Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Wenmeng Tian"'
Publikováno v:
Data in Brief, Vol 51, Iss , Pp 109722- (2023)
In-process thermal melt pool images and post-fabrication porosity labels are acquired for Ti-6Al-4V thin-walled structure fabricated with OPTOMEC Laser Engineered Net Shaping (LENS™) 750 system. The data is collected for nondestructive thermal char
Externí odkaz:
https://doaj.org/article/fbaff39c08c740f2b714c3792398048d
Autor:
Ayantha Senanayaka, Abdullah Al Mamun, Glenn Bond, Wenmeng Tian, Haifeng Wang, Sara Fuller, TC Falls, Shahram Rahimi, Linkan Bian
Publikováno v:
International Journal of Prognostics and Health Management, Vol 13, Iss 2 (2022)
This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, mo
Externí odkaz:
https://doaj.org/article/5f507fdef4f64e82ace348f0151a8512
Autor:
Seyyed Hadi Seifi, Aref Yadollahi, Wenmeng Tian, Haley Doude, Vincent H. Hammond, Linkan Bian
Publikováno v:
Advanced Intelligent Systems, Vol 3, Iss 12, Pp n/a-n/a (2021)
The presence of process‐induced internal defects (i.e., pores, microcracks, and lack‐of‐fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization t
Externí odkaz:
https://doaj.org/article/4c77b40b84e44ddabd1eb88459fe12f8
Autor:
Zamiela, Christian, Stokes, Ryan, Wenmeng Tian, Doude, Haley, Priddy, Matthew W., Linkan Bian
Publikováno v:
Journal of Manufacturing Science & Engineering; Aug2024, Vol. 146 Issue 8, p1-16, 16p
Publikováno v:
Manufacturing Letters. 36:86-90
Autor:
Abdullah Al Mamun, Mahathir Mohammad Bappy, Ayantha Senanayaka Mudiyanselage, Jiali Li, Zhipeng Jiang, Zhenhua Tian, Sara Fuller, T. C. Falls, Linkan Bian, Wenmeng Tian
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 124:1321-1334
Publikováno v:
IEEE Transactions on Automation Science and Engineering. 19:3338-3355
Publikováno v:
Journal of Manufacturing Systems. 64:648-656
Publikováno v:
Journal of Manufacturing Science and Engineering. 145
This study aims to develop an intelligent, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions by leveraging knowledge transfer from the existing process conditions. Conventional machine le
Autor:
Christian Zamiela, Zhipeng Jiang, Ryan Stokes, Zhenhua Tian, Anton Netchaev, Charles Dickerson, Wenmeng Tian, Linkan Bian
Publikováno v:
Journal of Manufacturing Science and Engineering. 145
We developed a deep fusion methodology of nondestructive in-situ thermal and ex-situ ultrasonic images for porosity detection in laser-based additive manufacturing (LBAM). A core challenge with the LBAM is the lack of fusion between successive layers