurt S (2004) Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel. Mater Des 25(6):507–513. doi:10.1016/j.matdes.2003.12.011
Quintana G, Ciurana J, Ferrer I, Rodriguez CA (2009) Sound mapping for identification of stability lobe diagrams in milling processes. Int J Mach Tools Manuf 49(3–4):203–211. doi:10.1016/j.ijmachtools.2008.11.008
Lu MC, Kannatey-Asibu E (2002) Analysis of sound signal generation due to flank wear in turning. J Manuf Sci Eng Trans ASME 124(4):799–808. doi:10.1115/1.1511177
Alonso FJ, Salgado DR (2005) Application of singular spectrum analysis to tool wear detection using sound signals. Proc Inst Mech Eng B J Eng Manuf 219(9):703–710. doi:10.1243/095440505x32634
Raja JE, Lim W, Venkataseshaiah C (2013) Tool condition monitoring using competitive neural network and Hilbert-Huang transform. Asian J Sci Res 6(4):703–714
Downey J, O’Leary P, Raghavendra R (2014) Comparison and analysis of audible sound energy emissions during single point machining of HSTS with PVD TiCN cutter insert across full tool life. Wear 313(1–2):53–62. doi:10.1016/j.wear.2014.02.004
Tangjitsitcharoen S, Rungruang C, Pongsathornwiwat N (2011) Advanced monitoring of tool wear and cutting states in CNC turning process by utilizing sensor fusion. In: Jiang ZY, Li SQ, Zeng JM, Liao XP, Yang DG (eds) Manufacturing Process Technology, Pts 1–5, vol 189–193. Advanced Materials Research. Trans Tech Publications Ltd, Stafa-Zurich, pp 377–384. doi:10.4028/www.scientific.net/AMR.189-193.377
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Seemuang, N., McLeay, T. & Slatter, T. Using spindle noise to monitor tool wear in a turning process. Int J Adv Manuf Technol 86, 2781–2790 (2016). https://doi.org/10.1007/s00170-015-8303-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-8303-8