The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
A layer-by-layer quality monitoring framework for 3D printing
Abstract
Abstract Technology development in additive manufacturing is accelerating transition from mass production to mass customization. In this transition, automation in all stages of production including quality control is a key. In this study, a layer-wise framework is proposed to monitor quality of 3D printing parts based on top-view images. The proposed statistical process monitoring method starts with self-start control charts that require only two successful initial prints. Answering the challenges of image processing due to lighting, a Machine Learning (ML) method is adopted to separate each layer from the printing bed. A sample image is compared to the standard image from a good part at each layer. The number of pixels in the difference images is fed into the proposed control charts to monitor printing process at each layer. An Exponentially Weighted Moving Average (EWMA) chart based on the number of pixels is used for process monitoring at each layer. Once enough parts have been printed, homogeneous layers are clustered to reduce the number of control charts needed for process monitoring. Experimental results based on a 3-inch diameter basket part show that the proposed framework based on continuously monitoring of layer-by-layer images is able of detecting small changes in printing process.
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“With the advancement of data acquisition, machine learning algorithms can be applied to address this aspect [436,437].”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [436]
Citations
Cites
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Self-citation
Research works printed but not linked: 2
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Cited by
Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond
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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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Artificial Intelligence‐Augmented Additive Manufacturing: Insights on Closed‐Loop 3D Printing
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Metadata
- Authors
- Najjartabar Bisheh, M., Chang, S. I., & Lei, S.
- Affiliations
- Kansas State University (US)
- Venue
- Computers & Industrial Engineering
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 157
- Pages
- 107314
- ISSN
- 0360-8352 (ISSN-L)
- OpenAlex
- W3155557184
- Cited by
- 58
- References
- 38
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 11 · 2023: 13 · 2022: 9 · 2021: 4
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Computers & Industrial Engineering (publishedVersion)
- Funders
- National Science Foundation Directorate for Engineering
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- 3D printing, additive manufacturing, quality monitoring, statistical process control, top-view images, EWMA control chart, layer segmentation, image processing, mass customization, process change detection
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- Najjartabar Bisheh, M., Chang, S. I., & Lei, S. (2021). A layer-by-layer quality monitoring framework for 3D printing. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2021.107314
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
