The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation

The capability to generate complex geometrical features at tight tolerances and fine surface roughness is a key element in the 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 co-ordinate measurements both the radial and tangential forces can be gauged versus the accuracy of the ground features. The tangential force was 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 (proportional to force: power was also included). For multi process, one machining platforms hole making was also investigated in terms of monitoring the force to ensure the mean cylinder was kept within required tolerances and with minimal subsequent machining (due to these imposed accuracies this is also considered a complex feature). 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 correlated with the said chosen “gauging” methodology (for grinding process). GP was also used to correlate the force and flank wear (VB) for hole deviations. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of ground and drilled hole features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile (both grinding and hole making). 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. The best obtained GP terminal sets were realised in rule-based embedded coded systems which were finally 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 used for GP decision verification ensuring less sensitivity to possible outliers giving more robustness to the integrated system.