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基于机器学习的TiAl合金热的最终滚动温度预测

Wei Lian¹, Fengshan Du¹, Qian Pei²

¹National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, China.

Materials (Basel, Switzerland)

|April 24, 2025

概括

准确预测- (TiAl) 合金的最终压温度对于材料性能至关重要. 一个基于新型遗传算法的BP神经网络 (GABP) 模型实现了高精度温度预测,这对于航空航天应用至关重要.

科学领域:

材料科学材料科学材料科学
金工业是金工业的一个方面.
人工智能的人工智能

背景情况:

最后的滚动温度对谷物再结晶和材料的机械性能产生了关键的影响.
航空航天行业需要高质量的TiAl合金,需要精确控制其狭窄和高温滚动范围.
传统的有限元分析太慢,无法实时在线监测滚动温度.

研究的目的:

开发一个准确和高效的模型来预测TiAl合金的最终滚动温度.
为应对TiAl合金接中实时温度控制的挑战.
优化制计划,并实现在线控制,以改善TiAl合金生产.

主要方法:

提出了一个基于遗传算法的BP神经网络 (GABP) 预测模型.
使用MATLAB分析各种因素对最终滚动温度的影响,以确定最佳的神经网络输入.
将GABP模型的性能与模糊神经网络 (FNN) 进行了比较.

主要成果:

GABP模型表现出高预测准确度,主要在0-1°C范围内出现错误.
拟议的GABP模型在预测准确性方面明显优于模糊神经网络 (FNN).
该模型有效地预测了TiAl合金的最终滚动温度.

结论:

该GABP模型提供了一个可行的解决方案,用于准确和实时预测TiAl合金的最终滚动温度.
这种方法有助于提高TiAl合金制造中的质量控制和流程优化.
开发的模型适用于航空航天行业的在线监控和控制应用.

关键词:

合金是TiAl的合金之一.完成滚动温度的完成模糊理论模糊理论遗传算法是一种遗传算法.神经网络的神经网络的神经网络

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