Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.

[1]  Berend Denkena,et al.  Abrasion Monitoring and Automatic Chatter Detection in Cylindrical Plunge Grinding , 2013 .

[2]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[3]  Jue Jin,et al.  Study on machining prediction in plane grinding based on artificial neural network , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.

[4]  T.M.A. Maksoud,et al.  Applications of Artificial Intelligence to Grinding Operations via Neural Networks , 2003 .

[5]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[6]  Jun Qu,et al.  Grinding wheel condition monitoring with boosted minimum distance classifiers , 2008 .

[7]  Jun Qu,et al.  GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS , 2006 .

[8]  Guofa Li,et al.  On-line prediction of surface roughness in cylindrical traverse grinding based on BP+GA algorithm , 2011, 2011 Second International Conference on Mechanic Automation and Control Engineering.

[9]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[10]  Ekkard Brinksmeier,et al.  Monitoring of grinding wheel wear , 1992 .

[11]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[12]  Ekkard Brinksmeier,et al.  Advances in Modeling and Simulation of Grinding Processes , 2006 .

[13]  Davood Afshari,et al.  Creep feed grinding optimization by an integrated GA-NN system , 2010, J. Intell. Manuf..

[14]  Je Morgan,et al.  In-process detection of grinding wheel truing and dressing conditions using a flapper nozzle arrangement , 1997 .

[15]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[16]  Ichiro Inasaki,et al.  Tribology of Abrasive Machining Processes , 2004 .

[17]  Martin T. Hagan,et al.  Neural network design , 1995 .

[18]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[19]  W. Rowe,et al.  Handbook of Machining with Grinding Wheels , 2006 .

[20]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[21]  T.M.A. Maksoud,et al.  Review of Intelligent Grinding and Dressing Operations , 2004 .

[22]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[23]  Abbas Vafaeesefat,et al.  Optimum creep feed grinding process conditions for Rene 80 supper alloy using neural network , 2009 .

[24]  Fukuo Hashimoto,et al.  Industrial challenges in grinding , 2009 .

[25]  Stephen Malkin,et al.  Grinding Technology: Theory and Applications of Machining with Abrasives , 1989 .

[26]  Dilip Kumar Pratihar,et al.  Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding , 2004, Fuzzy Sets Syst..

[27]  Zhensheng Yang,et al.  Grinding wheel wear monitoring based on wavelet analysis and support vector machine , 2011, The International Journal of Advanced Manufacturing Technology.

[28]  Pawel Lezanski,et al.  An intelligent system for grinding wheel condition monitoring , 2001 .