使用RSM,DNN-GA,KNN,LM,DT,SVM模型对钢W18CR4V研磨的性能分析,以及通过可取性函数和MOGWOWO的优化
Sofiane Touati1, Haithem Boumediri2, Yacine Karmi3
1Mechanical Engineering Department, University Abbes Laghrour, BP. 1252, 40004 Khenchela, Algeria.
Heliyon
|March 14, 2025
概括
这项研究利用机器学习和多目标优化优化了W18CR4V钢材研磨. 先进的模型显著降低了表面粗度,并提高了高精度应用的制造效率.
科学领域:
- 制造业 工程 制造工程
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- W18CR4V钢对于轮机制造至关重要,要求优化研磨工艺.
- 实现高表面质量 (低Ra,Rz) 和高效的生产时间是具有挑战性的.
- 现有的方法缺乏复杂的多目标研磨优化精度.
研究的目的:
- 开发和验证一种创新的方法,以优化W18CR4V钢的研磨.
- 将先进的机器学习模型与多目标优化技术相结合.
- 提高表面表面 (Ra,Rz) 并减少精密研磨的生产时间.
主要方法:
- 使用的机器学习模型:带有遗传算法 (DNN-GA) 的深度神经网络,K-近邻 (KNN),Levenberg-Marquardt (LM),决策树 (DT) 和支持矢量机器 (SVM).
- 使用多目标优化技术,包括可取性函数和多目标灰狼优化 (MOGWO).
- 预测和优化关键研磨结果:表面粗度 (Ra),最大粗高度 (Rz) 和生产时间.
主要成果:
- 在Ra (高达81.5%) 和Rz (高达77.7%) 中实现了显著的减少.
- 混合DNN-GA模型表现出高精度 (R2>0.99) 并将预测误差降低了23-45%.
- 可取性 函数优化得出 Ra ~0.341 微米,Rz ~2.3 微米. 莫格沃提供了帕雷托最佳解决方案,平衡了表面质量和效率.
结论:
- 机器学习模型和优化技术的整合提供了一种强大的方法来提高研磨性能.
- 这种新的方法显著提高了W18CR4V钢的表面质量和制造效率.
- 该研究为优化高精度工业中复杂加工过程提供了强大的框架.
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