Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials

[1] Abd Elaziz, M., Shehabeldeen, T. A., Elsheikh, A. H., Zhou, J., Ewees, A. A., & Al-qaness, M. A. Utilization of Random Vector Functional Link integrated with Marine Predators Algorithm for tensile behavior prediction of dissimilar friction stir welded aluminum alloy joints. Journal of Materials Research and Technology., 2020. 9(5): 11370–11381. Abd ElazizM. ShehabeldeenT. A. ElsheikhA. H. ZhouJ. EweesA. A. Al-qanessM. A. Utilization of Random Vector Functional Link integrated with Marine Predators Algorithm for tensile behavior prediction of dissimilar friction stir welded aluminum alloy joints Journal of Materials Research and Technology 2020 9 5 11370 11381 Search in Google Scholar

[2] Arslankaya, S. Estimating the effects of heat treatment on aluminum alloy with artificial neural networks. Emerging Materials Research., 2020. 9(2): 540–549. ArslankayaS. Estimating the effects of heat treatment on aluminum alloy with artificial neural networks Emerging Materials Research 2020 9 2 540 549 Search in Google Scholar

[3] Dai, Q. S., Deng, Y. L., Tang, J. G., & Yu, W. A. N. G. Deformation characteristics and strain-compensated constitutive equation for AA5083 aluminum alloy under hot compression. Transactions of Nonferrous Metals Society of China., 2019. 29(11): 2252–2261. DaiQ. S. DengY. L. TangJ. G. YuW. A. N. G. Deformation characteristics and strain-compensated constitutive equation for AA5083 aluminum alloy under hot compression Transactions of Nonferrous Metals Society of China 2019 29 11 2252 2261 Search in Google Scholar

[4] Nwobi-Okoye, C. C., Ochieze, B. Q., & Okiy, S. Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite. Journal of Materials Research and Technology., 2019. 8(3): 3054–3075. Nwobi-OkoyeC. C. OchiezeB. Q. OkiyS. Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite Journal of Materials Research and Technology 2019 8 3 3054 3075 Search in Google Scholar

[5] Munoz-Ibanez, C., Alfaro-Ponce, M., & Chairez, I. Hierarchical artificial neural network modelling of aluminum alloy properties used in die casting. The International Journal of Advanced Manufacturing Technology., 2019. 104(1): 1541–1550. Munoz-IbanezC. Alfaro-PonceM. ChairezI. Hierarchical artificial neural network modelling of aluminum alloy properties used in die casting The International Journal of Advanced Manufacturing Technology 2019 104 1 1541 1550 Search in Google Scholar

[6] D’Orazio, A., Forcellese, A., & Simoncini, M. Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model. Neural Computing and Applications., 2019. 31(11): 7211–7226. D’OrazioA. ForcelleseA. SimonciniM. Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model Neural Computing and Applications 2019 31 11 7211 7226 Search in Google Scholar

[7] Arslankaya, S. Estimation of hanging and removal times in eloxal with artificial neural networks. Emerging Materials Research., 2020. 9(2): 366–374. ArslankayaS. Estimation of hanging and removal times in eloxal with artificial neural networks Emerging Materials Research 2020 9 2 366 374 Search in Google Scholar

[8] Orta, A. H., Kayabasi, I., & Senol, M. Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks. Ironmaking & Steelmaking., 2020. 47(6): 596–605. OrtaA. H. KayabasiI. SenolM. Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks Ironmaking & Steelmaking 2020 47 6 596 605 Search in Google Scholar

[9] Naresh, C., Bose, P. S. C., & Rao, C. S. P. Artificial neural networks and adaptive neuro-fuzzy models for predicting WEDM machining responses of Nitinol alloy: Comparative study. SN Applied Sciences., 2020. 2(2): 1–23. NareshC. BoseP. S. C. RaoC. S. P. Artificial neural networks and adaptive neuro-fuzzy models for predicting WEDM machining responses of Nitinol alloy: Comparative study SN Applied Sciences 2020 2 2 1 23 Search in Google Scholar

[10] Iglesias Martínez, M., Antonino-Daviu, J., de Córdoba, P. & Conejero, J. Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors. Applied Mathematics and Nonlinear Sciences., 2020. 5(2): 1–14. Iglesias MartínezM. Antonino-DaviuJ. de CórdobaP. ConejeroJ. Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors Applied Mathematics and Nonlinear Sciences 2020 5 2 1 14 Search in Google Scholar

[11] Touchent, K., Hammouch, Z. & Mekkaoui, T. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives. Applied Mathematics and Nonlinear Sciences., 2020. 5(2): 35–48. TouchentK. HammouchZ. MekkaouiT. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives Applied Mathematics and Nonlinear Sciences 2020 5 2 35 48 Search in Google Scholar

[12] Vembu, V. Age Hardening Process Modeling and Optimization of Aluminum Alloy 8011/SiC Particulate Composite for Brake Drum Application using RSM and ANN. International Journal of Applied Engineering Research., 2020. 15(2): 127–130. VembuV. Age Hardening Process Modeling and Optimization of Aluminum Alloy 8011/SiC Particulate Composite for Brake Drum Application using RSM and ANN International Journal of Applied Engineering Research 2020 15 2 127 130 Search in Google Scholar

[13] Chen, S. H., & Huang, W. S. Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks. Sensors and Materials., 2020. 32(3): 859–872. ChenS. H. HuangW. S. Prediction of Thermal Deformation of Rotary Table in Multifunction Machine Tool Using Neural Networks Sensors and Materials 2020 32 3 859 872 Search in Google Scholar