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Study on optimal design of pressure-adjusting spring using artificial neural networks

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Abstract

A preliminary discussion has been carried out on the traditional optimization design method for pressure-adjusting spring of relief valve. Based on the traditional optimization methods about the pressure-adjusting spring of the relief valve and combined with the advantages of neural network, this paper puts forward the optimization method with many parameters and a lot of constraints based on neural network in order to find the maximal inherent frequency. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure-adjusting spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure-adjusting spring. The specified technical performances of the relief valve are certified by experiments. The results of experiments showed that by configuring pressure-adjusting spring the dynamic performance and working stability of the relief valve are enhanced.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Shaoxing College of Arts and Sciences, Shaoxing, Zhejiang, 312000, People’s Republic of China

    Xiao-Jin Fu

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  1. Xiao-Jin Fu
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Correspondence to Xiao-Jin Fu.

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Fu, XJ. Study on optimal design of pressure-adjusting spring using artificial neural networks. Engineering with Computers 23, 55–60 (2007). https://doi.org/10.1007/s00366-006-0042-x

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  • DOI: https://doi.org/10.1007/s00366-006-0042-x

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