The prediction of profile deviations when Creep Feed grinding complex geometrical features by use of neural networks and genetic programming with real-time simulation

Abstract

The capability to generate complex geometry features at tight tolerances and fine surface roughness is a key element in implementation of Creep Feed grinding process in specialist applications such as the aerospace manufacturing environment. Based on the analysis of 3D cutting forces, this paper proposes a novel method of predicting the profile deviations of tight geometrical features generated using Creep Feed grinding. In this application, there are several grinding passes made at varying depths providing an incremental geometrical change with the last cut generating the final complex feature. With repeatable results from coordinate measurements, both the radial and tangential forces can be gauged versus the accuracy of the ground features. The results of the tangential force were found more sensitive to the deviation of actual cut depth from the theoretical one. However, to make a more robust prediction on the profile deviation, its values were considered as a function of both force components. In addition, the power signals were obtained as these signals are also proportional to force and deviation measurements. Genetic programming (GP), an evolutionary programming technique, has been used to compute the prediction rules of part profile deviations based on the extracted radial and tangential force and correlated with the initial “gauging” methodology. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of the ground features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile. The mathematical rules are generated from Darwinian evolutionary strategy which provides the mapping between different output classes. GP works from crossover recombination of different rules, and the best individual is evaluated in terms of the given best fitness value so far which closes on an optimal solution. Once the best rule has been generated, this can be further used independently or in combination with other close-tobest rules to control the evolution of output measures of machining processes. The best GP terminal sets will be realised in rule-based embedded coded systems which will finally be implemented into a real-time Simulink simulation. This realisation gives a view of how such a control regime can be utilised within an industrial capacity. Neural networks were also used for GP rule verification.