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      Control of grain structure, phases, and defects in additive manufacturing of high-performance metallic components

      , , , , , ,
      Progress in Materials Science
      Elsevier BV

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          Additive manufacturing (3D printing): A review of materials, methods, applications and challenges

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            Machine learning: Trends, perspectives, and prospects.

            Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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              Additive manufacturing of metallic components – Process, structure and properties

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                Author and article information

                Contributors
                Journal
                Progress in Materials Science
                Progress in Materials Science
                Elsevier BV
                00796425
                September 2023
                September 2023
                : 138
                : 101153
                Article
                10.1016/j.pmatsci.2023.101153
                06c32ad7-2b74-46cd-a49d-8829800d3ee6
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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