The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Powder-based processes are prone to issues of porosity, lackof-fusion, balling, and cracks [428,430,431], while geometry deviation, shape shrinkage, surface imperfections, layer misalignment, and adhesion issues [432-435] are the main concerns in the FFF processes.”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [434]
Citations
Cites
Metal Additive Manufacturing: A Review
Frazier, W. E. (2014) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-014-0958-z · Cited by 5,984
Ding, D., Pan, Z., Cuiuri, D., & Li, H. (2015) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-015-7077-3 · Cited by 1,648
Self-citationSurface roughness analysis, modelling and prediction in selective laser melting
Strano, G., Hao, L., Everson, R. M., & Evans, K. E. (2013) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2012.11.011 · Cited by 876
A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM)
Ding, D., Pan, Z. S., Cuiuri, D., & Li, H. (2014) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2014.08.008 · Cited by 577
Self-citationA review on wire arc additive manufacturing: Monitoring, control and a framework of automated system
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., Chen, S., & Zhang, Y. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.08.008 · Cited by 460
Self-citationLow cost integration of additive and subtractive processes for hybrid layered manufacturing
Karunakaran, K. P., Suryakumar, S., Pushpa, V., & Akula, S. (2010) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2010.03.008 · Cited by 377
Tabari, H., Kisi, O., Ezani, A., & Hosseinzadeh Talaee, P. (2012) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.04.007 · Cited by 311
Xiong, J., Li, Y., Li, R., & Yin, Z. (2017) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2017.09.020 · Cited by 264
Aminzadeh, M., & Kurfess, T. R. (2018) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1412-0 · Cited by 192
Deposition of Ti–6Al–4V using laser and wire, part I: Microstructural properties of single beads
Brandl, E., Michailov, V., Viehweger, B., & Leyens, C. (2011) · Surface and Coatings Technology · doi:10.1016/j.surfcoat.2011.07.095 · Cited by 187
Cited by
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
Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118
A comprehensive review of lunar-based manufacturing and construction
Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57
Wire arc additive manufacturing of low alloyed steels, copper aluminum alloys, and their bimetals
Karamimoghadam, M., Aghayar, Y., Forooghi, F., Denora, V., Mohammadi, M., Contuzzi, N., & Casalino, G. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16436-4 · Cited by 18
Metadata
- Authors
- Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S.
- Affiliations
- Shanghai Jiao Tong University (CN)
- University of Wollongong (AU)
- Corresponding author
- Pan, Zengxi
- Xu, Yanling
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2021-01-23
- Volume
- 33
- Issue
- 5
- Pages
- 1467-1482
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W3121721182
- Cited by
- 256
- References
- 29
- Citations by year
- 2026: 43 · 2025: 58 · 2024: 59 · 2023: 42 · 2022: 36 · 2021: 18
- FWCI
- 16.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Figshare (submittedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Machining and Optimization Techniques
- Physical Sciences › Engineering › Mechanical Engineering › Advanced machining processes and optimization
- Keywords
- wire arc additive manufacturing, surface roughness prediction, adaptive neuro-fuzzy inference system, extreme learning machine, support vector regression, genetic algorithm optimization, particle swarm optimization, cladding, surface integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. https://app.cochat.ai/papers/10.1007/s10845-020-01725-4
- The paper
- Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., & Chen, S. (2022). Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01725-4
Data sources
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.
