The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling
Open accessAbstract
This paper investigates the manufacturing of aluminium-boron carbide composites using the stir casting method. Mechanical and physical properties tests to obtain hardness, ultimate tensile strength (UTS) and density are performed after solidification of specimens. The results show that hardness and tensile strength of aluminium based composite are higher than monolithic metal. Increasing the volume fraction of B4C, enhances the tensile strength and hardness of the composite; however over-loading of B4C caused particle agglomeration, rejection from molten metal and migration to slag. This phenomenon decreases the tensile strength and hardness of the aluminium based composite samples cast at 800 °C. For Al-15 vol% B4C samples, the ultimate tensile strength and Vickers hardness of the samples that were cast at 1000 °C, are the highest among all composites. To predict the mechanical properties of aluminium matrix composites, two key prediction modelling methods including Neural Network learned by Levenberg-Marquardt Algorithm (NN-LMA) and Thin Plate Spline (TPS) models are constructed based on experimental data. Although the results revealed that both mathematical models of mechanical properties of Al-B4C are reliable with a high level of accuracy, the TPS models predict the hardness and tensile strength values with less error compared to NN-LMA models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
Cited by
Carbon fiber reinforced metal matrix composites: Fabrication processes and properties
Shirvanimoghaddam, K., Hamim, S. U., Karbalaei Akbari, M., Fakhrhoseini, S. M., Khayyam, H., Pakseresht, A. H., Ghasali, E., Zabet, M., Munir, K. S., Jia, S., et al. (2017) · Composites Part A: Applied Science and Manufacturing · doi:10.1016/j.compositesa.2016.10.032 · Cited by 574
Self-citation
Metadata
- Authors
- Shirvanimoghaddam, K., Khayyam, H., Abdizadeh, H., Karbalaei Akbari, M., Pakseresht, A. H., Ghasali, E., & Naebe, M.
- Venue
- Materials Science and Engineering: A
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 658
- Pages
- 135-149
- ISSN
- 0921-5093 (ISSN-L)
- OpenAlex
- W2298051512
- Cited by
- 169
- References
- 1
- FWCI
- 15.64
- Type
- journal article
- Language
- en
- Open access
- green
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: Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling. https://app.cochat.ai/papers/10.1016/j.msea.2016.01.114
- The paper
- Shirvanimoghaddam, K., Khayyam, H., Abdizadeh, H., Karbalaei Akbari, M., Pakseresht, A. H., Ghasali, E., & Naebe, M. (2016). Boron carbide reinforced aluminium matrix composite: Physical, mechanical characterization and mathematical modelling. Materials Science and Engineering: A. https://doi.org/10.1016/j.msea.2016.01.114
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.