Abstract
Developing a reliable monitoring system is essential to create an autonomous manufacturing industry for increased productivity. The tool life of a machine tool is a major key parameter in accessing process quality control for developing an automated system. Various machining parameters are known to have different effects on the tool life criterion. Thus, it is essential to estimate the correlation of these parameters on tool life. The aim of this research is geared at estimating the tool life criterion from the effects of machining parameters and monitors the high-speed end-milling process of H13 tool with coated carbide inserts using highly correlated AE features. Furthermore, it proposes a diagnostic scheme using a multi-sensor approach for categorising the state of the tool. This scheme uses feature components extracted via statistical means and wavelet transform to serve as inputs for a neural network. The results found that increased speed decreased tool life and feed rate possesses a negative correlation to wear.
Similar content being viewed by others
References
Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tools Manuf (39):105–122
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 26:693–710
Zhou J, Pang CK, Lewis FL, Zhong Z (2010) Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on Instrumentation and Measurement. doi:10.1109/TIM.2010.2050974
Jemielniak K, Otman O (1998) Tool failure detection based on analysis of acoustic emission signals. Journal of Materials Processing Technology 76(1–3):192–197
Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. International Journal of Machine Tools & Manufacture 48(10):1148–1160
Susic E, Grabec I (2000) Characterization of the grinding process by acoustic emission. International Journal of Machine Tools & Manufacture 40(2):225–238
Mathews PG, Shunmugam MS (1999) Condition monitoring in reaming through acoustic emission signals. Journal of Materials Processing Technology 86:81–86
Pontuale G, Farrelly FA, Petri A, Pitolli L (2003) A statistical analysis of acoustic emission signals for tool condition monitoring (TCM). Acoustics Research Letters Online-Arlo 4(1):13–18
Lee DE, Hwang I, Valente CMO, Oliveira JFG, Dornfeld DA (2006) Precision manufacturing process monitoring with acoustic emission. International Journal of Machine Tools & Manufacture 46(2):176–188
Dornfeld DA, Oliveira JFG, Lee D, Valente CMO (2003) Analysis of tool and workpiece interaction in diamond turning using graphical analysis of acoustic emission. Cirp Annals-Manufacturing Technology 52(1):479–482
Chen X, Li B (2007) Acoustic emission method for tool condition monitoring based on wavelet analysis. International Journal Of Advanced tool and Manufacture 33:968–976
Marinescu I, Axinte D (2009) A time–frequency acoustic emission-based monitoring technique to identify work piece surface malfunctions in milling with multiple teeth cutting simultaneously. Int J Mach Tools Manuf 53–65
Kunpeng Z, San WY, Soon HG (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. International Journal of Machine Tools & Manufacture 49:537–553
Omerhodzic I, Avdakovic S, Nuhanovic A, Dizdarevic K (2010) Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. International Journal of Biological and life Sciences 6(4)
Lee DTL, Yamamoto A (1994) Wavelet analysis: theory and applications. Hewlett Packard J 45(6)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Olufayo, O., Abou-El-Hossein, K. Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel. Int J Adv Manuf Technol 81, 39–51 (2015). https://doi.org/10.1007/s00170-015-7091-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-7091-5