Estimating Long Term Behaviour Of DED-printed AlCoNiFe Alloy

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

We present a workflow and model prediction of a behaviour for an alloy designed for an application, which require high strength materials, with multiscale material modelling method. Material is designed to have suitable phase composition with CALPHAD-method and neural network tool that is taught with the empirical high entropy alloy design criteria. The material is estimated to be two phase (FCC-BCC) structure in as-build condition and after heat treatment gamma-gamma’ and BCC-B2 structure. Designed alloy is atomized and test specimens are produced with direct energy deposition method. Specimens are heat-treated to get the desired phase composition. Tensile tests and micromechanical characterization are combined with simulation tools to create a micromechanical model that is used for mechanical property and performance simulations. A workflow to combine the different methods in order to assess the performance of the material.
Original languageEnglish
Title of host publicationEuro PM2023 Proceedings
PublisherEuropean Powder Metallurgy Association (EPMA)
ISBN (Electronic)978-1-899072-57-6
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
EventEuro Powder Metallurgy 2023 Congress & Exhibition, Euro PM2023 - Lisbon, Portugal
Duration: 1 Oct 20234 Oct 2023

Conference

ConferenceEuro Powder Metallurgy 2023 Congress & Exhibition, Euro PM2023
Abbreviated titlePM 2023
Country/TerritoryPortugal
CityLisbon
Period1/10/234/10/23

Funding

This research has received funding from the European Union's Horizon 2020 research and in-novation programme under theproject ACHIEF for the discovery of novel materials to be used in industrial processes with Grant Agreement 958374.

Fingerprint

Dive into the research topics of 'Estimating Long Term Behaviour Of DED-printed AlCoNiFe Alloy'. Together they form a unique fingerprint.

Cite this