RETRACTED: Modeling Ductile-to-Brittle Transition Temperature of Functionally Graded Steels by Gene Expression Programming

Abstract

In 2019 SAGE became aware of author misconduct concerning suspected redundant publication of 22 articles published in International Journal of Damage Mechanics and Journal of Composite Materials. SAGE and the journals' Editors immediately launched an investigation and found that the following articles contain significant overlap with previously published articles by at least one of the authors listed on each of the articles below. Therefore, SAGE and the journals' Editors have decided to retract the following articles for reasons of redundant publication.

Toughness prediction in functionally graded Al6061/SiCp composites produced by roll-bonding

“…Regression techniques are often based on predefined functions where analyses of these functions are later performed while no predefined function is considered for GP approach. GP is believed to be powerful with respect to regression techniques and neural networks and has proven to be an effective tool to model and obtain clear formulations of experimental studies including multivariate problems with no existing analytical models [28][29][30][31][32].…”

Searching for the superior solution to the population-based optimization problem: Processing of the wear resistant commercial AA6061 AMCs

“…Kuo integrated the mutation mechanism of GA with PSO. Valdez combined GA and PSO using fuzzy logic to integrate the results of both methods and for parameter tuning (Beasley et al, 1993;Brits, 2002;Engelbrecht et al, 2005;Kennedy, 1999;Kennedy andEberhart, 1995, 2001;Kennedy and Mendes, 2002;Krohling and Mendel, 2009;Shabani, 2012a, 2012b;Milani and Nazari, 2012;Nazari and Riahi, 2012;Mazahery, 2012a, 2012b;van den Bergh and Engelbrecht, 2006).…”

Duplex ceramic coating produced by low temperature thermo-reactive deposition and diffusion on the cold work tool steel substrate: Thermodynamics, kinetics and modeling

“…Their ability to learn by example makes ANNs very flexible and powerful. Therefore, neural networks have been extremely used for solving regression and classification problems in many fields [26][27][28]. Recently, neural networks have been used in the areas that require computational techniques, such as pattern recognition, optical character recognition, predicting outcomes, and problem classification [29,30].…”