Development of a fuzzy-nets based adapted surface roughness control (FNASRC) system in end-milling operations

A fuzzy-nets based in-process adaptive surface roughness control (FNASRC) system was developed in this research. The FNASRC system was able to adapt cutting parameters in-process and in a real time fashion to improve the surface roughness of machined parts when the surface roughness quality was not meeting customer requirements in the end milling operations. The FNASRC system was comprised of two sub-systems: (a) in-process surface roughness recognition (IPSRR); and (b) fuzzy-nets adaptive feed rate control (FNAFRC). Two IPSRR sub-systems with different theories were developed and evaluated in this study. First, a multiple linear regression based in-process surface roughness recognition system (MLR-IPSRR) was developed; it had approximately 90% accuracy with 30 testing data sets. Secondly, a fiizzy-nets based in-process surface roughness recognition (FN-IPSRR) sub-system was also developed and had 94% accuracy in predicting surface roughness while the machining process was taking place using similar testing data sets. Due to superior accuracy, the FN-IPSRR sub-system was implemented into the FNASRC system along with the FNAFRC sub-system. Furthermore, the FNAFRC sub-system was developed using a fuzzy nets theory to adapt a proper feed rate, which could produce the desired surface roughness when the original surface roughness could not meet customer requirements. Integrating the above-mentioned two sub-systems, the FNASRC system was developed and tested. To test the system, while the machining process was taking place, the FN-IPSRR system predicted the surface roughness, which was then compared to the desired

[1]  Pau-Lo Hsu,et al.  FUZZY ADAPTIVE-CONTROL OF MILLING PROCESSES , 1992 .

[2]  S. C. Huang,et al.  Modeling and Vibration Analysis of Spinning-Disk and Moving-Head Assembly in Computer Storage Systems , 1997 .

[3]  Yoram Koren,et al.  Control of Machine Tools , 1997 .

[4]  John W. Sutherland,et al.  An Improved Method for Cutting Force and Surface Error Prediction in Flexible End Milling Systems , 1986 .

[5]  John A. Allocca,et al.  Transducers: Theory and Applications , 1984 .

[6]  Ruxu Du,et al.  Tool condition monitoring in turning using fuzzy set theory , 1992 .

[7]  M. Bratos-Anderson,et al.  Solving problems in vibrations , 1987 .

[8]  Joseph C. Chen.  An effective fuzzy-nets training scheme for monitoring tool breakage , 2000, J. Intell. Manuf..

[9]  O. Adetona,et al.  Predicting quality characteristics of end-milled parts based on multi-sensor integration using neural networks: individual effects of learning parameters and rules , 1995, J. Intell. Manuf..

[10]  Dong-Woo Cho,et al.  Accurate 3-D cutting force prediction using cutting condition independent coefficients in end milling , 2001 .

[11]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[12]  Eiji Usui,et al.  An evaluation approach of machine tool characteristics with adaptive prediction , 1996 .

[13]  Zhang Deyuan,et al.  On-line detection of tool breakages using telemetering of cutting forces in milling , 1995 .

[14]  Edward G. Schilling,et al.  Juran's Quality Handbook , 1998 .

[15]  A. Galip Ulsoy,et al.  Model Reference Adaptive Force Control in Milling , 1989 .

[16]  Joseph C. Chen,et al.  A fuzzy-nets tool-breakage detection system for end-milling operations , 1996 .

[17]  Colin Bradley,et al.  A Fiber Optic Sensor for Surface Roughness Measurement , 1998 .

[18]  J. Temple Black,et al.  The design of the factory with a future , 1991 .

[19]  Min-Yang Yang,et al.  A Study on the Relationships Between Static/Dynamic Cutting Force Components and Tool Wear , 2001 .

[20]  A.G. Ulsoy,et al.  Applications of adaptive control to machine tool process control , 1989, IEEE Control Systems Magazine.

[21]  Chaojun Wang,et al.  Intelligent adaptive control in milling processes , 1999, Int. J. Comput. Integr. Manuf..

[22]  Cairo L. Nascimento,et al.  An NN-based approach for tuning servocontrollers , 1999, Neural Networks.

[23]  W. M. Steen,et al.  Fuzzy Rule Based Prediction System of Surface Damage in the Laser Cleaning Process , 2000 .

[24]  Dong Young Jang,et al.  Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning , 1996 .

[25]  Sounak Kumar Choudhury,et al.  In-process tool wear estimation in milling using cutting force model , 2000 .

[26]  Kuang-Hua Fuht,et al.  A Proposed statistical model for surface quality prediction in end-milling of A1 alloy , 1995 .

[27]  John H. Sheesley,et al.  Quality Engineering in Production Systems , 1988 .

[28]  S. Li,et al.  Tool Condition Monitoring in Machining by Fuzzy Neural Networks , 1996 .

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

[30]  Tohru Watanabe.  A Model-Based Approach to Adaptive Control Optimization in Milling , 1986 .

[31]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[32]  Jack B. Revelle,et al.  The QFD handbook , 1998 .

[33]  Krzysztof Jemielniak,et al.  Catastrophic Tool Failure Detection Based on Acoustic Emission Signal Analysis , 1998 .

[34]  Yueh-Jaw Lin,et al.  A 3D Predictive Cutting-Force Model for End Milling of Parts Having Sculptured Surfaces , 2000 .

[35]  John T. Roth,et al.  Monitoring End-Mill Wear and Predicting Tool Failure Using Accelerometers , 1998, Manufacturing Science and Engineering.

[36]  J. Mou,et al.  An Adaptive Methodology for Machine Tool Error Correction , 1995 .

[37]  Shih-Chieh Lin,et al.  Force-based model for tool wear monitoring in face milling , 1995 .

[38]  M. W. Cho,et al.  Application of the fuzzy control strategy to adaptive force control of non-minimum phase end milling operations , 1994 .

[39]  J. Bennett,et al.  Scanning force microscope as a tool for studying optical surfaces. , 1995, Applied optics.

[40]  Mohamed A. Elbestawi,et al.  Application of Some Parameter Adaptive Control Algorithms in Machining , 1990 .

[41]  Farrokh Sassani,et al.  DESIGN AND ANALYSIS OF ADAPTIVE CONTROLLERS FOR MILLING PROCESS , 1990 .

[42]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[43]  Igor Grabec,et al.  Application of a neural network to the estimation of surface roughness from ae signals generated by friction process , 1995 .

[44]  Y. S. Tarng,et al.  In-process prediction of corner wear in drilling operations , 2000 .

[45]  Hirokazu Ihara,et al.  Fuzzy Control for Automatic Train Operation System , 1983 .

[46]  Reza Langari,et al.  A Neuro-Fuzzy System for Tool Condition Monitoring in Metal Cutting , 2001 .

[47]  M. A. Younis.  On line surface roughness measurements using image processing towards an adaptive control , 1998 .

[48]  Shih-Chieh Lin,et al.  Tool wear monitoring in face milling using force signals , 1996 .

[49]  Li-Xin Wang.  Stable adaptive fuzzy control of nonlinear systems , 1993, IEEE Trans. Fuzzy Syst..

[50]  Richard F. Reiss,et al.  Anatomy Of Automation , 1962 .

[51]  Marvin E. Frerking.  Digital signal processing in communication systems , 1994 .

[52]  Duc Truong Pham,et al.  Neural Networks for Identification, Prediction and Control , 1995 .

[53]  Chuan-Teh Chang,et al.  The fuzzy-nets based approach in predicting the cutting power of end milling operations , 1997 .

[54]  George Chryssolouris,et al.  A Comparison of Statistical and AI Approaches to the Selection of Process Parameters in Intelligent Machining , 1990 .

[55]  Tao Cheng,et al.  A neural network based fuzzy learning controller and its experimental application to milling , 2000, Int. J. Comput. Integr. Manuf..

[56]  Joseph C. Chen,et al.  Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations , 2000, Int. J. Comput. Integr. Manuf..

[57]  Min-Yang Yang,et al.  In-process prediction of cutting depths in end milling , 1999 .

[58]  Pau-Lo Hsu,et al.  Fuzzy Adaptive Control of Machining Processes With a Self-Learning Algorithm , 1996 .

[59]  Kuang-Hua Fuh,et al.  A predicted milling force model for high-speed end milling operation , 1997 .

[60]  Joseph C. Chen,et al.  A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations , 1997 .

[61]  Kai Yang,et al.  Statistical surface roughness checking procedure based on a cutting tool wear model , 1994 .

[62]  Yung C. Shin,et al.  In-process control of surface roughness due to tool wear using a new ultrasonic system , 1996 .

[63]  Janez Kopac,et al.  Tool wear monitoring during the turning process , 2001 .

[64]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[65]  Joseph C. Chen,et al.  An in-process surface recognition system based on neural networks in end milling cutting operations , 1999 .

[66]  Joseph C. Chen,et al.  Multilevel on-line surface roughness recognition system in end milling operation , 1999 .

[67]  Armen Zakarian,et al.  Analysis of Process Models: A Fuzzy Logic Approach , 2001 .

[68]  Philip Wild.  Industrial Sensors and Applications for Condition Monitoring , 1994 .

[69]  S. J. Lou,et al.  In-process surface recognition of a CNC milling machine using the fuzzy nets method , 1997 .

[70]  Lakhmi C. Jain,et al.  Intelligent Adaptive Control: Industrial Applications , 1998 .

[71]  B. Lee,et al.  Modeling the surface roughness and cutting force for turning , 2001 .

[72]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[73]  H. J. Park,et al.  A Fuzzy Self-Learning Control Method with Application to Hydroforming Processes , 1995 .

[74]  Joseph C. Chen,et al.  Development of four in-process surface recognition systems to predict surface roughness in end milling , 1997 .