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Digitisation of metal AM for part microstructure and property control

Doğu, Merve Nur orcid logoORCID: 0000-0003-1843-6040, McCarthy, Éanna, McCann, Ronán orcid logoORCID: 0000-0002-2071-0785, Mahato, Vivek, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545, Bambach, Markus, Ahad, Inam Ul orcid logoORCID: 0000-0002-3802-6156 and Brabazon, Dermot orcid logoORCID: 0000-0003-3214-6381 (2022) Digitisation of metal AM for part microstructure and property control. International Journal of Material Forming, 15 . ISSN 1960-6206

Metal additive manufacturing, which uses a layer-by-layer approach to fabricate parts, has many potential advantages over conventional techniques, including the ability to produced complex geometries, fast new design part production, personalised production, have lower cost and produce less material waste. While these advantages make AM an attractive option for industry, determining process parameters which result in specific properties, such as the level of porosity and tensile strength, can be a long and costly endeavour. In this review, the state-of-the-art in the control of part properties in AM is examined, including the effect of microstructure on part properties. The simulation of microstructure formation via numerical simulation and machine learning is examined which can provide process quality control and has the potential to aid in rapid process optimisation via closed loop control. In-situ monitoring of the AM process, is also discussed as a route to enable first time right production in the AM process, along with the hybrid approach of AM fabrication with post-processing steps such as shock peening, heat treatment and rolling. At the end of the paper, an outlook is presented with a view towards potential avenues for further research required in the field of metal AM.
Item Type:Article (Published)
Additional Information:Article number: 30
Uncontrolled Keywords:Additive Manufacturing; Powder Bed Fusion; Selective laser melting; Industry 4.0; Smart manufacturing; Numerical modelling; Monitoring; Quality control; Process control
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Engineering > Materials
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Mechanical and Manufacturing Engineering
Research Institutes and Centres > I-Form
Official URL:https://doi.org/10.1007/s12289-022-01686-4
Copyright Information:© 2021 The Authors.
Funders:Science Foundation Ireland (SFI) under Grant Numbers 16/1571 RC/3872 and 19/US-C2C/3579 and is co-funded under the European Regional Development Fund., Open Access funding provided by the IReL Consortium
ID Code:27065
Deposited On:26 Apr 2022 11:29 by Annalina Caputo . Last Modified 23 Mar 2023 16:07

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