Inverse design of nano-sized FGM phononic crystals with anticipated band gaps using probabilistic generation based deep-learning network

Li, Jingru; Yin, Jingming; Li, Sheng; Zhang, Zhifu; Liu, Xiaomei

doi:10.1016/j.engstruct.2024.118222

Engineering Structures

2024

DOI: 10.1016/j.engstruct.2024.118222

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Inverse design of nano-sized FGM phononic crystals with anticipated band gaps using probabilistic generation based deep-learning network

Jingru Li,

Jingming Yin,

Sheng Li

et al.

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Machine Learning for Additive Manufacturing of Functionally Graded Materials

Karimzadeh,

Basvoju,

Vakanski

et al. 2024

Materials

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Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

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Machine Learning for Additive Manufacturing of Functionally Graded Materials

Karimzadeh,

Basvoju,

Vakanski

et al. 2024

Materials

View full text Add to dashboard Cite

show abstract

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Inverse design of nano-sized FGM phononic crystals with anticipated band gaps using probabilistic generation based deep-learning network

Cited by 1 publication

References 74 publications

Machine Learning for Additive Manufacturing of Functionally Graded Materials

Machine Learning for Additive Manufacturing of Functionally Graded Materials

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