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首页> 外文期刊>Journal of Materials Processing Technology >Neural network analysis of experimental data for air/water spray cooling
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Neural network analysis of experimental data for air/water spray cooling

机译：空气/水喷雾冷却实验数据的神经网络分析

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摘要

To obtain appropriate strength properties, nickel-based superalloy or titanium materials used in the aerospace industry are heat treated by cooling from high temperatures. Unacceptably high residual stresses may result, if the rate of cooling is too high. After extensive investigation, it was found that air-assisted atomised water sprays offer an excellent capability of controlling the rate of cooling, and they are a viable alternative to the widely used techniques of quenching in oil or water. The heat transfer data were obtained for a wide range of pressure ratios and hence water flows for surface temperatures of up to 850 deg C. This paper provides a neural methodology for heat transfer analyses of data obtained experimentally during the investigation of the use of air-assisted atomised water spray systems for the controlled cooling of high temperature forgings. The model created to train the neural network relates the spray input variables to the corresponding heat transfer data for the range of conditions observed experimentally. For comparison purposes and accurate evaluation of the predictions, part of the data is used to train the neural network and the remainder to test the model. It is described in detail how a neural network can be trained to successfully predict the resulting heat flux for specific input spray parameters. This particular knowledge can then be used to optimise the process, i.e. to establish the spray conditions that would yield the cooling rate required to attain the pre-specified mechanical properties, and to minimise the residual stresses.
机译：为了获得适当的强度性能，将航空航天工业中使用的镍基高温合金或钛材料通过高温冷却进行热处理。如果冷却速率太高，可能会导致不可接受的高残余应力。经过广泛的研究，发现空气辅助雾化喷雾具有出色的控制冷却速率的能力，它们是广泛使用的油或水淬火技术的可行替代品。传热数据是在很宽的压力比范围内获得的，因此表面温度高达850℃时水流。本文提供了一种神经学方法，用于对空气-燃料的使用过程中通过实验获得的数据进行传热分析。辅助雾化喷水系统，可控制高温锻件的冷却。为训练神经网络而创建的模型将喷雾输入变量与实验观察到的条件范围内的相应传热数据相关联。为了比较目的和对预测的准确评估，部分数据用于训练神经网络，其余部分用于测试模型。详细描述了如何训练神经网络以成功预测特定输入喷雾参数的最终热通量。然后，可以使用该特定知识来优化过程，即建立喷涂条件，该条件将产生达到预定机械性能所需的冷却速率，并使残余应力最小化。

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