The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Prediction of surface roughness in extrusion-based additive manufacturing with machine learning
Abstract
Abstract Additive manufacturing (AM), also known as 3D printing, has been increasingly adopted in the aerospace, automotive, energy, and healthcare industries over the past few years. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is surface integrity. To improve the surface integrity of additively manufactured parts, a data-driven predictive modeling approach to predicting surface roughness in AM is introduced. Multiple sensors of different types, including thermocouples, infrared temperature sensors, and accelerometers, are used to collect temperature and vibration data. An ensemble learning algorithm is introduced to train the predictive model of surface roughness. Features in the time and frequency domains are extracted from sensor-based condition monitoring data. A subset of these features is selected to improve computational efficiency and prediction accuracy. The predictive model is validated using the condition monitoring data collected from a set of AM tests conducted on a fused filament fabrication (FFF) machine. Experimental results have shown that the proposed predictive modeling approach is capable of predicting the surface roughness of 3D printed components with high accuracy.
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Cited by
Machine Learning in Additive Manufacturing: A Review
Meng, L., McWilliams, B., Jarosinski, W., Park, H.-Y., Jung, Y.-G., Lee, J., & Zhang, J. (2020) · JOM · doi:10.1007/s11837-020-04155-y · Cited by 470
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
The role of additive manufacturing for biomedical applications: A critical review
Kumar, R., Kumar, M., & Chohan, J. S. (2021) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2021.02.022 · Cited by 367
A review of industrial big data for decision making in intelligent manufacturing
Li, C., Chen, Y., & Shang, Y. (2022) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2021.06.001 · Cited by 325
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00089-x · Cited by 303
Metadata
- Authors
- Li, Z., Zhang, Z., Shi, J., & Wu, D.
- Venue
- Robotics and Computer-Integrated Manufacturing
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 57
- Pages
- 488-495
- ISSN
- 0736-5845 (ISSN-L)
- OpenAlex
- W2909096226
- Cited by
- 432
- References
- 29
- FWCI
- 21.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, Z., Zhang, Z., Shi, J., & Wu, D. (2019). Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2019.01.004
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