Fuzzy Logic for Machining Applications

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

[2]  G. Krishna Mohana Rao,et al.  Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm , 2009 .

[3]  Sivarao Hybrid Intelligence modeling of cut edge quality for Mn-Mo in laser machining by adaptive neuro-fuzzy inference system (ANFIS) , 2007, 2007 International Conference on Intelligent and Advanced Systems.

[4]  M.G. Simoes,et al.  Fuzzy Modeling Approaches for the Prediction of Machine Utilization in Hard Rock Tunnel Boring Machines , 2006, Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting.

[5]  Bor-Tsuen Lin,et al.  Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm , 2009, Expert Syst. Appl..

[6]  U. Zuperl,et al.  Fuzzy control strategy for an adaptive force control in end-milling , 2005 .

[7]  Feng Zou,et al.  Linguistic fuzzy model identification based on PSO with different length of particles , 2012, Appl. Soft Comput..

[8]  A. Shih,et al.  Design and tuning of a fuzzy logic controller for micro-hole electrical discharge machining , 2008 .

[9]  Chen Lu,et al.  Study on prediction of surface quality in machining process , 2008 .

[10]  Laith Abdullah Al-Juboori,et al.  Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240) , 2018 .

[11]  K. Kadirgama,et al.  Response Ant Colony Optimization of End Milling Surface Roughness , 2010, Sensors.

[12]  Tao Yu,et al.  Incorporating prior model into Gaussian processes regression for WEDM process modeling , 2009, Expert Syst. Appl..

[13]  T. Rajmohan,et al.  Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites , 2013 .

[14]  Jacob Chen,et al.  Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations , 2002 .

[15]  Ship-Peng Lo,et al.  An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling , 2003 .

[16]  Susmita Roy,et al.  Application of grey fuzzy logic for the optimization of CNC milling parameters for Al–4.5%Cu–TiC MMCs with multi-performance characteristics , 2016 .

[17]  Habibollah Haron,et al.  Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA , 2011, Expert Syst. Appl..

[18]  Grzegorz Skrabalak,et al.  Building of rules base for fuzzy-logic control of the ECDM process , 2004 .

[19]  Meng You Huo,et al.  Adaptive fuzzy control system of a servomechanism for electro-discharge machining combined with ultrasonic vibration , 2002 .

[20]  Doyoung Jeon,et al.  Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors , 2011 .

[21]  Jing Wu,et al.  A Modified Ant Colony System for the Selection of Machining Parameters , 2008, 2008 Seventh International Conference on Grid and Cooperative Computing.

[22]  Danko Brezak,et al.  Tool wear estimation using an analytic fuzzy classifier and support vector machines , 2012, J. Intell. Manuf..

[23]  G. E. D’Errico,et al.  Fuzzy control systems with application to machining processes , 2001 .

[24]  Habibollah Haron,et al.  Prediction of surface roughness in the end milling machining using Artificial Neural Network , 2010, Expert Syst. Appl..

[25]  Ibrahim N. Tansel,et al.  Selection of optimum cutting condition of cobalt-based superalloy with GONNS , 2010 .

[26]  María José del Jesús,et al.  Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction , 2005, IEEE Transactions on Fuzzy Systems.

[27]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[28]  Nabil Gindy,et al.  A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters , 2006 .

[29]  Antonín Dvorák,et al.  On linguistic approximation in the frame of fuzzy logic deduction , 1999, Soft Comput..

[30]  Rodolfo E. Haber,et al.  Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process , 2010, Journal of Intelligent Manufacturing.

[31]  C. K. Biswas,et al.  Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach , 2015 .

[32]  P. J. Pawar,et al.  Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms , 2010, Appl. Soft Comput..

[33]  Habibollah Haron,et al.  Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining , 2011, Engineering with Computers.

[34]  S. Rajesh,et al.  Neural Network and Fuzzy Logic based prediction of Surface Roughness and MRR in Cylindrical Grinding Process , 2017 .

[35]  N. Baskar,et al.  Optimization techniques for machining operations: a retrospective research based on various mathematical models , 2010 .

[36]  Fikri Dweiri,et al.  Fuzzy surface roughness modeling of CNC down milling of Alumic-79 , 2003 .

[37]  George C. Mouzouris,et al.  A Singular-Value-QR Decomposition Based Method for Training Fuzzy Logic Systems in Uncertain Environments , 1997, J. Intell. Fuzzy Syst..

[38]  Y. S. Tarng,et al.  Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics , 2000 .

[39]  Zuperl Uros,et al.  Adaptive network based inference system for estimation of flank wear in end-milling , 2009 .

[40]  Subramonian Sivarao,et al.  Mamdani Fuzzy Inference System Modeling to Predict Surface Roughness in Laser Machining , 2009 .

[41]  Ali Moeini,et al.  Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process , 2005 .

[42]  J. Paulo Davim,et al.  A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules , 2009 .

[43]  Liangchi Zhang,et al.  Surface roughness prediction of ground components using a fuzzy logic approach , 1999 .

[44]  Ning Wang,et al.  Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness , 2011 .

[45]  Dilip Kumar Pratihar,et al.  Forward and reverse mappings of electrical discharge machining process using adaptive network-based fuzzy inference system , 2010, Expert Syst. Appl..

[46]  Amir Saman Kheirkhah,et al.  Fuzzy logic in manufacturing: A review of literature and a specialized application , 2011 .

[47]  Keith Ridgway,et al.  An expert troubleshooting system for the milling process , 2007 .

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

[49]  Joseph C. Chen,et al.  Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations , 2006, Expert Syst. Appl..

[50]  Krzysztof Jemielniak,et al.  TSK fuzzy modeling for tool wear condition in turning processes: An experimental study , 2011, Eng. Appl. Artif. Intell..

[51]  Francisco Herrera,et al.  A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems , 2005, Eng. Appl. Artif. Intell..

[52]  Asif Iqbal,et al.  A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process , 2007, Expert Syst. Appl..

[53]  Ruey-Jing Lian,et al.  A grey prediction fuzzy controller for constant cutting force in turning , 2005 .

[54]  Jagdev Singh,et al.  An Adaptive Neuro-Fuzzy Inference System modeling for material removal rate in stationary ultrasonic drilling of sillimanite ceramic , 2010, Expert systems with applications.

[55]  Shuting Lei,et al.  Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations , 2004 .

[56]  Hasan Öktem,et al.  An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation , 2009 .

[57]  Y. S. Tarng,et al.  Adaptive learning control of milling operations , 1995 .

[58]  Li Xiang,et al.  A survey on artificial intelligence technologies in modeling of High Speed end-milling processes , 2009, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[59]  Hossam A. Kishawy,et al.  Optimization of CNC ball end milling : a neural network-based model , 2005 .

[60]  Emel Kuram,et al.  Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling , 2016, J. Intell. Manuf..

[61]  Clodeinir Ronei Peres,et al.  Fuzzy model and hierarchical fuzzy control integration: an approach for milling process optimization , 1999 .

[62]  R. Rajesh,et al.  Optimal Selection of Process Parameters in CNC End Milling of Al 7075-T6 Aluminium Alloy Using a Taguchi-fuzzy Approach , 2014 .

[63]  S. Ros,et al.  Fuzzy model of cutting process on a milling machine , 1994 .

[64]  Siti Zaiton Mohd Hashim,et al.  Fuzzy logic-based for predicting roughness performance of TiAlN coating , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[65]  Liang-Ying Wei,et al.  A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting , 2013, Appl. Soft Comput..

[66]  Vladimir Pucovsky,et al.  Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing , 2013, J. Intell. Manuf..

[67]  Chi-Cheng Fang,et al.  Application of genetic algorithm-based fuzzy logic control in wire transport system of wire-EDM machine , 2008 .

[68]  Chin Jeng Feng,et al.  Approach to prediction of laser cutting quality by employing fuzzy expert system , 2011, Expert Syst. Appl..

[69]  Abdulazeez Abdulraheem,et al.  Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization , 2011 .

[70]  S. Markos,et al.  Monitoring of milling processes based on artificial intelligence , 1993 .

[71]  S. Sharif,et al.  SIMULATED ANNEALING TO ESTIMATE THE OPTIMAL CUTTING CONDITIONS FOR MINIMIZING SURFACE ROUGHNESS IN END MILLING Ti-6Al-4V , 2010 .

[72]  Shi-Jer Lou,et al.  In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations , 1999 .

[73]  T. Ko,et al.  Estimation of tool wear length in finish milling using a fuzzy inference algorithm , 1993 .

[74]  G. Padmanabhan,et al.  Fuzzy logic-based forward modeling of Electro Chemical Machining process , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[75]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[76]  C. L. Lin,et al.  Optimisation of the EDM Process Based on the Orthogonal Array with Fuzzy Logic and Grey Relational Analysis Method , 2002 .

[77]  Uday S. Dixit,et al.  Application of soft computing techniques in machining performance prediction and optimization: a literature review , 2010 .

[78]  Uday S. Dixit,et al.  A knowledge-based system for the prediction of surface roughness in turning process , 2006 .

[79]  Angel Alique,et al.  Toward intelligent machining: hierarchical fuzzy control for the end milling process , 1998, IEEE Trans. Control. Syst. Technol..

[80]  Rodolfo E. Haber,et al.  Networked Control Based on Fuzzy Logic. An Application to a High-Performance Milling Process , 2007, IWANN.

[81]  Yunn-Shiuan Liao,et al.  Design of a fuzzy controller for the adaptive control of WEDM process , 2000 .

[82]  Patricia A. S. Ralston,et al.  Fuzzy logic control of chip form during turning , 1992 .

[83]  H. Metin Ertunç,et al.  Tool Wear Condition Monitoring in Drilling Processes Using Fuzzy Logic , 2006, ICONIP.

[84]  Vincenzo Catania,et al.  Genetic Tuning of Fuzzy Rule Deep Structures for Efficient Knowledge Extraction from Medical Data , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[85]  A. M. Sifullah,et al.  A Fuzzy Logic-Based Prediction Model for Kerf Width in Laser Beam Machining , 2016 .

[86]  Wei Li,et al.  A fuzzy system approach of feed rate determination for CNC milling , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[87]  Sitarama P Chakravarthy,et al.  A hybrid approach for selection of optimal process parameters in abrasive water jet cutting , 2000 .

[88]  Shankar Chakraborty,et al.  Application of grey-fuzzy approach in parametric optimization of EDM process in machining of MDN 300 steel , 2018 .

[89]  Hwa Jen Yap,et al.  Application of genetic algorithm for fuzzy rules optimization on semi expert judgment automation using Pittsburg approach , 2012, Appl. Soft Comput..

[90]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[91]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[92]  Tzu-Liang Tseng,et al.  A novel approach to predict surface roughness in machining operations using fuzzy set theory , 2016, J. Comput. Des. Eng..

[93]  Hazim El-Mounayri,et al.  NC end milling optimiza-tion using evolutionary computation , 2002 .

[94]  Dilbag Singh,et al.  Optimization of Tool Geometry and Cutting Parameters for Hard Turning , 2007 .

[95]  Gy. Hermann,et al.  Artificial intelligence in monitoring and the mechanics of machining , 1990 .

[96]  Habibollah Haron,et al.  Integration of simulated annealing and genetic algorithm to estimate optimal solutions for minimising surface roughness in end milling Ti-6AL-4V , 2011, Int. J. Comput. Integr. Manuf..

[97]  Yi Wang,et al.  A hybrid intelligent method for modelling the EDM process , 2003 .

[98]  Rodolfo E. Haber,et al.  Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process , 2010, Inf. Sci..

[99]  Shinn-Ying Ho,et al.  Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system , 2002 .

[100]  R. Saravanan,et al.  Optimization of multi-pass turning operations using ant colony system , 2003 .

[101]  Antonio González Muñoz,et al.  A Study About the Inclusion of Linguistic Hedges in a Fuzzy Rule Learning Algorithm , 1999, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[102]  Pandu Ranga Vundavilli,et al.  Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process , 2012, Knowl. Based Syst..

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

[104]  Mohsen Marani Barzani,et al.  Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning , 2015 .

[105]  Mohammad Ali Badamchizadeh,et al.  Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDM processes , 2013 .