Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Susheel Dharmadhikari"'
Autor:
Susheel Dharmadhikari, Amrita Basak
Publikováno v:
Machine Learning with Applications, Vol 7, Iss , Pp 100247- (2022)
Fatigue damage is one of the most common causes of failure in aerospace structural components. While numerical modeling and laboratory-scale experimentation provide much insight to the physics of failure evolution, it is extremely challenging to acco
Externí odkaz:
https://doaj.org/article/eb20f83caa8e4c6cb5308a7a213c636b
Publikováno v:
Applied Sciences, Vol 13, Iss 3, p 1542 (2023)
The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal micr
Externí odkaz:
https://doaj.org/article/ea0bf32af9b04cbfaa2f166f156f47a9
Publikováno v:
Metals, Vol 12, Iss 11, p 1849 (2022)
Fatigue damage detection and its classification in metallic materials are persistently challenging the structural health monitoring community. The mechanics of fatigue damage is difficult to analyze and is further complicated because of the presence
Externí odkaz:
https://doaj.org/article/53773bf264ef4bedb94c184849fc0f67
Publikováno v:
Machines, Vol 9, Iss 10, p 211 (2021)
The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors
Externí odkaz:
https://doaj.org/article/65c9c1489cc14fba95016943add74741
Process optimization for metal additive manufacturing (AM) is crucial to ensure repeatability, control microstructure, and minimize defects. Despite efforts to address this via the traditional design of experiments and statistical process mapping, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::452f5f4489bfd614ef7c4719ee7af6af
http://arxiv.org/abs/2211.09545
http://arxiv.org/abs/2211.09545
Publikováno v:
Machines
Volume 9
Issue 10
Machines, Vol 9, Iss 211, p 211 (2021)
Volume 9
Issue 10
Machines, Vol 9, Iss 211, p 211 (2021)
The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3efbfdb14553d78aaf16525ac6881c78
Autor:
Susheel Dharmadhikari, Amrita Basak
Publikováno v:
Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration.
In this paper, three distinct energy dissipation metrics are proposed to enable fatigue damage detection in aluminum specimens. The metrics are (i) Energy Dissipation Rate, (ii) Cumulative Energy Dissipation, and (iii) Material Stiffness. They are cr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47f19c456894ff6c2ca3e1043bd5db6d
https://doi.org/10.1115/gt2021-58787
https://doi.org/10.1115/gt2021-58787
Publikováno v:
ASME Letters in Dynamic Systems and Control. 1
Fatigue failure occurs ubiquitously in mechanical structures when they are subjected to cyclic loading well below the material’s yield stress. The tell-tale sign of a fatigue failure is the emergence of cracks at the internal or surface defects. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::723c616fc8c076b50c272e4cbff682b7
https://doi.org/10.1115/1.4050183
https://doi.org/10.1115/1.4050183
Publikováno v:
International Journal of Fatigue. 142:105922
This paper presents a dual-imaging methodology to investigate fatigue crack initiation & propagation along the lateral and transverse directions in Al7075 flat specimens having a one-sided V-notch. The framework consists of a confocal and a digital m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8c7a290e73f97afe3fa57f8d482e20a
https://doi.org/10.1016/j.ijfatigue.2020.105922
https://doi.org/10.1016/j.ijfatigue.2020.105922