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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

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