Retraction
Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels
Paper Information
Record ID:
24061
Author(s):
Journal:
Publication Date:
November 10, 2011
Retraction Date:
August 17, 2020
(5.3 years years ago)
Subjects:
Institution:
Department of Materials Science and Engineering, Saveh Branch, Islamic Azad University, Saveh, IranCountry:
š®š· IranArticle Type:
Publisher:
Springer
Open Access:
Yes
PubMed ID:
Not indexed in PubMed
Retraction PubMed ID:
Not indexed in PubMed
Retraction Details
Citations (21)
21
Total Citations5
Post-Retraction(23.8%)
15
Pre-Retraction1
Same DayPost-Retraction Citation Analysis
3
Within 30 days
4
Within 1 year
0
After 2+ years
414
Days since retraction (latest)
Artificial neural network modeling to evaluate and predict the mechanical strength of duplex stainless steel during casting
Titus Thankachan, K. Soorya Prakash, Sathiskumar Jothi
Sadhana
Published: Oct 2021
7 citations
7 citations
414 days after retraction
Retraction Note to: The role of the Islamic Azad University in science production of Iran: from the past to the future
Ali Nazari
Neural Computing and Applications
Open Access
Published: May 2021
280 days after retraction
Retraction Note: Prediction microhardness profile of functionally graded steels by ANFIS
Hamid Bohlooli, Ali Nazari, Mohammad Mehdi Kaykha
Neural Computing and Applications
Open Access
Published: Aug 2020
8 days after retraction
Retraction Note: Analytical modeling of tensile strength of functionally graded steels
Ali Nazari
Neural Computing and Applications
Open Access
Published: Aug 2020
8 days after retraction
Retraction Note: Modeling the correlation between Charpy impact energy and chemical composition of functionally graded steels by artificial neural networks
Ali Nazari
Neural Computing and Applications
Open Access
Published: Aug 2020
8 days after retraction
Retraction Note to: Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels
Ali Nazari
Neural Computing and Applications
Open Access
Published: Aug 2020
Same day as retraction
Prediction of mechanical properties of welded steel X70 pipeline using neural network modelling
Adel Saoudi, Mamoun Fellah, Naouel Hezil et al. (10 authors)
International Journal of Pressure Vessels and Piping
Published: Jul 2020
19 citations
19 citations
32 days before retraction
Prediction of the Ultimate Tensile Strength in API X70 Line Pipe Steel Using an Artificial Neural Network Model
Adel Saoudi, Djahida Lerari, Farida Khamouli et al. (5 authors)
Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena
Published: Sep 2019
5 citations
5 citations
351 days before retraction
Artificial Neural Network-Based Modeling for Impact Energy of Cast Duplex Stainless Steel
Titus Thankachan, K. Sooryaprakash
Arabian Journal for Science and Engineering
Published: Oct 2017
12 citations
12 citations
1029 days before retraction
Neural approach to estimate the stress intensity factor of semiāelliptical cracks in rotating cracked shafts in bending
Patricia Rubio, B. MuƱozāAbella, L. Rubio
Fatigue & Fracture of Engineering Materials & Structures
Open Access
Published: Oct 2017
10 citations
10 citations
1046 days before retraction
Ship Manoeuvrability Prediction Using Neural Networks Analysis
Remus ZÄgan, M.G. Chitu, Mihaela-Greti Manea
Advanced materials research
Published: Oct 2014
1 citation
1 citation
2147 days before retraction
Stress intensity factor estimation for unbalanced rotating cracked shafts by artificial neural networks
B. MuƱozāAbella, L. Rubio, Patricia Rubio
Fatigue & Fracture of Engineering Materials & Structures
Published: Aug 2014
14 citations
14 citations
2195 days before retraction
Artificial neural networks application to predict the ultimate tensile strength of X70 pipeline steels
Gholamreza Khalaj, Tohid Azimzadegan, Mahdi Khoeini et al. (4 authors)
Neural Computing and Applications
Published: Sep 2012
45 citations
45 citations
2878 days before retraction
Predicting compressive strength of different geopolymers by artificial neural networks
Ali Nazari, F. PachecoāTorgal
Ceramics International
Open Access
Published: Sep 2012
61 citations
61 citations
2907 days before retraction
An artificial neural-network model for impact properties in X70 pipeline steels
Tohid Azimzadegan, Mahdi Khoeini, Moslem Etaat et al. (4 authors)
Neural Computing and Applications
Published: Jul 2012
14 citations
14 citations
2940 days before retraction
RETRACTED ARTICLE: Modeling the correlation between Charpy impact energy and chemical composition of functionally graded steels by artificial neural networks
Ali Nazari
Neural Computing and Applications
Published: Jul 2012
24 citations
24 citations
2963 days before retraction
RETRACTED ARTICLE: Analytical modeling of tensile strength of functionally graded steels
Ali Nazari
Neural Computing and Applications
Published: Jun 2012
5 citations
5 citations
2972 days before retraction
Programming squat wall strengths and tuning associated codes with pruned modular neural network
HsingāChih Tsai, Yun-Wu Wu, Yaw-Yauan Tyan et al. (4 authors)
Neural Computing and Applications
Published: Jun 2012
4 citations
4 citations
2988 days before retraction
Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels
Gholamreza Khalaj
Neural Computing and Applications
Published: Jun 2012
42 citations
42 citations
2991 days before retraction
ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels
Gholamreza Khalaj, Mahdi Khoeini, Meysam Khakian-Qomi
Neural Computing and Applications
Published: Jun 2012
23 citations
23 citations
2992 days before retraction
RETRACTED ARTICLE: Prediction microhardness profile of functionally graded steels by ANFIS
Hamid Bohlooli, Ali Nazari, Mohammad Mehdi Kaykha
Neural Computing and Applications
Published: Dec 2011
4 citations
4 citations
3156 days before retraction
Quick Stats
Total Citations:
21
Years Since Retraction:
5.3 years
Open Access:
Yes
Last Checked:
Jul 24, 2025