Home ›
Entities
› academia
› A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts
A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts
Research article (International Journal of Mechanical Sciences, 2020) · cited 57× · AI/ML
A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts
Summary
A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts is a scholarly article[1].
Key Facts
A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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.
APA4ort.xyz Knowledge Graph. (2026). A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-hybrid-driven-approach-to-integrate-surrogate-model-and-bayesian-framework-for-the-prediction-of-machining-errors-of-t
MLA“A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-hybrid-driven-approach-to-integrate-surrogate-model-and-bayesian-framework-for-the-prediction-of-machining-errors-of-t.
BibTeX@misc{4ortxyz_a-hybrid-driven-approach-to-integrate-surrogate-model-and-bayesian-framework-for-the-prediction-of-machining-errors-of-t_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts}}, year = {2026}, url = {https://4ort.xyz/entity/a-hybrid-driven-approach-to-integrate-surrogate-model-and-bayesian-framework-for-the-prediction-of-machining-errors-of-t}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts — https://4ort.xyz/entity/a-hybrid-driven-approach-to-integrate-surrogate-model-and-bayesian-framework-for-the-prediction-of-machining-errors-of-t (retrieved 2026-05-24)
