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Machine learning integrated design for additive manufacturing

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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.

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Acknowledgements

This research was funded by Digital Manufacturing and Design (DManD) Research Center at the Singapore University of Technology and Design supported by the Singapore National Research Foundation.

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Authors and Affiliations

  1. Digital Manufacturing and Design Center, Singapore University of Technology and Design, Singapore, Singapore

    Jingchao Jiang, Yi Xiong, Zhiyuan Zhang & David W. Rosen

  2. The G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    David W. Rosen

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  1. Jingchao Jiang
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  2. Yi Xiong
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Correspondence to David W. Rosen.

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Jiang, J., Xiong, Y., Zhang, Z. et al. Machine learning integrated design for additive manufacturing. J Intell Manuf 33, 1073–1086 (2022). https://doi.org/10.1007/s10845-020-01715-6

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