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Research Article

Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm

ORCID Icon, & | (Reviewing editor)
Article: 1681055 | Received 01 May 2019, Accepted 07 Oct 2019, Published online: 25 Oct 2019

References

  • AFS. (2000). Mold & core test handbook (3rd ed.). IL: American Foundry Society.
  • Atuanya, C. U., Government, M. R., Nwobi-Okoye, C. C., & Onukwuli, O. D. (2014). Predicting the mechanical properties of date palm wood fibre-recycled low density polyethylene composite using artificial neural network. International Journal of Mechanical and Materials Engineering, 7(1), 1–17.
  • Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. Hoboken, New Jersey, USA: John Wiley & Sons, Inc.
  • Igboanugo, A. C., & Nwobi-Okoye, C. C. (2011). Optimisation of transfer function models using genetic algorithms. Journal of the Nigerian Association of Mathematical Physics, 19, 439–452.
  • Karunakar, D. B., & Datta, G. L. (2007). Controlling green sand mould properties using artificial neural networks and genetic algorithms—A comparison. Applied Clay Science, 37(1–2), 58–66. doi:10.1016/j.clay.2006.11.005
  • Nagur Babu, N., Ohdar, R. K., & Pushp, P. T. (2006). Evaluation of green compressive strength of clay bonded moulding sand mixture, neural network and neuro fuzzy based approach. International Journal of Cast Metal Research, 19(2), 19–110.
  • Nwobi-Okoye, C. C., & Ochieze, B. Q. (2018). Age hardening process modeling and optimization of aluminum alloy A356/cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing. Defence Technology, 8(3), 3054–3075. doi:10.1016/j.dt.2018.04.001
  • Nwobi-Okoye, C. C., Ochieze, B. Q., & Okiy, S. (2019). 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, 8(3), 3054–3075. doi:10.1016/j.jmrt.2019.01.031
  • Nwobi-Okoye, C. C., & Umeonyiagu, I. E. (2013). Predicting the compressive strength of concretes made with unwashed gravel from Eastern Nigeria Using Artificial Neural Networks. Nigerian Journal of Technological Research, 8(2), 22–29. doi:10.4314/njtr.v8i2.96695
  • Nwobi-Okoye, C. C., & Umeonyiagu, I. E. (2015). Predicting the flexural strength of concretes made with granite from Eastern Nigeria using multi-layer perceptron networks. Journal of the Nigerian Association of Mathematical Physics, 29(2015), 55–64.
  • Nwobi-Okoye, C. C., & Umeonyiagu, I. E. (2016). Modelling the effects of petroleum product contaminated sand on the compressive strength of concretes using fuzzy logic and artificial neural networks. African Journal of Science Technology, Innovation and Development (taylor and Francis), 8(3), 264–274.
  • Nwobi-Okoye, C. C., Umeonyiagu, I. E., & Nwankwo, C. G. (2013). Predicting the compressive strength of concretes made with granite from Eastern Nigeria using Artificial Neural Networks. Nigerian Journal of Technology (NIJOTECH), 32(1), 13–21.
  • Nwobi-Okoye, C.C, & Igboanugo, A.C. (2013). Predicting water levels at kainji dam using artificial neural networks. Nigerian Journal Of Technology, 32(1), 129-136.
  • Ohdar, R. K., & Pushp, P. T. (2003). Prediction of collapsibility of moulds and cores of CO2 sands using a neural network. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(4), 475–487. doi:10.1243/095440503321628152
  • Okiy, S., Oreko, B. U., Nwobi-Okoye, C. C., & Igboanugo, A. C. (2017). Optimisation of multi input single output transfer function models using genetic algorithms. Journal of the Nigerian Association of Mathematical Physics, 40, 459–466.
  • Okonji, P. C., Nwobi-Okoye, C. C., & Atanmo, P. N. (2018). Experimental study of the feasibility of using groundnut shell ash and ant hill powder in foundry application. Journal of the Chinese Advanced Materials Society, 6, 270–281. doi:10.1080/22243682.2018.1461576
  • Parappagoudar, M. B., Pratihar, D. K., & Datta, G. L. (2007). Modelling of input–output relationships in cement bonded moulding sand system using neural networks. International Journal of Cast Metals Research, 20(5), 265–274.
  • Parappagoudar, M. B., Pratihar, D. K., & Datta, G. L. (2008). Neural network-based approaches for forward and reverse mappings of sodium silicate-bonded, carbon dioxide gas hardened moulding sand system. Materials and Manufacturing Processes, 24(1), 59–67. doi:10.1080/10426910802543681
  • Piuleac, C. G., Curteanu, S., Rodrigo, M. A., Sáez, C., & Fernández, F. J. (2013). Optimization methodology based on neural networks and genetic algorithms applied to electro-coagulation processes. Central European Journal of Chemistry, 11(7), 1213–1224.
  • Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A modern approach. Upper Saddle River, New Jersey, USA: Pearson Educational Inc.
  • Shang, Y. (2015). Inhomogeneous long-range percolation on the hierarchical lattice. Reports on Mathematical Physics, 76(1), 53–61. doi:10.1016/S0034-4877(15)30018-5
  • Shang, Y. (2016a). On the likelihood of forests. Physica A: Statistical Mechanics and Its Applications, 456, 157–166. doi:10.1016/j.physa.2016.03.021
  • Shang, Y. (2016b). Effect of link oriented self-healing on resilience of networks. Journal of Statistical Mechanics: Theory and Experiment, 2016(8), 083403. doi:10.1088/1742-5468/2016/08/083403
  • Umeonyiagu, I. E., & Nwobi-Okoye, C. C. (2013). Predicting the compressive strength of concretes made with washed gravel from Eastern Nigeria using artificial neural networks. Journal of the Nigerian Association of Mathematical Physics, 23, 559.
  • Umeonyiagu, I. E., & Nwobi-Okoye, C. C. (2015a). Predicting flexural strength of concretes incorporating river gravel using multi multi-layer perceptron networks: A case study of Eastern Nigeria. Nigerian Journal of Technology, 34(1), 12–20. doi:10.4314/njt.v34i1.2
  • Umeonyiagu, I. E., & Nwobi-Okoye, C. C. (2015b). Modelling compressive strength of concretes incorporating termite mound soil using multi-layer perceptron networks: A case study of Eastern Nigeria. International Journal of Research and Reviews in Applied Sciences, 24(1), 19–30.
  • Umeonyiagu, I. E., & Nwobi-Okoye, C. C. (2019). Modelling and multi-objective optimization of bamboo reinforced concrete beams using ANN and genetic algorithms. European Journal of Wood and Wood Products, 2019(77), 931–947. doi:10.1007/s00107-019-01418-7
  • Vundavilli, P. R., Surekha, B., & Parappagoudar, M. B. (2014). ABC and GA optimized NN to model resin bonded mould/core sand system: A soft computing-based approach. Journal for Manufacturing Science and Production, 14(4), 257–267. doi:10.1515/jmsp-2014-0029