In-Process monitoring of porosity during laser additive manufacturing process

Zhang, Bin; Liu, Shunyu; Shin, Yung C.

doi:10.1016/j.addma.2019.05.030

Cited by 163 publications

(75 citation statements)

References 27 publications

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“…This is done to investigate on the cross-validation technique that yields the best performing feature test set [29]. This is in-line with literature stating that cross-validation analysis is important to construct a simplified network that provides high performance [30]. Number of CNN layers such as 3, 5 and 7 are selected for this purpose [12].…”

Section: Model Evaluationmentioning

confidence: 94%

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

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Section: Model Evaluationmentioning

confidence: 94%

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Govindarajan

Swaminathan

2020

Appl Intell

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“…Zhang et al . [ 28 ] described a CNN strategy for monitoring porosity in laser additive manufacturing (AM) processes. The melt-pool data were gained through a high-speed digital camera for in-process sensing.…”

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

150

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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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“…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

215

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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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In-Process monitoring of porosity during laser additive manufacturing process

Cited by 163 publications

References 27 publications

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

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

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