Interface Morphology Description and Quantification of Al–Cu Magnetic Pulse Welding Joints Using X-Ray Micro-computed Tomography

Zielinski, Benjamin; Sadat, Tarik; Guibert, Robin; Jouaffre, Denis; Markiewicz, Éric; Dubar, Laurent

doi:10.1007/s10921-023-00984-4

Cited by 1 publication

(2 citation statements)

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“…XCT is a non-destructive characterization technique that offers global morphology analysis, but is constrained by its long acquisition time. 255 ANNs have been used to reduce the acquisition time for morphological analysis for PBF. 223 However, the pore morphology of BJ components is significantly different from that of PBF components (uniform vs. clustered pores), requiring a different analytical approach.…”

Section: Porosity Analysismentioning

confidence: 99%

“…In addition, traditional image analysis software requires specific conditions and manual inputs, making the porosity analysis process time‐consuming and prone to error. XCT is a non‐destructive characterization technique that offers global morphology analysis, but is constrained by its long acquisition time 255 . ANNs have been used to reduce the acquisition time for morphological analysis for PBF 223 .…”

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%