Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Optimization of Solidification Process Based on Artificial Intelligence

Author : Lata Verma 1

PDF

Date of Publication :6th April 2018

Abstract: This paper exhibits an advancement approach for photosensitive gum hardening process dependent on counterfeit neural system joined with symmetrical test and hereditary calculation. A prescient model for hardening is built up utilizing fake neural system and the example for neural system model is planned by utilizing symmetrical trial strategy. In the model, the cementing procedure parameters including situation temperature, enlightenment separation and enlightenment time are treated as plan factors what's more, the goal is to get the greatest estimation of inflexibility. Advancement of hardening process parameters for photosensitive gum was led by presenting counterfeit neural system forecast models into hereditary calculation. The results show that the streamlining strategy dependent on counterfeit neural system and the hereditary calculation is practical for improve the structure nature of the cementing procedure.

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    1. L. G. Caldas and L. K. Norford, “A design optimization tool based on a genetic algorithm,” Autom. Constr., 2002, doi: 10.1016/S0926- 5805(00)00096-0.
    2.  M. Jaderberg et al., “Human-level performance in 3D multiplayer games with population-based reinforcement learning,” Science (80-. )., 2019, doi: 10.1126/science.aau6249.
    3. I. Inza, P. Larrañaga, R. Etxeberria, and B. Sierra, “Feature Subset Selection by Bayesian network-based optimization,” Artif. Intell., 2000, doi: 10.1016/S0004-3702(00)00052-7.
    4. B. A. Jensen, B. Joseph, and B. G. Lipták, “Expert systems,” in Instrument Engineers Handbook, Fourth Edition: Process Control and Optimization, 2005.
    5. A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective intelligent energy management for a microgrid,” IEEE Trans. Ind. Electron., 2013, doi: 10.1109/TIE.2012.2188873.
    6. F. Herrera, M. Lozano, and J. L. Verdegay, “Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis,” Artif. Intell. Rev., 1998, doi: 10.1023/A:1006504901164.
    7. P. Parandoush and A. Hossain, “A review of modeling and simulation of laser beam machining,” International Journal of Machine Tools and Manufacture. 2014, doi: 10.1016/j.ijmachtools.2014.05.008.
    8. T. Khatib, A. Mohamed, and K. Sopian, “A review of photovoltaic systems size optimization techniques,” Renewable and Sustainable Energy Reviews. 2013, doi: 10.1016/j.rser.2013.02.023.
    9. M. Bahram, A. Wolf, M. Aeberhard, and D. Wollherr, “A prediction-based reactive driving strategy for highly automated driving function on freeways,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2014, doi: 10.1109/IVS.2014.6856503.
    10. R. Quiza Sardiñas, M. Rivas Santana, and E. Alfonso Brindis, “Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes,” Eng. Appl. Artif. Intell., 2006, doi: 10.1016/j.engappai.2005.06.007.

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