Data-driven prediction of the surface layer state in hard-turning for optimization of component quality

Data-driven approaches are an effective solution for modeling problems in machining. To increase the service life of hard-turned components, it is important to quantify the correlation between the cutting parameters such as feed rate, cutting speed and depth of cut and the near-surface properties. F...

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Bibliographic Details
Published in:Production engineering (Berlin, Germany) Germany), 2024-04, Vol.18 (2), p.381-392
Main Authors: Wittich, Felix, Wegener, Thomas, Liehr, Alexander, Zinn, Wolfgang, Niendorf, Thomas, Kroll, Andreas
Format: Article
Language:English
Subjects:
Bayesian analysis
Cutting parameters
Cutting speed
Engineering
Feed rate
Gaussian process
Industrial and Production Engineering
Material Properties
Mathematical models
Optimization
Parameter identification
Polynomials
Production
Residual stress
Service life
Surface layers
Surface properties
Surface roughness
Turning (machining)
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Summary:Data-driven approaches are an effective solution for modeling problems in machining. To increase the service life of hard-turned components, it is important to quantify the correlation between the cutting parameters such as feed rate, cutting speed and depth of cut and the near-surface properties. For obtaining high-quality models with small data sets, different data-driven approaches are investigated in this contribution. Additionally, models that enable uncertainty quantification are crucial for effective decision-making and the adjustment of cutting parameters. Therefore, parametric multiple polynomial regression and Takagi–Sugeno models, as well as non-parametric Gaussian process regression as a Bayesian approach are considered and compared regarding their capability to predict residual stress and surface roughness values of 51CrV4 specimens after hard-turning. Moreover, a novel method based on optimization of data driven non-linear models is proposed that allows for identification of cutting parameter combinations, which at the same time lead to satisfactory surface roughness and residual stress states.
ISSN:0944-6524
1863-7353
DOI:10.1007/s11740-023-01256-w