Article: Stochastic tool wear assessment in milling difficult to machine alloys Journal: International Journal of Mechatronics and Manufacturing Systems (IJMMS) 2015 Vol.8 No.3/4 pp.134 - 159 Abstract: In the machining industry, maximising profit is intuitively a primary goal; therefore continuously increasing machining process uptime and consequently productivity and efficiency is crucial. Tool wear plays an important factor in both machining uptime and quality, and since tool failure is related to the surface quality and the dimensional accuracy of the end product, it is essential to quantify and predict this phenomenon with the best possible certainty. One of the most common ways of tool wear prediction is through the use of low cost spindle current sensing technology which is used to measure spindle power consumption in CNC machines and relate power increase to tool wear. In this work, two methods of stochastic filtering (i.e. Kalman and particle filter) were used in predicting tool flank wear in machining difficult-to-machine materials through spindle power consumption measurements. Results show a maximum of 15% average error in estimation, which indicates the good potential of using stochastic filtering techniques in estimating tool flank wear. In addition, the particle filter was used for online estimation of a spindle power model parameter with uniform and Gaussian mixture models as the initial probability density functions, and the evolution of this parameter to the true posterior density function over time was investigated. Inderscience Publishers - linking academia, business and industry through research

Title: Stochastic tool wear assessment in milling difficult to machine alloys

Authors: Farbod Akhavan Niaki; Durul Ulutan; Laine Mears

Addresses: International Center for Automotive Research, Clemson University, Greenville, SC, USA ' Mechanical Engineering Department, Bucknell University, Lewisburg, PA, USA ' International Center for Automotive Research, Clemson University, Greenville, SC, USA

Abstract: In the machining industry, maximising profit is intuitively a primary goal; therefore continuously increasing machining process uptime and consequently productivity and efficiency is crucial. Tool wear plays an important factor in both machining uptime and quality, and since tool failure is related to the surface quality and the dimensional accuracy of the end product, it is essential to quantify and predict this phenomenon with the best possible certainty. One of the most common ways of tool wear prediction is through the use of low cost spindle current sensing technology which is used to measure spindle power consumption in CNC machines and relate power increase to tool wear. In this work, two methods of stochastic filtering (i.e. Kalman and particle filter) were used in predicting tool flank wear in machining difficult-to-machine materials through spindle power consumption measurements. Results show a maximum of 15% average error in estimation, which indicates the good potential of using stochastic filtering techniques in estimating tool flank wear. In addition, the particle filter was used for online estimation of a spindle power model parameter with uniform and Gaussian mixture models as the initial probability density functions, and the evolution of this parameter to the true posterior density function over time was investigated.

Keywords: tool wear; Kalman filter; particle filter; milling; stochastic filtering; difficult to machine alloys; flank wear; spindle power modelling; energy consumption; CNC machining; wear monitoring.

DOI: 10.1504/IJMMS.2015.073090

International Journal of Mechatronics and Manufacturing Systems, 2015 Vol.8 No.3/4, pp.134 - 159

Received: 06 May 2015
Accepted: 29 Jul 2015
Published online: 16 Nov 2015 *

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Title: Stochastic tool wear assessment in milling difficult to machine alloys

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