FDM manufactured auxetic structures: An investigation of mechanical properties using machine learning techniques

Vyavahare, Swapnil; Teraiya, Soham; Kumar, Shailendra

doi:10.1016/j.ijsolstr.2023.112126

Cited by 10 publications

(7 citation statements)

References 52 publications

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“…Chang et al [111], Dong et al [81] and Liu et al [112] developed MLP models and then accelerated the following inverse design using genetic algorithm (GA) optimization for re-entrant structure, cross-chiral metamaterial and auxetic metamaterials with peanut-shaped pores. Other than GA, Vyavahare et al [20] applied the gray relational analysis technique to improve flexural responses and to reduce the weight and fabrication time of their auxetic metamaterials. They also demonstrated that a deeper MLP exhibits advantages in modeling material defects.…”

Section: Neural Network Based Modelsmentioning

confidence: 99%

“…To validate the quality of simulated data, some work [19,81,105,111,112,156,157] did real-world experiments to test the consistency between real-world and simulated data. Only the work of Vyavahare et al [20] applied thirty-two real-world specimens to train their MLP due to the lack of a well-established simulation for the fused deposition modeling (FDM) process. They investigated the effect of design factors (angle, width, and length of arm) of FDM-manufactured auxetic unit cells on their mechanical responses.…”

Section: Data Collectionmentioning

confidence: 99%

“…For example, Zhang et al [19] have developed surrogate models to predict the behavior of auxetic sleeve robots, replacing non-linear dynamic simulations and saving the evaluation time from minutes to instant. Vyavahare et al [20] have replaced the fabrication and testing process, which takes hours for one round, of 3D printed auxetic beams with surrogate models trained on experimental data. As experimental data is accurate but expensive, Hu et al [21] have transferred their surrogate model from a simulated dataset to a real experimental dataset, which provides a solution for both data availability and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A critical review on the application of machine learning in supporting auxetic metamaterial design

Zhang,

Zhao

2024

J. Phys. Mater.

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The progress of machine learning (ML) in the past years has opened up new opportunities to the design of auxetic metamaterials. However, successful implementation of ML algorithms remains challenging, particularly for complex problems such as domain performance prediction and inverse design.In this paper, we first reviewed classic auxetic designs and summarized their variants in different applications. The enormous variant design space leads to challenges using traditional design or topology optimization.Therefore, we also investigated how ML techniques can help address design challenges of auxetic metamaterials and when researchers should deploy them. The theories behind the techniques are explained, along with practical application examples from the analysed literature. The advantages and limitations of different ML algorithms are discussed and trends in the field are highlighted. Finally, two practical problems of ML-aided design, design scales and data collection are discussed.

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Section: Neural Network Based Modelsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A critical review on the application of machine learning in supporting auxetic metamaterial design

Zhang,

Zhao

2024

J. Phys. Mater.

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“…The prediction accuracy, indicated by a root mean square error, ranged from about 11% to 16% across two different models [36]. Swapnil et al [37] conducted an empirical investigation into re-entrant auxetic structures using FDM, developing regression models and neural networks for prediction. Their trained DNN models exhibited deviations in predicting strength, stiffness, and specific energy absorption (SEA) for ABS and PLA materials.…”

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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“…[25][26][27] The mechanical characteristics of the 3D printed components vary based on the AM processes and the printing parameters adopted. [28][29][30][31][32][33] In this study, two thermoplastic AM processes, MJF and FFF, were employed to fabricate an auxetic hybrid structure called re-entrant chiral auxetic (RCA) structure. To the best of the authors' knowledge, this study presents novel contributions on the dependence of the mechanical properties of auxetic cellular materials on the fusing techniques of the FFF and MJF 3D printing processes.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Manufacturing Techniques on the Mechanical Performance of an Auxetic Structure Manufactured by Fused Filament Fabrication and Multijet Fusion Processes

Alomarah,

Abbas,

Faisal

et al. 2024

Adv Eng Mater

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Additive manufacturing is increasingly becoming potential for short‐run part production. The aim of this work is to explore the mechanical properties of a hybrid auxetic structure fabricated by multijet fusion (MJF) and fused filament fabrication (FFF). The experimental measurements indicate that robust specimens with high‐dimensional accuracy are successfully achieved using MJF process. In the case of the FFF process, the walls–walls and walls–cylinders areas (connecting areas) suffer large pores, indicating poor printing quality. The 3D printed specimens via FFF process reveal smooth plateau stress, while plateau stress with high peaks is observed when the MJF specimens are compressed along the Y‐axis. Moreover, specimens manufactured by the MJF process reveal the highest specific energy absorption (SEA) per unit volume and per unit mass. The calculated values of SEA are 2.1 and 2.5 J g−1 (for specimens fabricated by MJF) in which they significantly outperform the SEA (0.495 and 0.480 J g−1) of those manufactured by the FFF process. Furthermore, the trend of auxetic features (negative Poisson's ratio) has not been affected by the manufacturing processes. This study sheds light on the influence of fabrication methods on the mechanical properties of cellular materials especially their ability to absorb energy.

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FDM manufactured auxetic structures: An investigation of mechanical properties using machine learning techniques

Cited by 10 publications

References 52 publications

A critical review on the application of machine learning in supporting auxetic metamaterial design

A critical review on the application of machine learning in supporting auxetic metamaterial design

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

The Effects of Manufacturing Techniques on the Mechanical Performance of an Auxetic Structure Manufactured by Fused Filament Fabrication and Multijet Fusion Processes

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