The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process
Open accessAbstract
Abstract In-situ detection of processing defects is a critical challenge for Laser Powder Bed Fusion Additive Manufacturing. Many of these defects are related to interactions between the recoater blade, which spreads the powder, and the powder bed. This work leverages Deep Learning, specifically a Convolutional Neural Network (CNN), for autonomous detection and classification of many of these spreading anomalies. Importantly, the input layer of the CNN is modified to enable the algorithm to learn both the appearance of the powder bed anomalies as well as key contextual information at multiple size scales. These modifications to the CNN architecture are shown to improve the flexibility and overall classification accuracy of the algorithm while mitigating many human biases. A case study is used to demonstrate the utility of the presented methodology and the overall performance is shown to be superior to that of methodologies previously reported by the authors.
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Research and application of machine learning for additive manufacturing
Qin, J., Hu, F., Liu, Y., Witherell, P., Wang, C. C. L., Rosen, D. W., Simpson, T. W., Lu, Y., & Tang, Q. (2022) · Additive Manufacturing · doi:10.1016/j.addma.2022.102691 · Cited by 391
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A review of machine learning applications in additive manufacturing
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Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118
Metadata
- Authors
- Scime, L., & Beuth, J.
- Venue
- Additive Manufacturing
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 24
- Pages
- 273-286
- ISSN
- 2214-7810 (ISSN-L)
- 2214-8604
- OpenAlex
- W2895046268
- Cited by
- 322
- References
- 31
- FWCI
- 15.03
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Scime, L., & Beuth, J. (2018). A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing. https://doi.org/10.1016/j.addma.2018.09.034
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