Abstract
Turning-based precision machining produces non-Gaussian spatial surface topographies, inherently defined by skewness () and kurtosis (), which govern surface quality, tribology, and functional performance of the machined workpiece. However, surface roughness modeling remains challenging due to process uncertainty, measurement variability, and the limited integration of and into spatial surface roughness analyses, despite their fundamental role in defining non-Gaussian topographies produced by turning operations. This paper presents a novel data-driven metamodeling framework–Spatial Non-Gaussian Roughness Metamodeling (SNGRM)–that integrates geospatial analysis and kriging interpolation to achieve spatial mapping of arithmetic mean roughness () under explicitly non-Gaussian surface conditions defined by and, thereby advancing multivariate characterization of non-Gaussian machined surfaces. For the interpolation of, ordinary kriging captures spatial variability inferred from the - domain, while universal kriging advances this metamodeling framework by incorporating machining parameters as external drift to explain systematic variations in surface roughness. Consequently, group-wise cross-validation demonstrates that universal kriging achieves superior predictive performance by more effectively capturing both non-Gaussian spatial variability and deterministic trends associated with machining parameters. The proposed framework directly exploits empirical machining data while retaining measurement variability arising from repeated observations, which is frequently averaged out or neglected in conventional roughness models. By jointly incorporating non-Gaussian spatial characteristics and machining parameters, SNGRM enables robust spatial interpolation of the roughness quality index,, with explicit quantification of associated uncertainty via kriging variance. This framework provides a rigorous data-driven metamodel for a reliable digital roughness mapping in precision machining.
Recommended Citation
P. R. Dey and D. Enke, "Data-driven Spatial Metamodeling For Non-Gaussian Digital Roughness Mapping In Precision Machining," Scientific Reports, vol. 16, no. 1, article no. 24892, Nature Research, Dec 2026.
The definitive version is available at https://doi.org/10.1038/s41598-026-52777-0
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Digital Roughness Mapping; Kriging; Non-Gaussian Surface; Precision Machining; Spatial Metamodeling
International Standard Serial Number (ISSN)
2045-2322
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2026
PubMed ID
42225732
Included in
Finance and Financial Management Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
