000 01905nab a22002537a 4500
850 _aเอกสารภาษาไทย (ชั้น 5)
999 _c150669
_d150669
008 191030b2019 xxu||||| |||| 00| 0 eng d
099 _aRepr.M46
100 1 _aLee, Seulbi.
_9164208
245 1 0 _aData analytics approach for melt-pool geometries in metal additive manufacturing /
_cSeulbi Lee ...[et.al.].
500 _aสนใจติดต่อขอรับบริการเอกสารฉบับเต็มที่ one stop service (สำนักหอสมุดฯ) หรือ e-mail ([email protected]) โทร. 0 2201 7254-56, 0 2201 7287-89 (จัดเก็บชั้น 5 - Repr.M46)
518 _aYJ2019 M11
520 _aModern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of meltpool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction ofmelt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.
650 0 _aPowder bed fusion process.
_9164209
650 0 _aMelt-poo.
_9164210
650 0 _aSingle track.
_9164211
650 0 _aMachine learning.
_9164212
650 0 _aCorrelation analysis.
_9164213
700 1 _aPeng, Jian.
_9164214
700 1 _aShin, Dongwon.
_9164215
700 1 _aChoi, Yoon Suk.
_9164216
773 _gScience and Technology of Advanced Materials
_t20 (1)2019:972-978
_x1468-6996
942 _2lcc