Recently, besides regression analysis, artificial neural networks (ANNs) are increasingly used to predict the state of tools. Nevertheless, simulations trained by cutting modes, material type and the method of sharpening twist drills (TD) and the drilling length from sharp to blunt as input parameters and axial drilling force and torque as output ANN parameters did not achieve the expected results. Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a function of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations of the other influencing parameters were taken as constant values. The results of the prediction obtained by applying the FANN were compared with the results obtained by regression analysis at the points of experimental results. The comparison confirmed that the FANN can be used as a very reliable method for predicting tool condition.

The first part of the paper is a systematic explanation of the process of defining the most important parameters for generating optimal and efficient models of small and medium sized enterprises (SMEs) of the clothing industry, with the presentation of specific and adequate methods of research, i.e. with the evaluation of data for designing new models, and including previous research data. The following is an explanation of the final phase, i.e. a systematic and objective design assessment, through the implementation of preliminary results of exploitation studies of the modal experiment and computer simulation of the new model, based on which the criteria for efficient and optimal implementation of the CAD/CAM systems are defined.

Twist drill flute profile design is necessary in order to determine the required grinding wheel profile for a flute production. An accurate drill flute profile design is generated for two-flute conical twist drills using analytical equations to generate a drill flute profile design needed for the production of twist drills with straight lips. The required grinding wheel profile for a flute production was expressed in digitized form as well as in terms of two curve-fitted circular arcs. The drill flute profile geometry can never be precisely generated when required grinding wheel profile is represented by two circular arcs and the generated flute profile is just a very good approximation of the design flute profile. A CAD (computer aided design) software has been developed using MATLAB to determine the required grinding wheel profile for generating a given drill flute profile design.

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