Polishing of uneven surfaces using industrial robots based on neural network and genetic algorithm
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In conventional polishing processes, the polishing parameters are constant along the surface. Hence, if the desired material to be removed from the surface is not equally distributed, an over-polishing may occur for the areas with small material removal and under-polishing for the areas with large material removal. Consequently, the quality of the processed surface may not meet the manufacture requirements. In this paper, the authors proposed a polishing algorithm to deal with this problem using neural network (NNW) and genetic algorithm (GA). The NNW is used to predict the polishing performance parameters corresponding to a certain polishing parameters. In addition, the GA is employed to optimize the polishing parameters according to an objective function that includes the desired material removal and surface roughness improvement using the output from the trained NNW model. The effectiveness of the proposed algorithm is verified through experiments of polishing uneven surface.
KeywordsRobotic finishing Uneven surface polishing Force control Neural network and genetic algorithm
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The authors gratefully acknowledge the support of the A*STAR Industrial Robotics Program Science and Engineering Research Council Grant number 122510004.
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