使用多层感知器人工神经网络和遗传算法优化灵活成型过程:用于先进高强度钢的通用方法
Luka Sevšek1, Tomaž Pepelnjak1
1Forming Laboratory, Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia.
Materials (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种使用人工神经网络和遗传算法来减少在柔性成型过程中薄化金属板的新方法. 这种优化提高了先进的高强度钢材的材料可塑性.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 计算智能是一种计算智能.
背景情况:
- 灵活的成型工艺使各种产品形状能够在不需要重新装备的情况下形成.
- 单点增量成型 (SPIF) 提供了灵活性,但与过度的板材稀释作斗争.
- 预成型阶段可以提高SPIF的厚度均性.
研究的目的:
- 开发一种通用方法,以尽量减少SPIF中的金属薄化.
- 优化SPIF工艺参数以提高可塑性,特别是对于先进的高强度钢 (AHSS).
- 整合人工智能来预测和控制稀释.
主要方法:
- 利用多层感知人造神经网络 (MLP ANN) 来根据输入参数预测稀释.
- 采用遗传算法 (GA) 来优化这些输入参数以实现最小的稀释.
- 专注于先进的高强度钢 (AHSSs) 的应用和可塑性.
主要成果:
- 成功演示了一种通用方法来预测和最大限度地减少金属板薄化.
- 优化的SPIF参数有效减少了材料稀释.
- 提供了关于AHSS在增量形成过程中的可成形性的宝贵见解.
结论:
- 拟议的MLP ANN和GA方法有效地减少了SPIF期间的板材稀释.
- 这种方法在柔性成型技术中提供了显著的进步.
- 该方法适用于广泛的材料和工业应用,特别是AHSS.
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