Title: A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials
Authors: Farbod Akhavan Niaki; Lujia Feng; Durul Ulutan; Laine Mears
Addresses: International Center for Automotive Research, Clemson University, 4 Research Drive, Greenville, SC, 29607, USA ' International Center for Automotive Research, Clemson University, 4 Research Drive, Greenville, SC, 29607, USA ' Mechanical Engineering, Bucknell University, 701 Moore Ave, Lewisburg, PA 17837, USA ' International Center for Automotive Research, Clemson University, 4 Research Drive, Greenville, SC, 29607, USA
Abstract: In this work, wavelet packet decomposition along with principle component analysis are utilised for feature extraction using two low cost sensing methods: vibration and power sensors, in end-milling of gamma prime-strengthened alloy. The high wear rate of this material induces a rapid transition from a sharp state to a dull state of the tool, and hence limits the number of available data for model establishment. To address this challenge, a neural network with Bayesian regularisation is designed and its performance is compared with regression and time-series models. A maximum of 4% estimation error for Bayesian regularisation neural network, compared to 33% and 17% estimation error of the latter models, shows the good potential of this technique when a limited dataset is available. In addition, the use of low cost measuring sensors in this paper aligned well with the industrial applications to detect and avoid unscheduled downtime in machining situations.
Keywords: condition monitoring; tool wear; wavelet packet decomposition; WPD; recurrent neural networks; time series analysis; data-driven modelling; wear assessment; difficult to machine materials; principle component analysis; PCA; feature extraction; low cost sensing; vibration sensors; power sensors; end milling; gamma prime-strengthened alloys; unscheduled downtime.
International Journal of Mechatronics and Manufacturing Systems, 2016 Vol.9 No.2, pp.97 - 121
Received: 10 Jul 2015
Accepted: 07 Dec 2015
Published online: 26 Apr 2016 *