在使用ML模型的反向μEDM制造微电极阵列中,对表面粗度的预测建模和优化
Suresh Pratap1, Prakash Kumar2, Hreetabh Kishore3
1G.L. Bajaj Institute of Technology and Management, Greater Noida, UP, 201306, India. sureshpratap@yahoo.com.
Scientific reports
|January 6, 2026
概括
这项研究使用反向微电放电加工 (Reverse-μEDM) 制造了微电极阵列 (MEAs),优化了用于神经信号记录的表面粗度. 随机森林模型准确地预测了粗度,这对于医疗应用至关重要.
科学领域:
- 材料科学与工程 材料科学与工程
- 生物医学工程 生物医学工程
- 制造业 制造技术 制造技术
背景情况:
- 微电极阵列 (MEAs) 对于医疗保健中的神经信号采集至关重要.
- 在MEA中达到最佳的表面粗度对于信号准确性至关重要.
- 反向微电放电加工 (反向μEDM) 为MEA制造提供了高精度.
研究的目的:
- 使用反向μEDM制造MEAs.
- 为了调查和优化表面粗度参数.
- 使用机器学习模型预测表面粗度.
主要方法:
- 通过反向μEDM制造MEAs.
- 塔古奇的L18实验设计用于分析电压,电容和料速率效应.
- 使用非接触型度测量和扫描电子显微镜 (SEM) 进行表面粗度分析.
- 机器学习模型 (ANN,SVR,随机森林,梯度增强,GPR) 用于表面粗度的预测.
- 交叉验证 (LOOCV) 用于绩效评估.
主要成果:
- 电容是影响表面粗度的主要因素 (86%),其次是电压 (11%).
- 随机森林回归实现了对表面粗度的最高预测精度 (R2值,MAE = 0.18μm).
- 人工神经网络 (ANN) 显示了可比的准确性,但需要更长的训练时间.
结论:
- 反向μEDM是一种适合制造高精度MEA的方法.
- 机器学习,特别是随机森林,可以有效地预测MEAs的表面粗度.
- 可实现优化的MEA表面粗度,增强神经信号记录能力.
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