Multi-sensor data fusion framework for CNC machining monitoring

Reliable machining monitoring systems are essential for lowering production time and manufacturing costs. Existing expensive monitoring systems focus on prevention/detection of tool malfunctions and provide information for process optimisation by force measurement. An alternative and cost-effective approach is monitoring acoustic emissions (AEs) from machining operations by acting as a robust proxy. The limitations of AEs include high sensitivity to sensor position and cutting parameters. In this paper, a novel multi-sensor data fusion framework is proposed to enable identification of the best sensor locations for monitoring cutting operations, identifying sensors that provide the best signal, and derivation of signals with an enhanced periodic component. Our experimental results reveal that by utilising the framework, and using only three sensors, signal interpretation improves substantially and the monitoring system reliability is enhanced for a wide range of machining parameters. The framework provides a route to overcoming the major limitations of AE based monitoring.

[1]  Dragos Axinte,et al.  A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations , 2008 .

[2]  Steven Y. Liang,et al.  Machining Process Monitoring and Control: The State-of-the-Art , 2004 .

[3]  D. Leea,et al.  Precision manufacturing process monitoring with acoustic emission , 2005 .

[4]  J. Grum,et al.  Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding , 2004 .

[5]  D. Dvorak,et al.  Tool path strategies for high speed milling aluminum workpieces with thin webs , 1998 .

[6]  Tippa S. Reddy,et al.  On-Line Monitoring of Tool Wear and Surface Roughness by Acoustic emissions in CNC Turning , 2011, Int. J. Robotics Autom..

[7]  Ying Li,et al.  Calibration of piezoelectric AE sensor sensitivity for longtitudinal wave , 2010, Proceedings of the 2010 Symposium on Piezoelectricity, Acoustic Waves and Device Applications.

[8]  Vimal Dhokia,et al.  Energy efficient process planning for CNC machining , 2012 .

[9]  Yuan Zhejun,et al.  Tool wear monitoring with wavelet packet transform—fuzzy clustering method , 1998 .

[10]  I. Inasaki,et al.  A Study on Acoustic Emission from the Orthogonal Cutting Process , 1990 .

[11]  Yoke San Wong,et al.  An intelligent sensor system approach for reliable tool flank wear recognition , 1998 .

[12]  Yuanqing Xia,et al.  Multi-Sensor Data Fusion , 2014 .

[13]  George Chryssolouris,et al.  Investigation of acoustic sensing for laser machining processes Part 1: Laser drilling , 1994 .

[14]  Jose Vicente Abellan-Nebot,et al.  A review of machining monitoring systems based on artificial intelligence process models , 2010 .

[15]  Yalcin M. Ertekin,et al.  Identification of common sensory features for the control of CNC milling operations under varying cutting conditions , 2003 .

[16]  J Webster,et al.  Acoustic emission applied to detect workpiece burn during grinding , 1999 .

[17]  James A. Stori,et al.  A Bayesian network approach to root cause diagnosis of process variations , 2005 .

[18]  David Dornfeld,et al.  Tool Wear Detection Using Time Series Analysis of Acoustic Emission , 1989 .

[19]  Liya Lu,et al.  The use of process monitoring techniques on a CNC wood router. Part 1. sensor selection. , 2000 .

[20]  Cheng-Jen Lin,et al.  An intelligent sensor fusion system for tool monitoring on a machining centre , 1996 .

[21]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[22]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[23]  M. J. Maeng,et al.  Tool fracture detection in end milling using cutting force and acoustic emission propagated through cutting fluid , 1997 .

[24]  R J. Kuo,et al.  Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network , 1999, Neural Networks.

[25]  M. Guillot,et al.  On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion , 1997 .

[26]  Paolo Gaudenzi,et al.  Structural Health Monitoring for Future Space Vehicles , 2006 .

[27]  Rodolfo E. Haber,et al.  An investigation of tool-wear monitoring in a high-speed machining process , 2004 .

[28]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[29]  Xiaoli Li,et al.  A brief review: acoustic emission method for tool wear monitoring during turning , 2002 .

[30]  Amin Al-Habaibeh,et al.  A new approach for systematic design of condition monitoring systems for milling processes , 2000 .

[31]  E. Ezugwu.  Key improvements in the machining of difficult-to-cut aerospace superalloys , 2005 .

[32]  Dragos Axinte,et al.  A time–frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously , 2009 .

[33]  T. N. Nagabhushana,et al.  TOOL CONDITION MONITORING USING ACOUSTIC EMISSION, SURFACE ROUGHNESS AND GROWING CELL STRUCTURES NEURAL NETWORK , 2012 .

[34]  David Dornfeld,et al.  Monitoring of Ultraprecision Machining Processes , 2003 .

[35]  Peiqing Ye,et al.  A tool path generation strategy for sculptured surfaces machining , 2002 .

[36]  S. Smith,et al.  Use of Audio Signals for Chatter Detection and Control , 1992 .

[37]  Xiaoli Li,et al.  Discrete wavelet transform for tool breakage monitoring , 1999 .

[39]  Yuchen Zhou,et al.  Sensors for intelligent machining-a research and application survey , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

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

[41]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[42]  Ciro A. Rodríguez,et al.  Influence of tool path strategy on the cycle time of high-speed milling , 2003, Comput. Aided Des..

[43]  R. Krishnamurthy,et al.  In-process tool wear and chip-form monitoring in face milling operation using acoustic emission. , 1994 .

[44]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.