Keywords: Multi-component balance, multi-piece balance, balance calibration, neural networks, Levenberg-Marquardt algorithm
Abstract: A 1.5 inch diameter, multi-piece, six-component (five-force / one-moment) balance was calibrated at three balance calibration facilities. This paper evaluates the use of neural networks to estimate the balance calibration loading at each facility. For the current work, multiple-input, single-output neural networks are trained using a Levenberg-Marquardt algorithm for each of the balance load gages. The load estimates obtained from these neural network computations are compared to load estimates computed from the more traditional regression-based polynomial math model for the balance response. Cross-validation of the neural network and regression models is performed to evaluate calibration repeatability within a facility and between facilities.
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