Asami | Analysis of Sinter-Based Additive Manufacturing for Functional Products | E-Book | www.sack.de
E-Book

E-Book, Englisch, 214 Seiten, Web PDF

Reihe: Engineering

Asami Analysis of Sinter-Based Additive Manufacturing for Functional Products


1. Auflage 2026
ISBN: 978-3-032-28433-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 214 Seiten, Web PDF

Reihe: Engineering

ISBN: 978-3-032-28433-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark



Sinter-based additive manufacturing enables the production of complex metallic components — but the path from digital design to reliable functional parts remains fraught with challenges. This book bridges the gap between research and industrial application by providing a systematic framework for the entire material extrusion (MEX/M) process chain. The book offers engineers, researchers, and graduate students a structured methodology to understand, control, and optimize the MEX/M process — from material selection and printing parameters to post-processing and final part qualification. By combining experimental investigation with data-driven approaches, it provides actionable insights for both academic research and industrial implementation in lightweight engineering and beyond.

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Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


Introduction.- State of the art.- Research hypotheses, methodology, and investigation approach.- Material and process parameter analysis.- Design analysis.- Machine learning for the prediction of quality parameters.- Manufacturing of the functional products.- Multi-material approach in the MEX/M process.- Evaluation, conclusions, and outlook.


Mohammad Karim Asami began his research career at the Institute for Laser and System Technologies (iLAS) at Hamburg University of Technology (TUHH), where he led a research group dedicated to sinter-based additive manufacturing. He subsequently joined the Fraunhofer Research Institution for Additive Production Technologies (IAPT) as group leader, driving the transfer of scientific findings into industrial application. His expertise spans process chain optimization, material characterization, and machine learning in production engineering.



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