![]() ![]() The numerical method qualitatively agrees with the experimental data while the ANN model is accurate and consistent in its predictions.Įvery manufacturing process aims at producing quality parts, i.e., parts that satisfy requirements and specifications posed by functional or even aesthetic demands. The results support the proposed cutting mechanism. The verification of the numerical method and the development of the ANN model were based on data gathered from turning experiments conducted on a CNC lathe. These deviations are calculated with reference to final diameter values measured with precision micrometers or on a CMM. ![]() The second approach involves the use of artificial neural networks (ANNs) in order to develop a model that can predict the dimensional deviation of the final part by correlating the cutting parameters and certain workpiece geometrical characteristics with the deviations of the depth of cut. ![]() The whole approach is implemented through a Microsoft Excel © workbook. Given the mechanical properties of the workpiece material, the geometry of the final part and the cutting force values, this numerical method can predict the elastic deflection. The first approach is based on solving the analytical equations of the elastic line, in discretized segments of the workpiece, by considering a stored modal energy formulation due to the cutting forces. Consequently, the problem of determining the workpiece elastic deflection is addressed from two different points of view. In this paper, a cutting mechanism is described suggesting that the above problem results in an over-dimensioned part. One of the problems faced in turning processes is the elastic deformation of the workpiece due to the cutting forces resulting in the actual depth of cut being different than the desirable one. ![]()
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