Zobrazeno 1 - 1
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pro vyhledávání: '"Femi Earnerstly"'
Autor:
Desmarita Leni, Yuda Perdana Kusuma, Muchlisinalahuddin Muchlisinalahuddin, Femi Earnerstly, Riza Muharni, Ruzita Sumiati
Publikováno v:
Rekayasa Mesin, Vol 14, Iss 2, Pp 611-626 (2023)
The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu,
Externí odkaz:
https://doaj.org/article/2596429f1bbb413993aac06ee46b7244
