| 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 | ||