Retraction Note to: Artificial neural networks for prediction Charpy impact energy of Al6061/SiCp-laminated nanocomposit
- PDF / 156,334 Bytes
- 1 Pages / 595.276 x 790.866 pts Page_size
- 56 Downloads / 234 Views
(0123456789().,-volV)(0123456789(). ,- volV)
RETRACTION NOTE
Retraction Note to: Artificial neural networks for prediction Charpy impact energy of Al6061/SiCp-laminated nanocomposites Ali Nazari1 • Vahid Reza Abdinejad1
Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Retraction to: Neural Comput & Applic (2013) 23:801–813 https://doi.org/10.1007/s00521-012-0996-0
The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2–4] and previously published articles [5, 6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction.
References 1. Nazari A, Abdinejad VR (2013) Artificial neural networks for prediction Charpy impact energy of Al6061/SiCp-laminated nanocomposites. Neural Comput Appl 23:801–813. https://doi. org/10.1007/s00521-012-0996-0
2. Riahi S, Nazari A (2019) RETRACTED ARTICLE: predicting the effects of nanoparticles on early age compressive strength of ashbased geopolymers by artificial neural networks. Neural Comput Appl 31:743–750. https://doi.org/10.1007/s00521-012-1085-0 3. Nazari A, Hajiallahyari H, Rahimi A et al (2019) RETRACTED ARTICLE: prediction compressive strength of Portland cementbased geopolymers by artificial neural networks. Neural Comput Appl 31:733–741. https://doi.org/10.1007/s00521-012-1082-3 4. Nazari A (2013) Analytical modeling of tensile strength of functionally graded steels. Neural Comput Appl 23:787–799. https://doi.org/10.1007/s00521-012-0995-1 5. Nazari A, Sedghi A, Didehvar N (2012) RETRACTED: modeling impact resistance of aluminum–epoxy-laminated composites by artificial neural networks. J Compos Mater 46(13):1593–1605. https://doi.org/10.1177/0021998311421222 6. Nazari A (2013) Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput Appl 23:507–518. https://doi.org/10.1007/s00521-0120945-y Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original article can be found online at https:// doi.org/10.1007/s00521-012-0996-0. & Ali Nazari [email protected] 1
Department of Materials Science and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
123
Data Loading...
