Predicting Melt Pool Dimensions for Wire-Feed Directed Energy Deposition Process

被引：4

作者：

Yang, Zhening ^{[1
]}

Verma, Amit K. ^{[1
]}

Smith, Lonnie ^{[1
]}

Guzel, Ali ^{[1
]}

Chen, Hangman ^{[2
]}

Pistorius, P. Christiaan ^{[1
]}

Rollett, Anthony D. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Dept Mat Sci & Engn, 5000 Forbes Ave, Pittsburgh, PA 15216 USA

[2] Univ Calif Irvine, Dept Mech & Aerosp Engn, Irvine, CA 92697 USA

来源：

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION | 2022年 / 11卷 / 04期

关键词：

Regression analysis; Directed energy deposition; Ti-6Al-4V; Melt pool shape;

D O I：

10.1007/s40192-022-00278-z

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Additive manufacturing (AM) is gaining attention because of its ability to design complex geometries. Direct energy deposition (DED), one of the AM processes, is widely used nowadays for its high deposition rate. When using DED process in manufacturing or repairing, it is important to know the melt pool dimensions as a function of processing parameters to obtain high deposition rate and avoid defects such as lack of fusion. In this study, we used the random forest (RF) algorithm to predict melt pool dimensions and compared the results against existing physics based lumped model by Doumanidis et al. [1]. The results show that RF model works well to predict the DED melt pool dimensions, where energy density and material volume deposited govern the dimensions. Further, we tested the ubiquitous semi-ellipsoidal shape assumption for DED cross section against the circular shape and found semi-ellipsoidal shape to be fair when deposition process is stable and free of defects. Overall, this study highlights the applicability of machine learning algorithms for small AM datasets.

引用

页码：532 / 544

页数：13

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