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

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Abstract

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.

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Acknowledgements

This work was supported by the Quality Made program led by Lockheed Martin under prime contract N00014-18-C-1026 from the Office of Naval Research. Andrew Huck, Glynn P. Adams, Edward A. Pierson, Prof. Peter Collins, Brandon Abranovic, Elizabeth Chang-Davidson, and Prof. Jack Beuth are thanked for fruitful discussions. Furthermore, collaborators from Colorado School of Mines, Prof. Branden Kappes (now at KMMD.io), Prof. Craig Brice, and Brandon King, for providing part of the data studied in this article.

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Authors and Affiliations

  1. Department of Material Science and Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15216, USA

    Zhening Yang, Amit K. Verma, Lonnie Smith, Ali Guzel, P. Christiaan Pistorius & Anthony D. Rollett

  2. Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, 92697, USA

    Hangman Chen

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  1. Zhening Yang
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Correspondence to Amit K. Verma.

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Yang, Z., Verma, A.K., Smith, L. et al. Predicting Melt Pool Dimensions for Wire-Feed Directed Energy Deposition Process. Integr Mater Manuf Innov 11, 532–544 (2022). https://doi.org/10.1007/s40192-022-00278-z

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