Automatic detection and characterization of porosities in cross-section images of metal parts produced by binder jetting using machine learning and image augmentation

Satterlee, Nicholas; Torresani, Elisa; Olevsky, Eugene A.; Kang, John S.

doi:10.1007/s10845-023-02100-9

Cited by 6 publications

(2 citation statements)

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“…Another approach was reported by Satterlee et al for realizing the global morphologies of BJ parts from local cross-sectional analysis using image augmentation. 248 Image augmentation is a data expanding technique, which is often used when training data is limited, and data acquisition is time-consuming. 257 The study obtained 3966 images (27,294 pores) from 67 SEM images (4545 cross-sectional pores) through image augmentation using generative adversarial ANN.…”

Section: Porosity Analysismentioning

confidence: 99%

“…Another approach was reported by Satterlee et al for realizing the global morphologies of BJ parts from local cross‐sectional analysis using image augmentation 248 . Image augmentation is a data expanding technique, which is often used when training data is limited, and data acquisition is time‐consuming 257 .…”

Section: Application Of Machine Learning In Polymer Additive Manufact...mentioning

confidence: 99%

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Application of machine learning in polymer additive manufacturing: A review

Nasrin,

Pourkamali‐Anaraki,

Peterson

2023

Journal of Polymer Science

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Additive manufacturing (AM) is a revolutionary technology that enables production of intricate structures while minimizing material waste. However, its full potential has yet to be realized due to technical challenges such as the dependence of part quality on numerous process parameters, the vast number of design options, and the occurrence of defects. These complications may be magnified by the use of polymers and polymer composites due to their complex molecular structures, batch‐to‐batch variations, and changes in final part properties caused by small alterations in process settings and environmental conditions. Machine learning (ML), a branch of artificial intelligence, offers approaches to tackle these challenges and significantly reduce the experimental and computational time and expense. This review provides a comprehensive analysis of existing research on integrating ML techniques into polymer AM. It highlights the challenges involved in adopting ML in polymer AM, proposes potential solutions, and identifies areas for future research.

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Section: Porosity Analysismentioning

confidence: 99%

Section: Application Of Machine Learning In Polymer Additive Manufact...mentioning

confidence: 99%