Simple method to construct process maps for additive manufacturing using a support vector machine

Aoyagi, Kazuhiko; Wang, Hao; Sudo, Hideki; Chiba, Akihiko

doi:10.1016/j.addma.2019.03.013

Cited by 95 publications

(56 citation statements)

References 52 publications

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“…In traditional 3D printing processes, Aoyagi et al . [ 17 ] proposed a method to construct a process map for 3D printing using a support vector machine. This method can predict a process condition that is effective for manufacturing a product with low pore density.…”

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%

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A Perspective on Using Machine Learning in 3D Bioprinting

Jiang

2020

IJB

151

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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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Section: Perspective On Using Machine Learning In Bioprintingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Perspective on Using Machine Learning in 3D Bioprinting

Jiang

2020

IJB

151

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“…Generally, machine learning can be used in medical diagnosis, image processing, prediction, classification, etc. Recently, research on using machine learning in AM has also been published for AM process optimization [ 86 , 87 , 88 , 89 , 90 , 91 ], dimensional accuracy analysis [ 92 , 93 , 94 , 95 ], manufacturing defect detection [ 96 , 97 , 98 ] and material property prediction [ 99 , 100 , 101 ]. However, machine learning has not been applied to improving path planning strategies yet.…”

Section: Future Perspectivesmentioning

confidence: 99%

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review

Jiang

2020

Micromachines

216

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Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models. Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing. Path planning in AM is an important step in the process of manufacturing products. The final fabricated qualities, properties, etc., will be different when using different path strategies, even using the same AM machine and process parameters. Currently, increasing research studies have been published on path planning strategies with different aims. Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic. Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning. This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties. The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future.

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“…S-RL can adaptively extract corresponding fault features from original data. In [32], Aoyagi et al proposed a simple method to construct process maps for additive manufacturing using SVM. They also found that the value of a decision function in SVM had a physical meaning which may be a semi-quantitative guideline for porosity density of parts fabricated by additive manufacturing.…”

Section: The State Of the Artmentioning

confidence: 99%

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

Yan

2020

Electronics

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The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.

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Simple method to construct process maps for additive manufacturing using a support vector machine

Cited by 95 publications

References 52 publications

A Perspective on Using Machine Learning in 3D Bioprinting

A Perspective on Using Machine Learning in 3D Bioprinting

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

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