用高压冷却液喷射来磨Ti-6Al-4V合金的多模拟和优化的比较评估和灵敏度分析
Mst Nazma Sultana1, Nikhil Ranjan Dhar2
1Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh.
Heliyon
|July 31, 2023
概括
优化磨 Ti-6Al-4V 合金使用预测模型,如自适应神经模糊推理系统 (ANFIS) 和基于比率分析 (MOORA) 的多目标优化,实现了卓越的性能. 该研究确定了改善可加工性和产品质量的最佳参数.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造过程 制造过程 制造过程
- 计算机建模 计算建模
背景情况:
- 有效的冷却和精确的工艺参数选择对于加强加工性能,减少资源损失和提高产品质量至关重要.
- 数学建模和优化是预测响应和在动态制造环境中选择最佳过程参数的重要工具.
- 提高Ti-6Al-4V合金的可加工性需要精确预测和优化削性能特征.
研究的目的:
- 开发数学模型来预测Ti-6Al-4V合金的削性能特征 (切削力,特定切削能量,表面粗性).
- 为了比较评估响应表面方法 (RSM),人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 预测模型的准确性.
- 使用基于比率分析 (MOORA) 的多目标优化方法优化削参数,由主要组件分析 (PCA) 提供信息.
主要方法:
- 使用响应表面方法 (RSM),人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 开发预测数学模型.
- 对RSM,ANN和ANFIS模型预测准确性的比较分析.
- 应用主要组件分析 (PCA) 来确定响应权重,随后是基于比率分析 (MOORA) 的多目标优化来进行参数优化.
主要成果:
- 适应性神经模糊推理系统 (ANFIS) 被确定为评估方法中最准确的预测模型.
- 使用PCA-MOORA方法,建议Ti-6Al-4V合金在32m/min的速度,22mm/min的料速率和0.75mm的切割深度下具有最佳的削参数,采用旋转高压冷却.
- 摩拉方法的敏感性分析证实了决策过程的稳定性.
结论:
- 与RSM和ANN相比,ANFIS为削性能特征提供了更高的预测准确度.
- 结合的PCA-MOORA方法有效地优化了Ti-6Al-4V合金的削参数,从而提高了加工能力.
- 该研究展示了通过综合建模和优化技术优化复杂加工过程的强大方法.
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