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Topic: Accelerating the Fusion Between Machine Learning and Additive Manufacturing

A special issue of Journal of Materials Informatics

ISSN 2770-372X (Online)

Submission deadline: 31 Oct 2022

Guest Editor(s)

  • Prof. Rongpei Shi

    School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong, China.

    Website | E-mail

  • Prof. Huiliang Wei
    School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

    Website | E-mail

  • Prof. Wentao Yan
    National University of Singapore, Singapore City, Singapore.

    Website | E-mail

  • Prof. Xingjun Liu
    School of Materials Science and Engineering and Institute of Materials Genome and Big Data, Harbin Institute of Technology, Shenzhen, Guangdong, China.

    Website | E-mail

Special Issue Introduction

Additive manufacturing (AM), also known as 3D printing, involves the layer-by-layer production of components. AM is emerging as an enabling technology for realizing innovative and rapid manufacturing of complex geometries with better control over site-specific mechanical properties and functionalities, which stands against traditional forming or subtractive manufacturing techniques that are more wasteful and could be constraining.

Progress so far has been limited by both the breadth and diversity of the processing parameter space and the inherent complexity of printing process. Selection of printing process and process variables results in an exceptional diversity of (and in turn, implications for the uncertainty) microstructure at multi-scales, properties, and defects that dictate the serviceability of the printed parts. Control of these attributes depends on the successful and efficient navigation of the processing-structure-property/functionality space landscapes, which remains challenging and thus necessitates a new methodology not commonly used in traditional manufacturing that relies on the knowledge base of metallurgy alone.

There is currently a push towards big data and artificial intelligence in materials research. AM is not immune to this phenomenon. The data science revolution is poised to transform the way AM is being practiced. Up to now, pairing machine learning (ML) and AM has proven to be particularly instrumental in pushing progress in various phases of building parts beyond metallic components, ranging from product design, process planning, to process monitoring and control, so as to help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification.

The current Focus issue is primarily designed as a platform for disseminating work relating to the fusion between ML and additive manufacturing. The issue aims to bring together leading experts in a diverse range of relevant fields for presenting the state of ML-driven AM research, exploring how ML is pushing forward AM research, what progress we can realistically expect, and what researchers should pay attention to in order to ensure their ML algorithm work the way they are designed to.

Scope of the Focus Issue
It is our pleasure to invite professionals from academic institutions, research centers, and industries around the world to submit their contributions to this Focus Issue. The main topics covered by this Focus Issue are scientific contributions on the following topics below:
1) Application of ML in AM
a) Optimization of process parameters
b) Process monitoring and control
c) Microstructure control
d) Defect mitigation
2) Modeling and Simulation of Process-Microstructure-Property Relationships for Additively Manufactured Materials
3) Modeling and Simulation for AM
4) Development of digital twin for AM
5) Hybrid models that combine mechanistic models and machine learning in AM
6) Creation of Surrogate model for AM component properties and performance prediction

The above list is not exhaustive, and papers on other topics associated with advances in AI-empowered AM are also welcome. We anticipate the breadth and diversity of AM methods and applications will provide a snapshot of some of the exciting research currently happening all over the globe.

Submission deadline: September 1st, 2022
Notification of first review: October 1st, 2022
Submission of revised manuscript: November 20th, 2022
Notification of final decision: November 25th, 2022

Submission Deadline

31 Oct 2022

Submission Information

Articles of special issue are free of charge for article processing.
For Author Instructions, please refer to
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Submission Deadline: 31 Oct 2022
Contacts: Lijun Jin, Managing Editor,

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