Home ›
Entities
› academia
› Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites
Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites
Research article (Smart Materials and Structures, 2021) · cited 16× · AI/ML
Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites
Summary
Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites is a scholarly article[1].
Key Facts
Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites'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). Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites. Retrieved May 24, 2026, from https://4ort.xyz/entity/artificial-neural-networks-and-phenomenological-degradation-models-for-fatigue-damage-tracking-and-life-prediction-in-la
MLA“Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/artificial-neural-networks-and-phenomenological-degradation-models-for-fatigue-damage-tracking-and-life-prediction-in-la.
BibTeX@misc{4ortxyz_artificial-neural-networks-and-phenomenological-degradation-models-for-fatigue-damage-tracking-and-life-prediction-in-la_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites}}, year = {2026}, url = {https://4ort.xyz/entity/artificial-neural-networks-and-phenomenological-degradation-models-for-fatigue-damage-tracking-and-life-prediction-in-la}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites — https://4ort.xyz/entity/artificial-neural-networks-and-phenomenological-degradation-models-for-fatigue-damage-tracking-and-life-prediction-in-la (retrieved 2026-05-24)
