Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

[1]  Asim Karim,et al.  Neural Network Model for Optimization of Cold-Formed Steel Beams , 1997 .

[2]  Nicolas Le Bihan,et al.  Polarized Signal Classification by Complex and quaternionic Multi-Layer Perceptrons , 2008, Int. J. Neural Syst..

[3]  Pramod K. Varshney,et al.  Noise Enhanced Nonparametric Detection , 2009, IEEE Transactions on Information Theory.

[4]  A. Tashakori,et al.  Optimum design of cold-formed steel space structures using neural dynamics model , 2002 .

[5]  John W. Sutherland,et al.  Dynamic model of the cutting force system in the turning process , 1990 .

[6]  Asim Karim,et al.  CONSCOM: An OO Construction Scheduling and Change Management System , 1999 .

[7]  Antoni Morro,et al.  Chaos-Based Mixed Signal Implementation of Spiking Neurons , 2009, Int. J. Neural Syst..

[8]  Mo Chen,et al.  Online Detection of the Modality of Complex-Valued Real World Signals , 2008, Int. J. Neural Syst..

[9]  Akira Hirose,et al.  Frequency-Multiplexing Ability of Complex-Valued Hebbian Learning in Logic Gates , 2008, Int. J. Neural Syst..

[10]  Yusuf Altintas,et al.  In-Process Detection of Tool Failure in Milling Using Cutting Force Models , 1989 .

[11]  Xiang Li,et al.  Genetic Algorithms for Feature Subset Selection in Equipment Fault Diagnosis , 2006 .

[12]  Witold Pedrycz,et al.  Experience-Consistent Modeling for Radial Basis Function Neural Networks , 2008, Int. J. Neural Syst..

[13]  Hojjat Adeli,et al.  Recurrent Neural Network for Approximate Earthquake Time and Location Prediction Using Multiple Seismicity Indicators , 2009, Comput. Aided Civ. Infrastructure Eng..

[14]  Hojjat Adeli,et al.  A neural dynamics model for structural optimization—Theory , 1995 .

[15]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[16]  Garimella Rama Murthy,et al.  Global Dynamics of a Class of Complex Valued Neural Networks , 2008, Int. J. Neural Syst..

[17]  Xiaoli Li,et al.  Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring , 2000, IEEE Trans. Ind. Electron..

[18]  Hojjat Adeli,et al.  Neuro‐genetic algorithm for non‐linear active control of structures , 2008 .

[19]  Hojjat Adeli,et al.  Enhancing Neural Network Traffic Incident‐Detection Algorithms Using Wavelets , 2001 .

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

[21]  Dennis Lendrem,et al.  Introduction to Statistical Quality Control, (4th edn) Douglas Montgomery, 2001 ISBN 0‐471‐31648‐2; 795 pages; £34.95, €57.70. $40.00 John Wiley & Sons; www.wileyeurope.com/cda/product/0,,0471986089,00.html , 2003 .

[22]  Naotake Kamiura,et al.  Associative Memory in quaternionic Hopfield Neural Network , 2008, Int. J. Neural Syst..

[23]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting , 2005 .

[24]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[25]  Xiang Li,et al.  An Intelligent Predictive Engine for Milling Machine Prognostic Monitoring , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[26]  Robert Gray,et al.  An intelligent business forecaster for strategic business planning , 1999 .

[27]  Huicheng Lian,et al.  No-Reference Video Quality Measurement with Support Vector Regression , 2009, Int. J. Neural Syst..

[28]  N. Sundararajan,et al.  A fully complex-valued radial basis function network and its learning algorithm. , 2009 .

[29]  Sundaram Suresh,et al.  A Fully Complex-Valued Radial Basis Function Network and its Learning Algorithm , 2009, Int. J. Neural Syst..

[30]  Boguslaw Cyganek,et al.  Color Image Segmentation with Support Vector Machines: Applications to Road Signs Detection , 2008, Int. J. Neural Syst..

[31]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[32]  James M. W. Brownjohn,et al.  Fuzzy Clustering of Stability Diagrams for Vibration-Based Structural Health Monitoring , 2008, Comput. Aided Civ. Infrastructure Eng..

[33]  Joseph C. Chen.  Neural Network-Based Tool Breakage Monitoring System for End Milling Operations , 2022 .

[34]  Karl Perusich,et al.  Using fuzzy cognitive maps to identify multiple causes in troubleshooting systems , 2008, Integr. Comput. Aided Eng..

[35]  Hojjat Adeli,et al.  NEURO-FUZZY LOGIC MODEL FOR FREEWAY WORK ZONE CAPACITY ESTIMATION , 2003 .

[36]  Masaki Kobayashi,et al.  Pseudo-Relaxation Learning Algorithm for Complex-Valued Associative Memory , 2008, Int. J. Neural Syst..

[37]  Xin Wang,et al.  A Fast Method for Implicit Surface Reconstruction Based on Radial Basis Functions Network from 3D Scattered Points , 2007, Int. J. Neural Syst..

[38]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[39]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[40]  Hojjat Adeli,et al.  Resource Scheduling Using Neural Dynamics Model of Adeli and Park , 2001 .

[41]  Tony R. Martinez,et al.  Improving Supervised Learning by Adapting the Problem to the Learner , 2009, Int. J. Neural Syst..

[42]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[43]  Hyo Seon Park,et al.  A neural dynamics model for structural optimization—Application to plastic design of structures , 1995 .

[44]  Anurag Pande,et al.  A Computing Approach Using Probabilistic Neural Networks for Instantaneous Appraisal of Rear‐End Crash Risk , 2008, Comput. Aided Civ. Infrastructure Eng..

[45]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

[46]  H. Adeli,et al.  A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease , 2008, Neuroscience Letters.

[47]  M. A. Elbestawi,et al.  Tool Condition Monitoring in Machining , 2006 .

[48]  Michele Scarpiniti,et al.  Flexible Nonlinear Blind Signal Separation in the Complex Domain , 2008, Int. J. Neural Syst..

[49]  Hojjat Adeli,et al.  Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings , 2005 .

[50]  Hojjat Adeli,et al.  A probabilistic neural network for earthquake magnitude prediction , 2009, Neural Networks.

[51]  Hojjat Adeli,et al.  RADIAL BASIS FUNCTION NEURAL NETWORK FOR WORK ZONE CAPACITY AND QUEUE ESTIMATION , 2003 .

[52]  Xiao-Hua Jin,et al.  Modeling Risk Allocation in Privately Financed Infrastructure Projects Using Fuzzy Logic , 2009, Comput. Aided Civ. Infrastructure Eng..

[53]  A Samant,et al.  ENHANCING NEURAL NETWORK INCIDENT DETECTION ALGORITHMS USING WAVELETS , 2001 .

[54]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[55]  Hojjat Adeli,et al.  Distributed neural dynamics algorithms for optimization of large steel structures , 1997 .

[56]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[57]  Hojjat Adeli,et al.  Comparison of fuzzy-wavelet radial basis function neural network freeway incident detection model with California algorithm , 2002 .

[58]  Hojjat Adeli,et al.  An adaptive conjugate gradient learning algorithm for efficient training of neural networks , 1994 .

[59]  Sadettin Orhan,et al.  Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness , 2007 .

[60]  Y. S. Tarng,et al.  Modeling and optimization of drilling process , 1998 .

[61]  Alessandro E. P. Villa,et al.  Emergence of Preferred Firing Sequences in Large Spiking Neural Networks during Simulated Neuronal Development , 2008, Int. J. Neural Syst..

[62]  José Ramón Villar,et al.  A fuzzy logic based efficient energy saving approach for domestic heating systems , 2009, Integr. Comput. Aided Eng..

[63]  Alireza Fatehi,et al.  Flexible Structure Multiple Modeling Using Irregular Self-Organizing Maps Neural Network , 2008, Int. J. Neural Syst..

[64]  Aini Hussain,et al.  An Intelligent Load Shedding Scheme Using Neural Networks and Neuro-Fuzzy , 2009, Int. J. Neural Syst..

[65]  Boaz Nadler,et al.  Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory , 2009, IEEE Transactions on Signal Processing.

[66]  R. Isermann,et al.  Model based detection of tool wear and breakage for machine tools , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[67]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

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

[69]  René V. Mayorga,et al.  A Radial Basis Function Network Approach for the Computation of Inverse Continuous Time Variant Functions , 2007, Int. J. Neural Syst..

[70]  Mir Mohammad Ettefagh,et al.  Effect of Different Tool Edge Conditions on Wear Detection by Vibration Spectrum Analysis in Turning Operation , 2008 .

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

[72]  Hujun Yin,et al.  Self-Organising Mixture autoregressive Model for Non-Stationary Time Series Modelling , 2008, Int. J. Neural Syst..

[73]  Simone G. O. Fiori.  Learning by Criterion Optimization on a Unitary Unimodular Matrix Group , 2008, Int. J. Neural Syst..

[74]  Tohru Nitta,et al.  The uniqueness Theorem for Complex-Valued Neural Networks with Threshold Parameters and the Redundancy of the Parameters , 2008, Int. J. Neural Syst..

[75]  Hojjat Adeli,et al.  Object-oriented backpropagation and its application to structural design , 1994, Neurocomputing.

[76]  A Karim,et al.  COMPARISON OF THE FUZZY–WAVELET RBFNN FREEWAY INCIDENT DETECTION MODEL WITH THE CALIFORNIA ALGORITHM , 2002 .

[77]  A. Unuvar,et al.  Tool condition monitoring in milling based on cutting forces by a neural network , 2003 .

[78]  Hojjat Adeli,et al.  Dynamic fuzzy wavelet neuroemulator for non‐linear control of irregular building structures , 2008 .

[79]  Barry P. Haynes,et al.  Pruning Artificial Neural Networks Using Neural Complexity Measures , 2008, Int. J. Neural Syst..

[80]  Ch. Venkateswarlu,et al.  Modeling and Optimization of a Pharmaceutical Formulation System Using Radial Basis Function Network , 2009, Int. J. Neural Syst..

[81]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[82]  Spyros G. Tzafestas,et al.  Neurodynamics and attractors in quantum associative memories , 2007, Integr. Comput. Aided Eng..

[83]  Hojjat Adeli,et al.  Parallel backpropagation learning algorithms on CRAY Y-MP8/864 supercomputer , 1993, Neurocomputing.

[84]  Hojjat Adeli,et al.  Fully Automated Design of Super-High-Rise Building Structures by a Hybrid AI Model on a Massively Parallel Machine , 1996, AI Mag..

[85]  V. Srinivasa Chakravarthy,et al.  Chaotic Synchronization Using a Network of Neural oscillators , 2008, Int. J. Neural Syst..

[86]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[87]  P. K. Simpson.  Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .

[88]  Hojjat Adeli,et al.  Optimization of space structures by neural dynamics , 1995, Neural Networks.

[89]  Antony Stathopoulos,et al.  Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow , 2008, Comput. Aided Civ. Infrastructure Eng..