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.

• 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.

• 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.

• 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.