Energy Consumption Analysis Of Machining Centers Using Bayesian Analysis And Genetic Optimization
Responding to the current urgent need for low carbon emissions and high efficiency in manufacturing processes, the relationships between three different machining factors (depth of cut, feed rate, and spindle rate) on power consumption and surface finish (roughness) were analysed by applying a Bayes...
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Zusammenfassung: | Responding to the current urgent need for low carbon emissions and high
efficiency in manufacturing processes, the relationships between three
different machining factors (depth of cut, feed rate, and spindle rate) on
power consumption and surface finish (roughness) were analysed by applying a
Bayesian seemingly unrelated regressions (SUR) model. For the analysis, an
optimization criterion was established and minimized by using an optimization
algorithm that combines evolutionary algorithm methods with a derivative-based
(quasi-Newton) method to find the optimal conditions for energy consumption
that obtains a good surface finish quality. A Bayesian ANOVA was also performed
to identify the most important factors in terms of variance explanation of the
observed outcomes. The data were obtained from a factorial experimental design
performed in two computerized numerical control (CNC) vertical machining
centers (Haas UMC-750 and Leadwell V-40iT). Some results from this study show
that the feed rate is the most influential factor in power consumption, and the
depth of cut is the factor with the stronger influence on roughness values. An
optimal operational point is found for the three factors with a predictive
error of less than 0.01% and 0.03% for the Leadwell V-40iT machine and the Haas
UMC-750 machine, respectively. |
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DOI: | 10.48550/arxiv.2207.05243 |