Analysis of sinter-based additive manufactureing for functional products
Publikationstyp
Doctoral Thesis
Date Issued
2026
Sprache
English
Author(s)
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2026-03-27
Citation
Springer 978-3-032-28432-7: (2026)
Publisher
Springer
ISBN
978-3-032-28433-4
978-3-032-28432-7
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. Key topics include Systematic analysis and optimization of debinding and sintering processes for metallic materials Development of design guidelines tailored to sinter-based additive manufacturing Integration of machine learning methods for process prediction and quality assurance Characterization of mechanical properties and dimensional accuracy of printed functional components Practical strategies for achieving reliable, load-bearing metal parts through MEX/M 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. The author 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.
Subjects
Engineering design
Industrial engineering
Production engineering
Materials
DDC Class
620: Engineering