使用人工神经网络和塔古奇-格雷关系分析预测和优化合金/MWCNT/RHA复合物的造控制参数,以获得多目标结果
Nitin Srivastava1, Manoj Kumar Yadav2, Selsam Ajith Arul Daniel3
1Department of Mechanical Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.
PloS one
|March 12, 2026
概括
使用灰色关系分析 (GRA) 和人工神经网络 (ANN) 对矩阵复合材料 (HAMMC) 的造参数进行优化,可显著提高机械性能. 该研究确定MWCNT含量是优越复合材料性能最有影响的因素.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 复合材料 复合材料 复合材料
背景情况:
- 矩阵复合材料 (AMC) 具有优越的性能,但需要针对特定应用进行优化.
- 碳纳米管 (CNT) 和米灰 (RHA) 是增强AMC机械性能的有希望的增强剂.
- 控制造参数对于在AMC中实现所需性能至关重要.
研究的目的:
- 研究造参数对AlP0507/CNT/RHA复合材料机械性能的影响.
- 用灰色关系分析 (GRA) 确定最佳的参数组合,以提高多目标性能.
- 为了验证人工神经网络 (ANN) 模型对抗拉强度的预测精度.
主要方法:
- 利用灰色关系分析 (GRA) 来优化造参数 (时间,速度,加工温度,钢筋含量).
- 使用人工神经网络 (ANN) 来预测制造的混合金属矩阵复合材料 (HAMMC) 的抗拉强度.
- 进行了变量分析 (ANOVA) 来确定每个参数贡献的意义.
主要成果:
- 该ANN模型准确地预测了拉伸强度,高R2得分为99.65%.
- 确定MWCNT含量是影响机械性能最重要的因素 (48.26%的贡献),其次是时间 (19.4%).
- 最佳参数组合 (A2B3C3D2E2) 产生了接近最高排名实验的GRG值,证实了优化准确性.
结论:
- 该研究成功优化了AlP0507/CNT/RHA复合材料的造参数,从而改善了机械性能.
- MWCNT含量和时间是提高造HAMMC性能的关键参数.
- 经过验证的优化方法为生产高性能金属矩阵复合材料提供了可靠的方法.
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