Home › Entities › academia › Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting

Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting

Research article (Journal of Manufacturing Processes, 2022) · cited 61× · AI/ML
academia openalex:W4283374139
Press Enter · cited answer in seconds

Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting

Summary

Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting is a scholarly article[1].

Key Facts

  • Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting. Retrieved May 24, 2026, from https://4ort.xyz/entity/comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s
MLA “Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s.
BibTeX @misc{4ortxyz_comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting}}, year = {2026}, url = {https://4ort.xyz/entity/comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Comparative analysis and experimental validation of statistical and machine learning-based regressors for modeling the surface roughness and mechanical properties of 316L stainless steel specimens produced by selective laser melting — https://4ort.xyz/entity/comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/comparative-analysis-and-experimental-validation-of-statistical-and-machine-learning-based-regressors-for-modeling-the-s · Last refreshed: