使用模拟,统计和机器学习技术预测C355/化/石墨烯混合纳米复合材料的电线放电加工表面粗度
Suresh Vellingiri1, Ravi Kumar Tata2, Sumalatha Manne3
1Department of Mechanical Engineering, KIT-Kalaignarkarunanidhi Institute of Technology (Autonomous), Coimbatore, 641 402, Tamil Nadu, India. winsureshv2011@gmail.com.
Scientific reports
|February 26, 2026
概括
机器学习模型准确地预测了混合复合材料的线电放电加工 (WEDM) 的表面粗度. 支持向量回归 (SVR) 在分析这种先进的制造过程中表现出卓越的性能.
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
- 计算智能是一种计算智能.
背景情况:
- 线电放电加工 (WEDM) 是一种用于复杂形状的非传统加工工艺.
- 合金C355混合复合材料,增强化 (Si3N4) 和石墨烯纳米粒子 (GNP),提供增强的材料性能.
- 在WEDM中优化这些先进复合材料的表面粗度对于性能和耐用性至关重要.
研究的目的:
- 为了研究和预测WEDM加工的C355混合复合材料的表面粗度.
- 为了比较机器学习 (ML) 模型的有效性,包括支持向量回归 (SVR) 和人工神经网络 (ANN),与响应表面方法 (RSM).
- 确定影响表面粗度的最有影响力的WEDM参数.
主要方法:
- 在Si3N4和GNP增强合金C355.5上进行实时WEDM实验.
- 采用SVR,ANN和RSM用于模拟和预测表面粗度.
- 利用基于网格的搜索来优化SVR参数,并确定ANN架构 (输入隐藏输出层).
主要成果:
- SVR显示出最高的预测准确度 (R=0.997603),平均绝对百分比误差 (MAPE) 最低的0.0748,表现优于ANN和RSM.
- 差异分析 (ANOVA) 显示,峰值电流是最重要的WEDM参数,对表面粗度变化贡献了60.21%.
- 结合的SVR和ANN方法有效地分析和预测混合复合材料的WEDM表面粗度.
结论:
- 机器学习,特别是SVR,为预测先进混合复合材料在WEDM中的表面粗性提供了一种高度准确和高效的方法.
- 该研究强调了整合模拟,统计和ML技术的潜力,以优化WEDM过程.
- 这些发现为在使用这些新型复合材料的行业中增强减法制造工艺提供了宝贵的见解.
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