Tool life prediction for sustainable manufacturing

Prediction of tool wear is essential to maintaining the quality and integrity of machined parts and minimizing material waste, for sustainable manufacturing. Past research has investigated deterministic models such as the Taylor tool life model and its variations for tool wear prediction. Due to the inherent stochastic nature of tool wear and varying operating conditions, the accuracy of such deterministic methods has shown to be limited. This paper presents a stochastic approach to tool wear prediction, based on the particle filter. The technique integrates physics-based tool wear model with measured data to establish a framework, by iteratively updating the tool wear model with force and vibration data measured during the machining process, following the Bayesian updating scheme. Effectiveness of the developed method is demonstrated through tool wear experiments using a ball nose tungsten carbide cutter in a CNC milling machine.

[1]  Michael Pecht,et al.  Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance , 2012 .

[2]  J. Archard.  Contact and Rubbing of Flat Surfaces , 1953 .

[3]  Sohyung Cho,et al.  Design of multisensor fusion-based tool condition monitoring system in end milling , 2010 .

[4]  Krzysztof Jemielniak,et al.  Advanced monitoring of machining operations , 2010 .

[5]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[6]  Adam G. Rehorn,et al.  State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[7]  Andrew Y. C. Nee,et al.  Integrated Condition Monitoring and Fault Diagnosis for Modern Manufacturing Systems , 2000 .

[8]  Amiya R Mohanty,et al.  Estimation of tool wear during CNC milling using neural network-based sensor fusion , 2007 .

[9]  Amiya R Mohanty,et al.  Bayesian-inference-based neural networks for tool wear estimation , 2006 .

[10]  Krzysztof Jemielniak,et al.  Catastrophic Tool Failure Detection Based on Acoustic Emission Signal Analysis , 1998 .

[11]  S. K. Choudhury,et al.  Tool wear prediction in turning , 2004 .

[12]  M. J. Er,et al.  Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation , 2009 .

[13]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[14]  Colin Bradley,et al.  A review of machine vision sensors for tool condition monitoring , 1997 .

[15]  Jin Jiang,et al.  Erratum to: State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[16]  I. S. Jawahir,et al.  A comprehensive tool-wear/tool-life performance model in the evaluation of NDM (near dry machining) for sustainable manufacturing , 2008 .

[17]  Hasan Ozturk,et al.  Tool condition monitoring in milling using vibration analysis , 2007 .

[18]  Lihui Wang,et al.  Reconfigurable manufacturing systems: the state of the art , 2008 .

[19]  S. Li,et al.  Tool Condition Monitoring in Machining by Fuzzy Neural Networks , 1996 .

[20]  Rubén Morales-Menéndez,et al.  Tool-Wear Monitoring Based on Continuous Hidden Markov Models , 2005, CIARP.

[21]  Peter Nyhuis,et al.  Changeable Manufacturing - Classification, Design and Operation , 2007 .

[22]  Hans-Christian Möhring,et al.  Process monitoring with sensory machine tool components , 2010 .

[23]  Debasis Sengupta,et al.  Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques , 2007 .