Machine Learning for Additive Manufacturing and Hot Isostatic Pressing based on Quality Characteristics
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Beschreibung
Additive manufacturing (AM) using laser powder bed fusion (PBF-LB/M) has many advantages such as design freedom and sustainability. However, AM also suffers from internal defects that limit its functionality. Furthermore, post-treatment methods like hot isostatic pressing (HIP) promise to enhance material properties. This work aims to develop a machine learning model using artificial neural networks, to predict PBF-LB/M or HIP process parameters based on quality characteristics.
Autor*in
Natan Nudelis
Themen in »Machine Learning for Additive Manufacturing and Hot Isostatic Pressing based on Quality Characteristics«
Technische Universität München TU München TUM TUM.University Press Dissertation TUM series on materials engineering Chair of Materials Engineering of Additive Manufacturing TUM series on materials engineering, TUM School of Engineering and Design Materials Engineering of Additive Manufacturing Additive Manufacturing (AM) Hot Isostatic Pressing (HIP) Laser Powder Bed Fusion (PBF-LB/M) Metals AlSi10Mg Machine Learning