Abstract
Input-output relationships of tungsten inert gas (TIG) welding and abrasive flow machining (AFM) processes were determined using radial basis function networks (RBFNs). A batch mode of training was adopted to implement the principle of back-propagation (BP) algorithm (which works based on a steepest descent algorithm) and a genetic algorithm (GA), separately. The performances of RBFN tuned by a BP algorithm and that trained by a GA were compared, on some test cases related to the above two manufacturing processes. The GA-optimized RBFN was found to perform slightly better than the BP-tuned RBFN. The back-propagation algorithm works based on the principle of a steepest descent method, whose solutions have the chance of getting stuck at the local minima, whereas the probability of the GA-solutions for being trapped at the local minima is less. However, their performances may depend on the nature of the deviation (error) function.
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Mollah, A.A., Pratihar, D.K. Modeling of TIG welding and abrasive flow machining processes using radial basis function networks. Int J Adv Manuf Technol 37, 937–952 (2008). https://doi.org/10.1007/s00170-007-1026-8
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DOI: https://doi.org/10.1007/s00170-007-1026-8