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In the first setting of input machining parameters, aluminum powder improves the surface roughness of TiAl sample about 32% comparing with EDM case and also aluminum particles with the size of 2 μm, in the second setting of input parameters lead to 54% enhancement of MRR comparing with EDM case.
We demonstrate a real-world application of the interactive multiple objective optimization (MOO) approach to the simultaneous setting of input and output amounts for the opening of new branches.
By virtue of its suitable features, Transitive heuristic provides good quality results in reasonable computing time and that without using the traditional sampling methods or even the setting of input parameters, such as the number of clusters, thresholds, and other parameters.
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In this paper we obtain extensions of these results to the setting of input-output maps of nonlinear discrete-time systems.
At present we do not rescale the results to a common set of input parameters.
The experimental results have been rescaled to a common set of input parameters (see table below).
The output was the electron density for a given set of input parameters.
The set of input data is collected from different azimuthal plane orientations inside the homogenization domain.
At present we do not apply a rescaling of the results to a common, updated set of input parameters.
We use Combos v3.20 (homepage, manual) for the rescaling of the experimental results to common sets of input parameters.
The interpolation produced incorrect results for limited sets of input parameters, in some cases yielding fraction > 1.
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CEO of Professional Science Editing for Scientists @ prosciediting.com