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- W3024039786 abstract "In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements of the final workpiece are defined with respect to grinding burn and surface roughness, and the safety constrains are defined with respect to the temperature at the grinding surface. Grinding temperature is measured at the contact zone between grinding wheel and workpiece using a pyrometer and an optical fiber, which is embedded inside the rotating grinding wheel. Constrained Bayesian optimization combined with Gaussian process models is applied to determine the optimal feed rate and cutting speed of a cup wheel grinding machine manufacturing tungsten carbide cutting inserts. The approach results in the determination of optimal parameters for unknown workpiece and tool combinations after only a few grinding trials. It also incorporates the uncertainty of the constraints in the prediction of optimal parameters by using stochastic process models." @default.
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- W3024039786 date "2020-05-01" @default.
- W3024039786 modified "2023-10-16" @default.
- W3024039786 title "Self-optimizing grinding machines using Gaussian process models and constrained Bayesian optimization" @default.
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- W3024039786 doi "https://doi.org/10.1007/s00170-020-05369-9" @default.
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