A survey of methods for detecting metallic grinding burn

Abstract Grinding burn detection is essential for metallic components manufacturing because grinding burn not only reduces the functional performance and fatigue life but also affects the quality of precision grinding. Grinding burn occurs when the temperature of the workpiece in grinding zone rises above the tempering temperature of the material due to inappropriate grinding conditions, resulting in microstructural changes in the surface and may be accompanied with a reduction in strength, plasticity, and hardness, and may introduce unfavorable residual stresses. This work comprehensively introduces each of the existing metallic grinding burn detection methods and discusses their applications, advantages, and limitations. Moreover, the trends of developing methods for detecting metallic grinding burn are also presented.

[1]  R. Krishnamurthy,et al.  On-line detection of grinding burn by integrated sensing , 2001, SIcon/01. Sensors for Industry Conference. Proceedings of the First ISA/IEEE. Sensors for Industry Conference (Cat. No.01EX459).

[2]  Shih-Bin Wang,et al.  Selections of working conditions for creep feed grinding. Part(II): workpiece temperature and critical grinding energy for burning , 2006 .

[3]  Kauko Leiviskä,et al.  An Efficient Procedure for Identifying the Prediction Model Between Residual Stress and Barkhausen Noise , 2013 .

[4]  Paul S. Prevéy,et al.  X-RAY DIFFRACTION RESIDUAL STRESS TECHNIQUES , 1986 .

[5]  Zhensheng Yang,et al.  Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel , 2013 .

[6]  N. Cook,et al.  The Wear of Grinding Wheels: Part 1—Attritious Wear , 1971 .

[7]  Yi Zang,et al.  Experimental Investigation of Static Characteristics on Eddy Current Sensor for External Cylindrical Grinding Measurement System , 2011 .

[8]  Xun Chen,et al.  Detection of Grinding Temperatures Using Laser Irradiation and Acoustic Emission Sensing Technique , 2012 .

[9]  A. Sorsa,et al.  Utilization of Barkhausen noise magnetizing sweeps for case-depth detection from hardened steel , 2012 .

[10]  Qiang Liu,et al.  Investigation of acoustic emission signals under a simulative environment of grinding burn , 2006 .

[11]  R. Winholtz,et al.  X-Ray Stress Analysis , 2008 .

[12]  Eduardo Carlos Bianchi,et al.  In-process grinding monitoring by acoustic emission , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  S. Malkin,et al.  Thermal Analysis of Grinding , 2007 .

[14]  Zuo Dunwen.  Feasibility of Evaluation for Machining Burn on Work Surface Based on CCD Image , 2005 .

[15]  Junz Jiunn-jyh Wang,et al.  A stochastic grinding force model considering random grit distribution , 2008 .

[16]  S. Sampathkumar,et al.  Condition monitoring of grinding process through machine vision system , 2012, 2012 International Conference on Machine Vision and Image Processing (MVIP).

[17]  Jeffrey S. Ceurter,et al.  Barkhausen noise inspection , 2002 .

[18]  Wei Wei,et al.  Evolution of the microstructure, mechanical properties, and high-order modal characteristics of AISI 1045 steel subjected to a simulative environment of surface grinding burn , 2016 .

[19]  B. Sadeghi,et al.  Thermal analysis of minimum quantity lubrication-MQL grinding process , 2012 .

[20]  W. B. Rowe,et al.  A Simplified Approach to Control of Thermal Damage in Grinding , 1996 .

[21]  Xiangbin Zhuge,et al.  Eddy Current Testing , 1999 .

[22]  W. Brian Rowe,et al.  Thermal analysis of high efficiency deep grinding , 2001 .

[23]  Jae-Seob Kwak,et al.  Neural network approach for diagnosis of grinding operation by acoustic emission and power signals , 2004 .

[24]  Hédi Hamdi,et al.  Temperature measurement and heat flux characterization in grinding using thermography , 2008 .

[25]  J. H. Espina-Hernandez,et al.  Unwrapping the influence of multiple parameters on the Magnetic Barkhausen Noise signal using self-organizing maps , 2013 .

[26]  Ekkard Brinksmeier,et al.  Surface integrity in material removal processes: Recent advances , 2011 .

[27]  A. Shih,et al.  Grinding Temperature Measurements in Magnesia-Partially-Stabilized Zirconia Using Infrared Spectrometry , 2003 .

[28]  S. Santa-aho.  Barkhausen Noise Method for Hardened Steel Surface Characterization - The Effect of Heat Treatments, Thermal Damages and Stresses , 2012 .

[29]  J. Mcclure,et al.  The magnetic barkhausen effect , 1976 .

[30]  Kauko Leiviskä,et al.  Prediction of Residual Stresses Using Partial Least Squares Regression on Barkhausen Noise Signals , 2013 .

[31]  Zhong Bing-lin,et al.  A study of on-line identification for grinding burn , 1992 .

[32]  Jiu-hua Xu,et al.  Effects of undeformed chip thickness on grinding temperature and burn-out in high-efficiency deep grinding of Inconel718 superalloys , 2017 .

[33]  S. Chandrasekar,et al.  A Model for Grinding Burn , 1997 .

[34]  L. Vijayaraghavan,et al.  Assessment of grinding wheel conditioning process using machine vision , 2014, 2014 International Conference on Prognostics and Health Management.

[35]  Zhonghua Yu,et al.  Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process , 2014 .

[36]  Peter Willett,et al.  Neural network detection of grinding burn from acoustic emission , 2001 .

[37]  Bin Lin,et al.  Research on the analytical thermal model in surface grinding by cup wheel , 2013 .

[38]  M. Lenglet,et al.  SEM-EDS and XPS Studies of the High Temperature Oxidation Behaviour of Inconel 718 , 2000, Microchimica Acta.

[39]  Albert J. Shih,et al.  Thermocouple Fixation Method for Grinding Temperature Measurement , 2008 .

[40]  Hans Jürgen Maier,et al.  Applications of High Frequency Eddy Current Technology for Material Characterization of Thin Coatings , 2016 .

[41]  Vijayender Singh,et al.  A New Model for Grinding Force Prediction and Analysis , 2010 .

[42]  Soumitra Paul,et al.  Modelling of specific energy requirement during high-efficiency deep grinding , 2008 .

[43]  Robert Bauer,et al.  A review of cutting fluid application in the grinding process , 2005 .

[44]  D. Guo,et al.  Physics-Based Predictive Cutting Force Model in Ultrasonic-Vibration-Assisted Grinding for Titanium Drilling , 2009 .

[45]  Juan Chen,et al.  The effect of temperature on the average volume of Barkhausen jump on Q235 carbon steel , 2016 .

[46]  Javier García-Martín,et al.  Non-Destructive Techniques Based on Eddy Current Testing , 2011, Sensors.

[47]  Fan Jianchun.  Research Advance of MAE Nondestructive Testing Method , 2008 .

[48]  C. Liu,et al.  Experimental investigation on variation of machined residual stresses by turning and grinding of hardened AISI 1053 steel , 2014 .

[49]  P. Sutowski,et al.  Monitoring of cylindrical grinding processes by use of a non-contact AE system , 2012 .

[50]  T. Jin,et al.  Determination of burn thresholds of precision gears in form grinding based on complex thermal modelling and Barkhausen noise measurements , 2017 .

[51]  W. B. Rowe,et al.  Implementation of Thermal Models in Grinding , 2007 .

[52]  W. B. Rowe,et al.  Temperatures in High Efficiency Deep Grinding (HEDG) , 2001 .

[53]  B. Karpuschewski,et al.  Surface Integrity Inspection on Gears Using Barkhausen Noise Analysis , 2011 .

[54]  Gerd Dobmann,et al.  Micro-Magnetic Evaluation of Micro-Residual Stresses of the IInd and IIIrd Orders , 2012, Welding in the World.

[55]  Steven Y. Liang,et al.  Grinding force and power modeling based on chip thickness analysis , 2007 .

[56]  Gui Yun Tian,et al.  Analysis of domain wall dynamics based on skewness of magnetic Barkhausen noise for applied stress determination , 2017 .

[57]  P. V. Rao,et al.  An investigation on surface burn during grinding of Inconel 718 , 2016 .

[58]  Liangchi Zhang,et al.  Review on grinding-induced residual stresses in metallic materials , 2017 .

[59]  R. Deiva Nathan,et al.  In-process monitoring of grinding burn in the cylindrical grinding of steel , 1999 .

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

[61]  David Jiles,et al.  Dynamics of domain magnetization and the Barkhausen effect , 2000 .

[62]  A. Sorsa,et al.  Optimized laser processing of calibration blocks for grinding burn detection with Barkhausen noise , 2012 .

[63]  Jinyuan Tang,et al.  Modeling and experimental study of grinding forces in surface grinding , 2009 .

[64]  Andreas Nocke,et al.  Review on quality assurance along the CFRP value chain – Non-destructive testing of fabrics, preforms and CFRP by HF radio wave techniques , 2015 .

[65]  A. Sorsa,et al.  Case depth verification of hardened samples with Barkhausen noise sweeps , 2014 .

[66]  B. Höhn,et al.  Grinding Burn on Gears: Correlation Between Flank-Load-Carrying Capacity and Material Characteristics , 2013 .

[67]  G. Bertotti,et al.  Domain-wall dynamics and Barkhausen effect in metallic ferromagnetic materials. II, Experiments , 1990 .

[68]  F. Klocke,et al.  Influence of Grinding Burn on Pitting Capacity , 2008 .

[69]  E. Ušák,et al.  New parameters in adaptive testing of ferromagnetic materials utilizing magnetic Barkhausen noise , 2016 .

[70]  Hans Kurt Tönshoff,et al.  Process Monitoring in Grinding , 2002 .

[71]  Kauko Leiviskä,et al.  BARKHAUSEN: A study on laser-processed grinding burn simulation and analysis based on Barkhausen noise measurement , 2010 .

[72]  A. Sorsa,et al.  Development of Barkhausen noise calibration blocks for reliable grinding burn detection , 2012 .

[73]  N. Gindy,et al.  Signal Analysis of Acoustic Emission for Laser Imitating Grinding Burn , 2005 .

[74]  A. B. Kaiser,et al.  Magnetic Barkhausen noise analysis of stress in steel , 2004 .

[75]  V. Moorthy,et al.  Evaluation of tempering induced changes in the hardness profile of case-carburised EN36 steel using magnetic Barkhausen noise analysis , 2003 .

[76]  S. Malkin,et al.  Analysis of Energy Partition in Grinding , 1995 .

[77]  S. Malkin,et al.  Temperatures and Energy Partition for Grinding with Vitrified CBN Wheels , 1999 .

[78]  T Jin,et al.  Analysis of Grinding Chip Temperature and Energy Partitioning in High-Efficiency Deep Grinding , 2006 .

[79]  A. Torrance,et al.  Measurement of grinding temperatures using a foil/workpiece thermocouple , 2012 .

[80]  T. Azuma,et al.  Development of Non-Destructive Inspection System for Grinding Burn-in-Process Detection of Grinding Burn , 2014 .

[81]  Seung Han Yang,et al.  Surface Quality Studies with Respect to Grinding Burn of New Typical Nickel-Based Superalloy , 2003 .

[82]  E. Gorkunov,et al.  Magnetoelastic Acoustic Emission in Ferromagnetic Materials. II. Effect of Elastic and Plastic Strains on Parameters of Magnetoelastic Acoustic Emission , 2001 .

[83]  Yongdong Li,et al.  Experimental study and theoretical analysis of the form grinding of gears using new type micro-crystal corundum grinding wheels , 2017 .

[84]  Gui Yun Tian,et al.  An application of back propagation neural network for the steel stress detection based on Barkhausen noise theory , 2013 .

[85]  Brian A. Shaw,et al.  The effect of microstructure and applied stress on magnetic Barkhausen emission in induction hardened steel , 2007 .

[86]  A. Sorsa,et al.  Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement , 2012 .

[87]  Y. S. Tarng,et al.  Measuring wear of the grinding wheel using machine vision , 2006 .

[88]  Michael Kröning,et al.  Micro-magnetic evaluation of micro residual stresses of the IInd and IIIrd order , 2009 .

[89]  Michele Dassisti,et al.  Methods of measuring residual stresses in components , 2012 .

[90]  V. Moorthy,et al.  On the shape of the magnetic Barkhausen noise profile for better revelation of the effect of microstructures on the magnetisation process in ferritic steels , 2015 .

[91]  Jihong Hwang,et al.  Measurement of Temperature Field in Surface Grinding Using Infra-Red (IR) Imaging System , 2003 .

[92]  Shih-Bin Wang,et al.  Selections of working conditions for creep feed grinding. Part(III): avoidance of the workpiece burning by using improved BP neural network , 2004 .

[93]  R. Sacramento,et al.  Eddy current techniques for super duplex stainless steel characterization , 2015 .

[94]  W. Ding,et al.  Prediction of high-speed grinding temperature of titanium matrix composites using BP neural network based on PSO algorithm , 2017 .

[95]  Marc Pepi,et al.  Specific grinding energy causing thermal damage in helicopter gear steel , 2002 .

[96]  Juha Siiriäinen,et al.  Barkhausen Noise and its Use for Quality Control of the Production of Transmission Gears , 2008 .

[97]  A. Sorsa,et al.  Barkhausen noise-magnetizing voltage sweep measurement in evaluation of residual stress in hardened components , 2014 .

[98]  Fábio Romano Lofrano Dotto,et al.  Automatic detection of thermal damage in grinding process by artificial neural network , 2003 .

[99]  Arnold W. M. Smeulders,et al.  Color-based object recognition , 1997, Pattern Recognit..