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      Data analytics approach for melt-pool geometries in metal additive manufacturing

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          ABSTRACT

          Modern 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 melt-pool 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 of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.

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          A comparison of methods for multiclass support vector machines.

          Chih-Wei Hsu, Chih-Jen Lin (2002)
          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones

            Saad A. Khairallah, Andrew Anderson, Alexander Rubenchik … (2016)
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              In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing

              Chu Lun Alex Leung, Sebastian Marussi, Robert Atwood … (2018)
              The laser–matter interaction and solidification phenomena associated with laser additive manufacturing (LAM) remain unclear, slowing its process development and optimisation. Here, through in situ and operando high-speed synchrotron X-ray imaging, we reveal the underlying physical phenomena during the deposition of the first and second layer melt tracks. We show that the laser-induced gas/vapour jet promotes the formation of melt tracks and denuded zones via spattering (at a velocity of 1 m s−1). We also uncover mechanisms of pore migration by Marangoni-driven flow (recirculating at a velocity of 0.4 m s−1), pore dissolution and dispersion by laser re-melting. We develop a mechanism map for predicting the evolution of melt features, changes in melt track morphology from a continuous hemi-cylindrical track to disconnected beads with decreasing linear energy density and improved molten pool wetting with increasing laser power. Our results clarify aspects of the physics behind LAM, which are critical for its development.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Technol Adv Mater
                Journal ID (iso-abbrev): Sci Technol Adv Mater
                Journal ID (publisher-id): TSTA
                Journal ID (publisher-id): tsta20
                Title: Science and Technology of Advanced Materials
                Publisher: Taylor & Francis
                ISSN (Print): 1468-6996
                ISSN (Electronic): 1878-5514
                Publication date Collection: 2019
                Publication date (Electronic): 25 September 2019
                Volume: 20
                Issue: 1
                Pages: 972-978
                Affiliations
                [a ]School of Materials Science and Engineering, Pusan National University , Busan, Korea
                [b ]Materials Science and Technology Division, Oak Ridge National Laboratory , Oak Ridge, TN, USA
                Author notes
                CONTACT Yoon Suk Choi choiys@ 123456pusan.ac.kr School of Materials Science and Engineering, Pusan National University , Busan, Korea
                Author information
                http://orcid.org/0000-0002-5797-3423
                Article
                Publisher ID: 1671140
                DOI: 10.1080/14686996.2019.1671140
                PMC ID: 6818108
                SO-VID: bebd3e1a-f8d8-48c0-ae09-f1232f9b38ce
                Copyright © © 2019 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 July 2019
                Date revision received : 19 September 2019
                Date accepted : 19 September 2019
                Page count
                Figures: 5, Tables: 2, References: 27, Pages: 7
                Funding
                Funded by: Ministry of Trade, Industry and Energy 10.13039/501100003052
                Award ID: 20000201
                Funded by: Ministry of Trade, Industry and Energy 10.13039/501100003052
                Award ID: 10077677
                This research was supported by the Industrial Strategic Technology Development Program [10077677] and the Technology Innovation Program [20000201] funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).
                Categories
                Subject: Engineering and Structural material

                Keywords: powder bed fusion (pbf) process,melt-pool,single track,machine learning,correlation analysis,106 metallic materials,404 materials informatics / genomics
                Data availability:
                Keywords: powder bed fusion (pbf) process, melt-pool, single track, machine learning, correlation analysis, 106 metallic materials, 404 materials informatics / genomics

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