The fuzzy-nets based approach in predicting the cutting power of end milling operations

Process planning is a major determinant of manu&cturing cost. The selection of machining parameters is an important element of process planning. The development of a utility to show the cutting power on-line would be helpfiil to programmers and process planners in selecting machining parameters. The relationship between the cutting power and the machining parameters is nonlinear. Presently there is no accurate or simple algorithm to calculate the required cutting power for a selected set of parameters. Although machining data handbooks, machinability data systems, and machining databases have been developed to recommend machining parameters for efScient machining, they are basically for general reference and hard to use as well. In this research, a self-organizing fuzzy-nets optimization system was developed to generate a knowledge bank that can show the required cutting power on-line for a short length of time in an NC verifier. The fiizzy-nets system (FNS) utilizes a five-step self-learning procedure. A generic FNS program consisting of fuzzification and deflizzification modules was nnplemented in the C-Hprogramming language to perform the procedure. The FNS was assessed before an actual experiment was set up to collect data. The performance of the FNS was then examined for end milling operations on a Fadal VMC40 vertical machining center. The cutting force signals were measured by a threeconq}onent dynamometer mounted on the table of the Fadal CNC machine with the workpiece moimted on it. Amplified signals were collected by a personal computer on which an Omega DAS-1401 analog-to-digital (A/D) converter was installed to sample the data on-line. Data sets were collected to train and test the system The results showed that the FNS possessed a

[1]  Ibrahim N. Tansel,et al.  Detection of tool breakage in milling operations-I , 1993 .

[2]  Hung T. Nguyen,et al.  Theoretical aspects of fuzzy control , 1995 .

[3]  S.K. Pal,et al.  Neurocomputing motivation, models, and hybridization , 1996, Computer.

[4]  E. Degarmo.  Materials and Processes in Manufacturing , 1974 .

[5]  Hamid R. Berenji,et al.  Learning and tuning fuzzy logic controllers through reinforcements , 1992, IEEE Trans. Neural Networks.

[6]  Naim A. Kheir.  Systems modeling and computer simulation: J. Dastych Ruhr university Bochum, lehrstuhl elektrische steuerung und regelung, postfach 102148, D-4630 Bochum 1, F.R.G , 1990, Autom..

[7]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

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

[9]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[10]  Robert G. Sargent,et al.  Simulation model verification and validation , 1991, 1991 Winter Simulation Conference Proceedings..

[11]  Phillip F. Ostwald,et al.  Manufacturing processes : SI version , 1979 .

[12]  Chao-Hsien Chu,et al.  Manufacturing Cell Formation by Competitive Learning , 1993 .

[13]  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..

[14]  Bernard Widrow,et al.  Neural networks: applications in industry, business and science , 1994, CACM.

[15]  E.J.A. Armarego,et al.  Force prediction models and CAD/CAM software for helical tooth milling processes. II. Peripheral milling operations , 1993 .

[16]  Stuart E. Dreyfus,et al.  A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear , 1995 .

[17]  T. J. Ko,et al.  On-line monitoring of tool breakage in face milling using a self-organized neural network , 1995 .

[18]  B. S. V. Prasad.  Designing programming station software for CNC profile cutting , 1992 .

[19]  G. Abdou,et al.  Analysis of force patterns and tool life in milling operations , 1995 .

[20]  E.J.A. Armarego,et al.  Force prediction models and CAD/CAM software for helical tooth milling processes. I. Basic approach and cutting analyses , 1993 .

[21]  R. A. Lindberg,et al.  Processes and materials of manufacture , 1977 .

[22]  I. S. Jawahir,et al.  Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters , 1994 .

[23]  Konstantinos-Dionysios Bouzakis,et al.  NC-Code Preparation with Optimum Cutting Conditions in 3-Axis Milling , 1992 .

[24]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

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

[26]  Harold William Porter,et al.  Machine Shop Operations and Setups , 1973 .

[27]  Neil Sclater,et al.  McGraw-Hill Electronics Dictionary , 1978 .

[28]  Ibrahim N. Tansel,et al.  Detection of tool breakage in milling operations—II. The neural network approach , 1993 .

[29]  Anca L. Ralescu,et al.  Applied Research in Fuzzy Technology , 1994 .

[30]  R.M.D. Mesquita,et al.  Computer-aided selection of optimum machining parameters in multipass turning , 1995 .

[31]  Chew Lim Tan,et al.  An Artificial Neural Network that Models Human Decision Making , 1996, Computer.

[32]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[33]  Noel R. Parsons,et al.  N/C machinability data systems , 1971 .

[34]  Yunn-Shiuan Liao,et al.  A new on-line spindle speed regulation strategy for chatter control , 1996 .

[35]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[36]  Adedeji Badiru,et al.  Expert Systems Applications in Engineering and Manufacturing , 1998 .

[37]  Peter L. Knepell,et al.  Simulation Validation: A Confidence Assessment Methodology , 1993 .

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

[39]  Y. S. Tarng,et al.  Sensing tool breakage in face milling with a neural network , 1994 .

[40]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.