Theoretical and empirical coupled modeling on the surface roughness in diamond turning

• A surface roughness model capable of modeling certain and uncertain components is proposed.
• RBF neural network is employed in the modeling of uncertain components.
• PSO algorithm is applied in the process of optimization of cutting parameters.

In this work, a theoretical and empirical coupled method is proposed to predict the surface roughness achieved by single point diamond turning, in which the surface roughness is considered to be composed of the certain and uncertain parts. The certain components are directly formulated in theory, such as the effects of the kinematics and minimum undeformed chip thickness. The uncertain components in relation to the material spring back, plastic side flow, micro defects on the cutting edge of diamond tool, and others, are empirically predicted by a RBF (radial basis function) neural network, which is established by referring to the experimental data. Finally, the particle swarm optimization algorithm is employed to find the optimal cutting parameters for the best surface roughness. The validation experiments show that the optimization is satisfied, and the prediction accuracy is high enough, i.e. that the prediction error is only 0.59–10.11%, which indicates that the novel surface roughness prediction method proposed in this work is effective.

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