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Article overview
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Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization | | Markus Maier
; Alisa Rupenyan
; Christian Bobst
; Konrad Wegener
; | | Date: |
9 Jun 2020 | | 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 constraints are defined
with respect to the temperature at the grinding surface. Grinding temperature
is measured at the contact zone between the 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. | | Source: | arXiv, 2006.5360 | | Services: | Forum | Review | PDF | Favorites | |
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