Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network

Jiang, Jingchao; Hu, Guobiao; Xiao, Li; Xu, Xun; Zheng, Pai; Stringer, Jonathan

doi:10.1080/17452759.2019.1576010

Cited by 97 publications

(27 citation statements)

References 33 publications

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“…Different slicing parameters will affect the final printed properties. We previously [13][14][15][16][17] also studied different parameters' effects on printable overhang and bridge features. We also [18][19][20] proposed some new printing methods via improving path planning process.…”

Section: Introductionmentioning

confidence: 99%

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Jiang¹,

Xu²,

Rui³

et al. 2021

E3S Web Conf.

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Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. However, in current FDM processes, it is difficult to fabricate parts with highly accurate dimensions. One of the reasons is due to the slicing process of 3D models. Current slicing software divides the parts into layers and then lines (paths) based on a fixed value. However, in a real printing process, the printed line width will change when the process parameters are set in different values. The various printed widths may result in inaccuracy of printed dimensions of parts if using a fixed value for slicing. In this paper, a mathematical model is proposed to predict the printed line width in different layer heights. Based on this model, a method is proposed for calculating the optimal width value for slicing 3D parts. In the future, the proposed mathematical model can be integrated into slicing software to slice 3D models for precision additive manufacturing.

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Section: Introductionmentioning

confidence: 99%

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Jiang¹,

Xu²,

Rui³

et al. 2021

E3S Web Conf.

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“…Currently, Jiang et al . [ 32 ] used backpropagation neural network to analyze and predict printable bridge length[ 38 ] in FDM processes.…”

Section: Perspective On Using Machine Learning In Bioprintingmentioning

confidence: 99%

“…In terms of 3D printing processes, machine learning can lead to a reduction of fabrication time, minimized cost, and increased quality. In literature, machine learning has already been applied to process optimization[ 17 - 21 ], dimensional accuracy analysis[ 22 - 25 ], manufacturing defect detection[ 26 - 28 ], and material property prediction[ 29 - 32 ]. However, machine learning has not been applied in 3D bioprinting yet.…”

Section: Introductionmentioning

confidence: 99%

A Perspective on Using Machine Learning in 3D Bioprinting

Jiang

2020

ijb

Self Cite

120

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Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.

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“…Taking the calculation results of each sub-model as sample points, the radial basis function neural network is used to generate the target response surface. Neural network has been widely used in many fields for its advantage of cost saving [26]. In present study, 5000 initial sample points are generated in the design variable space for training and verification.…”

Section: Decoupling Approximate Response Analysis Of Design Parametersmentioning

confidence: 99%

Coupling characteristic analysis of ship shafting design parameters and research on multidisciplinary design optimization

Yin¹,

Liu²,

Zeng³

2020

J. vibroeng.

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In the design process of traditional ship shafting, the design quality is generally hard to get guaranteed for the lack of discipline coupling. In this paper, Multidisciplinary Design Optimization (MDO) is innovatively introduced to ensure design quality. Multidisciplinary decomposition of shafting can help to construct the MDO model of ship shafting based on multidisciplinary feasibility method. Then the sub-discipline model of shifting design can be further established, including calibration neutron discipline model, whirling vibration model, and dynamic stiffness of radial oil film bearing model. Collaborative operation is implemented by the multidisciplinary model of shifting to obtain the experimental results. Based on Radial Basis Function (RBF) neural network, the responsive surfaces of variable, bearing load, and support stiffness can be constructed, in the meanwhile the dynamic stiffness decoupling of vibration model can be obtained. Fireworks algorithm is used to establish multidisciplinary optimization of seven-dimensional design variable. The results show that MDO helps improve the quality of shafting alignment and whirling vibration. The work in present paper also provides insight for the future optimization of research methods, design quality, and engineering experiments.

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Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network

Cited by 97 publications

References 33 publications

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

Line Width Mathematical Model in Fused Deposition Modelling for Precision Manufacturing

A Perspective on Using Machine Learning in 3D Bioprinting

Coupling characteristic analysis of ship shafting design parameters and research on multidisciplinary design optimization

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