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WEB Optimal design for metal additive manufacturing: An integrated computational materials engineering (ICME) approach

Wednesday (01.01.2020)
00:00 - 00:15
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We present an approach for performance-oriented optimal design for metal additive manufacturing (AM). It involves computationally linking the process-structure-properties-performance (PSPP) chain using a multi-scale and multi-physics integrated computational materials engineering (ICME) approach.

In this approach, the space of design parameters (design space) consists of alloy composition, process parameters and macroscopic geometry of the structure/component, with the design objective being the in-service performance of the final component. The performance depending on the thermo-chemo-mechanical (TCM) service load may include multiple functional aspects, such as specific energy absorption capacity, fatigue strength/life, high temperature strength, creep resistance, erosion/wear resistance and/or corrosion resistance. The TCM processing fields, microstructure and (macroscopic) TCM material properties are treated as design internal/hidden variables, which are directly affected by the design parameters and determine the performance of the final product.

The abundance of design parameters and complex relationship between those and the performance of AM components has been the biggest impediment for the widespread adoption of metal AM technologies for structurally critical load-bearing components. To unlock the full potential of metal AM, establishing a full quantitative PSPP linkage is essential. It will not only help understanding the underlying physical processes but also serves as a powerful and effective tool for optimal computational design. We illustrate an example of ICME-based PSPP linkage in metal AM along with a hybrid physics-based data-driven strategy for its application in optimal design of a lattice structure based on its performance (specific energy absorption capacity).

S. Amir H. Motaman
RWTH Aachen University
Additional Authors:
  • Patrick Köhnen
    RWTH Aachen University
  • Fabian Kies
    RWTH Aachen University
  • Dr. Andrey Molotnikov
    RMIT University
  • Dr. Christian Haase
    RWTH Aachen University