文献
J-GLOBAL ID:202202274367135171
整理番号:22A0740081
付加製造Inconel718合金の密度と欠陥の機械学習可能予測【JST・京大機械翻訳】
Machine learning-enabled prediction of density and defects in additively manufactured Inconel 718 alloy
著者 (5件):
Sah Aman Kumar
(Department of Fuel, Minerals and Metallurgical Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, Jharkhand, India)
,
Agilan M.
(Materials and Mechanical Entity, Vikram Sarabhai Space Centre, Indian Space Research Organisation, Thiruvananthapuram 695022, Kerala, India)
,
Dineshraj S.
(Materials and Mechanical Entity, Vikram Sarabhai Space Centre, Indian Space Research Organisation, Thiruvananthapuram 695022, Kerala, India)
,
Rahul M.R.
(Department of Fuel, Minerals and Metallurgical Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, Jharkhand, India)
,
Govind B.
(Materials and Mechanical Entity, Vikram Sarabhai Space Centre, Indian Space Research Organisation, Thiruvananthapuram 695022, Kerala, India)
資料名:
Materials Today Communications
(Materials Today Communications)
巻:
30
ページ:
Null
発行年:
2022年
JST資料番号:
W3060A
ISSN:
2352-4928
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)