Self-organizing Maps for Optimized Robotic Trajectory Planning Applied to Surface Coating

The process of surface coating is widely applied in the manufacturing industry. The accuracy of coating strongly affects the mechanical properties of the coated components. This work suggests the use of Self-Organizing Maps (Kohonen neural networks) for an optimal robotic beam trajectory planning for surface coating applications. The trajectory is defined by the one-dimensional sequence of neurons around a triangulated substrate and the neuron weights are defined as the position, beam vector and node velocity. During the training phase, random triangles are selected according to local curvature and the weights of the neurons whose beam coats the selected triangles are gradually adapted. This is achieved using a complicated coating thickness model as a function of stand-off distance, spray impact angle and beam surface spot speed. Initial results are presented from three objects widely used in manufacturing. The accuracy of this method is validated by comparing the simulated coating resulting from the SOM-planned trajectory to the coating performed for the same objects by an expert.