Heterogeneities-guided alloy design by and for 4D printing

HeteroGenius4D aims to develop tailored alloys for additive manufacturing by leveraging microstructural heterogeneities to enhance performance and enable 4D printing through integrated computational materials engineering.

Subsidie
€ 1.499.999
2024

Projectdetails

Introduction

Superior high-performance materials and CO2-free production technologies are key enablers to solving Europe’s current and future societal challenges [1]. In this context, additive manufacturing (AM) as one of the disruptive, green production technologies of our time “is expected to become a key manufacturing technology in the sustainable society of the future” [2].

Challenges in Additive Manufacturing

However, alloys specifically designed for AM are rarely available, which prohibits AM from reaching its full potential. In contrast to conventional alloys and processing, alloys processed by AM are highly microstructurally heterogeneous.

Objectives of HeteroGenius4D

The aim of HeteroGenius4D is to use the process-inherent conditions of a highly precise, bottom-up AM approach to tailor these heterogeneous structures (e.g. grains/phases and their boundaries and orientations, chemical gradients, etc.) locally and spatially on various length scales. This is the basis for the novel design concept of heterogeneities-guided alloy design for AM.

The potential to print local microstructures and properties in AM adds a 4th dimension to the design of 3D printed components; i.e., it enables 4D printing.

Research Methodology

AM-processed metals with an increasing degree of heterogeneity (from pure elements over solid solutions with chemical gradients to multi-phase alloys with further phases and gradients) are studied systematically.

The process-structure-properties-performance linkages are identified and quantified by:

  1. Combining high-throughput material synthesis (using extreme high-speed laser material deposition)
  2. Characterization with physics-based simulation tools

This approach enables a comprehensive integrated computational materials engineering (ICME) framework.

Data Utilization

The generated data serves as a basis for sophisticated data-driven (machine learning, ML) materials modeling and enables the establishment of an Experiments-ICME-ML optimal design approach for metal AM.

Generalization of Concepts

Finally, the concept of heterogeneities-guided alloy design is generalized and transferred to graded components.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 1.499.999
Totale projectbegroting€ 1.499.999

Tijdlijn

Startdatum1-4-2024
Einddatum31-3-2029
Subsidiejaar2024

Partners & Locaties

Projectpartners

  • TECHNISCHE UNIVERSITAT BERLINpenvoerder
  • RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN

Land(en)

Germany

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