Publication Type

Journal Article

Version

acceptedVersion

Publication Date

11-2020

Abstract

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and performance spaces. Furthermore, the primary advantage of ML over other surrogate modelling methods is the capability to model input–output relationships in both directions. That is, a deep neural network can model property–structure relationships, given structure–property input–output data. A case study was carried out to demonstrate the effectiveness of using ML to design a customized ankle brace that has a tunable mechanical performance with tailored stiffness.

Keywords

Additive manufacturing, Design for AM, Machine learning

Discipline

Artificial Intelligence and Robotics | Software Engineering

Publication

Journal of Intelligent Manufacturing

Volume

33

Issue

4

First Page

1073

Last Page

1086

ISSN

0956-5515

Identifier

10.1007/s10845-020-01715-6

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s10845-020-01715-6

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