A new formulation for martensite start temperature of Fe–Mn–Si shape memory alloys using genetic programming

Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

“…This approach has been widely applied in multidisciplinary fields, including but not limited to financial market analysis 20 , biological science 21 , 22 , software development 23 , and identifying interatomic potential models from calculated energies 24 – 26 . In materials science and engineering, researchers have used genetic programming to develop predictive models for properties of concrete and cement 27 – 29 , asphalt 30 , shape memory alloys 31 , and heterogeneous catalysts 32 . They have also been used to determine the effects of processing parameters on metal alloys 33 – 36 , predict the impact roughness of cold formed materials 37 , optimize productivity for the steel industry 38 , and develop models for a variety of problems in structural engineering 39 .…”

Design Concept and Applications of Fe–Mn–Si-Based Alloys—from Shape-Memory to Seismic Response Control

“…To predict the phase transformation behavior and to design shape-memory property, the optimum concentrations of the alloy components can be calculated with various equations for the Gibbs free energy difference between the £-and ¾-phases, 2831) and various empirical equations for M s temperature of the £ ¼ ¾ transformation. 32,33) On the other hand, the roles of silicon are diversified and complicated. Silicon hardens the parent matrix to suppress the dislocation gliding, while it promotes the £ ¼ ¾ martensitic transformation through lowering the stacking fault energy.…”

Determination of Barreling of Aluminum Solid Cylinders During Cold Upsetting Using Genetic Algorithm

“…Statistical parameters of test and training sets of GP formulations are presented in Table 8 where R; MSE and MAE corresponds to the coefficient of correlation, mean square error and the mean absolute error of proposed GEP model, respectively as seen in Table 8. In literature [18,19], this type of studies includes test sets as 20%-30% of the train set. The patterns used in test and training sets are selected in systematic randomly.…”

“…The genetic code is very simple where there exist one-to-one relationships between the symbols of the chromosome and the functions or terminals they represent. Spatial organization and terminals in the ETs and type of interaction between sub-ETs can be determined easily by rules [17,18]. That"s why two languages are used in the GEP: the language of the genes and the language of ETs.…”