Machine learning-driven 3D printing: A review

Zhang, Xijun; Chu, Dianming; Zhao, Xinyue; Gao, Chenyu; Lu, Lingxiao; He, Yan; Bai, Wenjuan

doi:10.1016/j.apmt.2024.102306

Applied Materials Today

2024

DOI: 10.1016/j.apmt.2024.102306

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Machine learning-driven 3D printing: A review

Xijun Zhang,

Dianming Chu,

Xinyue Zhao

et al.

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2024

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Cited by 4 publications

References 106 publications

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Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

Lu,

Yuan,

Xie

et al. 2024

Virtual and Physical Prototyping

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Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

Lu,

Yuan,

Xie

et al. 2024

Virtual and Physical Prototyping

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Effects of Anisotropic Mechanical Behavior on Nominal Moment Capability of 3D Printed Concrete Beam with Reinforcement

Park,

Memari,

Hojati

et al. 2024

Buildings

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In this study, 3D-printed reinforced concrete beams were tested for flexural performance and compared with the analytical model based on the material test results. Two cementitious mixes (PSU and GCT) were designed for concrete printing and were mechanically tested and compared. Anisotropies in the compressive strength and modulus of elasticity of printed concrete were observed, applied to the analytical prediction of flexural bending behavior, and validated by actual test results. Significant differences between analytical predictions and experimental tests of the bending behaviors of the printed concrete beams were observed. Furthermore, higher compressive strengths and moduli of elasticity were observed when the loading direction was perpendicular to the printed layers or with the PSU mix. The effect of anisotropic mechanical properties on a reinforced beam was compared to the flexural bending tests for both mixes. The analytical model based on the material test results was compared to the flexural bending test results. The significant errors in the prediction of printed concrete’s structural performance, from 10% to 50%, suggest that factors other than reduced compressive strengths may influence the structural behaviors of printed concrete beams.

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Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

Shan,

Gao,

Rao

et al. 2024

Metals

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Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R2) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality.

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Machine learning-driven 3D printing: A review

Cited by 4 publications

References 106 publications

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

Effects of Anisotropic Mechanical Behavior on Nominal Moment Capability of 3D Printed Concrete Beam with Reinforcement

Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

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