Abstract
The dressing process is affected on the wheel surface topography and the selection of optimal dressing parameters makes increasing in the grinding performance. In this paper, a feed forward backpropagation neural network (FFBP-NN) and a simulated annealing (SA) algorithm were used for simultaneously minimize the tangential grinding force as well as minimize the surface roughness. First, the FFBP-NN was developed using the data generated based on experimental observations. In the experiments procedure, the grinding conditions were constant and only the disk dressing conditions were varied. Then, a SA algorithm was applied to the FFBP-NN for solving the optimal dressing parameters. In order to demonstrate the model performance, several initial trial values have tested. Results show that the proposed model has an acceptable performance to optimize the grinding process.
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Baseri, H. Simulated annealing based optimization of dressing conditions for increasing the grinding performance. Int J Adv Manuf Technol 59, 531–538 (2012). https://doi.org/10.1007/s00170-011-3518-9
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DOI: https://doi.org/10.1007/s00170-011-3518-9