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Published on: September 17, 2019
Data-driven spatial metamodeling for non-Gaussian digital roughness mapping in precision machining.
Prithbey Raj Dey1, David Enke2
1Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, 600 W 14th St, Rolla, MO, 65409, USA. prdfyb@mst.edu.
Scientific Reports
|June 1, 2026
Summary
Precision machining creates complex surfaces. This study introduces a new method using geospatial analysis and kriging to map surface roughness, considering non-Gaussian characteristics for better quality control.
Area of Science:
- Manufacturing Engineering
- Surface Metrology
- Data Science
Background:
- Turning processes generate non-Gaussian surface topographies characterized by skewness and kurtosis.
- Accurate surface roughness modeling is crucial for predicting tribology and functional performance but is challenged by process variability and limited integration of non-Gaussian parameters.
- Existing models often neglect the spatial variability and non-Gaussian nature of machined surfaces.
Purpose of the Study:
- To develop a novel data-driven metamodeling framework, Spatial Non-Gaussian Roughness Metamodeling (SNGRM), for spatial mapping of arithmetic mean roughness ([Formula: see text]).
- To integrate geospatial analysis and kriging interpolation to explicitly account for non-Gaussian surface conditions defined by skewness ([Formula: see text]) and kurtosis ([Formula: see text]).
- To advance the multivariate characterization of non-Gaussian machined surfaces by incorporating machining parameters.
Main Methods:
- Implementation of a data-driven metamodeling framework (SNGRM) utilizing geospatial analysis and kriging interpolation.
- Application of ordinary kriging to capture spatial variability within the skewness-kurtosis domain.
- Advancement using universal kriging, incorporating machining parameters as external drift to model systematic roughness variations.
Main Results:
- Universal kriging demonstrated superior predictive performance compared to ordinary kriging.
- The framework effectively captures both non-Gaussian spatial variability and deterministic trends influenced by machining parameters.
- SNGRM successfully integrates empirical machining data, retaining measurement variability for robust spatial interpolation of [Formula: see text].
Conclusions:
- The proposed SNGRM framework provides a rigorous, data-driven approach for digital roughness mapping in precision machining.
- Jointly incorporating non-Gaussian spatial characteristics and machining parameters enables reliable spatial interpolation of [Formula: see text] with quantified uncertainty.
- This metamodeling approach enhances the understanding and prediction of surface quality in turning operations.
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