Deep Learning and Design for Additive Manufacturing: A Framework for Microlattice Architecture

Després, Nathaniel; Cyr, Edward; Setoodeh, Peyman; Mohammadi, Mohsen

doi:10.1007/s11837-020-04131-6

Cited by 21 publications

(4 citation statements)

References 17 publications

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“…The approach's efficacy was validated using an MBB-beam design test case [39]. Concurrently, another study used an Autoencoder model to predict the mechanical characteristics of microlattices and generate new structures based on defined mechanical criteria [40]. Additionally, neural networks have been applied in material science to correlate the design of regular triangular lattices with their elastic properties, thereby enabling architectural lattices with customizable anisotropy [41].…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

Hussain,

Lee,

Tsang

et al. 2024

Preprint

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Triply Periodic Minimal Surface (TPMS) lattice structures are utilized in diverse fields such as engineering, material design, and biomedical. The use of appropriate TPMS lattice structures in 3D printing can obtain benefits in terms of production efficiency and material reduction towards a greener 3D printing process. However, there is a lack of an automated solution to suggest the appropriate TPMS lattice structure parameters, such that unnecessary material wastage cannot be neglected in the existing practices. To address the above challenges, this study proposes a machine learning-based recommendation framework for generating the TPMS lattice structures based on the engineering requirements. First, we compiled a dataset by producing 144 samples via the material extrusion (ME) technique and conducted compression tests on four TPMS lattice structures (Diamond, Gyroid, Schwarz, and split-P), each with varying parameters, fabricated using Polylactic acid (PLA) material. Second, we train four machine learning algorithms (K-Nearest Neighbors, Decision Tree, Random Forest, and Bayesian Regression) on this dataset to predict TPMS lattice structure (unit cell type, unit cell size, and wall thickness). Extensive experiments assess algorithm performance using R-squared values and Root Mean Square Error (RMSE) as evaluation measures. Our results indicate that the Random Forest and Decision Tree algorithms perform best, achieving R-squared scores of 0.9694 and 0.9689, along with RMSE values of 0.1180 and 0.0795, respectively. This work not only advances the field's understanding of automated selection for TPMS lattice structures but also holds noteworthy implications for eco-design and eco-innovation, particularly in the realm of sustainable and efficient green 3D printing applications.

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Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

Hussain,

Lee,

Tsang

et al. 2024

Preprint

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“…A composite material part's geometry and tool path are reversed engineered using CT scan images in an RNN [80]. Similarly, graphic data from lattice designs is also used to predict design-oriented targets [76,81]. Cases, where 3D data is used to predict design characteristics, are found in the literature as well.…”

Section: Design Characteristicsmentioning

confidence: 99%

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

et al. 2022

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Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications on AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods and suggestions are given.

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“…This is a new paradigm in design-driven build of customized products. Machine learning and deep learning can be adopted for both analysis and design of microlattices, which can be fabricated using additive manufacturing techniques [9]. Figure 4 shows an example of additive manufacturing [10].…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning in Manufacturing

Sadiku¹,

Suman²,

Musa³

2021

IJASRE

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Deep Learning and Design for Additive Manufacturing: A Framework for Microlattice Architecture

Cited by 21 publications

References 17 publications

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

Deep Learning in Manufacturing

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