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使用双重方法对电放电钻石研磨 (EDDG) 系统进行参数优化

Vijay Kumar¹, Shailendra Kumar Jha²

¹Mechanical Engineering, IIMT College of Engineering, Greater Noida, India.

Network (Bristol, England)

|August 11, 2025

概括

一种新的修改的狮优化 - 人工神经网络 (MALO-ANN) 技术优化了电放电钻石研磨 (EDDG) 过程. 这种方法显著提高了耐用,导电材料的材料去除率和表面粗度.

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
人工智能的人工智能

背景情况:

导电材料由于其强度和刚性,对许多应用至关重要.
电放电钻石研磨 (EDDG) 是生产这些材料的关键方法.
传统的人工神经网络 (ANN) 模型经常面临性能问题,原因是潜在的隐藏层和权重.

研究的目的:

引入和评估修改后的狮优化-人工神经网络 (MALO-ANN) 技术.
为了提高EDDG过程的性能和参数优化.
调查输入因素对材料去除率 (MRR) 和表面粗度 (SR) 的影响.

主要方法:

这项研究使用了修改后的狮优化 (MALO) 算法来优化人工神经网络 (ANN) 的权重和隐藏层.
输入参数包括砂大小,脉冲开/关持续时间和电流被系统分析.
应用MALO-ANN模型来预测和优化EDDG过程中的MRR和SR.

主要成果:

马洛-安恩技术在EDDG的参数优化方面取得了显著的改进.
优化的模型实现了高精度,MRR和SR的绝对误差间隔从1.03%到4.49%不等.
实现了89%的收率,这表明EDDG操作的效率和准确性有所提高.

结论:

与传统的ANN模型相比,MALO-ANN方法为优化EDDG流程提供了一种优越的方法.
这种技术显示出提高耐用,导电材料制造效率和精度的巨大潜力.
这项研究验证了MALO-ANN在实现最佳材料去除率和表面粗度方面的有效性.

关键词:

人工神经网络的人工神经网络碳化物碳化物电气放电钻石研磨系统钻石研磨系统材料去除率;表面粗度和修改的狮优化算法.

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