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反向传播基于神经网络的预测模型,用钻石丝切割的大理石表面质量

Hui Dong^1,2, Fan Cui¹, Zhipu Huo¹

¹Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China.

|September 27, 2025

概括

这项研究引入了一种改进的鱼优化算法-反向传播 (IWOA-BP) 神经网络,用于预测钻石丝后的大理石表面质量. 该模型准确地预测了表面粗度和波纹,提高了加工效率.

科学领域:

材料科学与工程材料科学与工程
人工智能在制造业中的应用
表面计量学表面计量学

背景情况:

大理石在建筑中的广泛使用需要精确的切割技术.
钻石丝提供了高效的大理石切割,但需要准确的表面质量预测.
由于复杂的参数相互作用,传统模型难以预测表面质量.

研究的目的:

开发一个非常准确的预测模型大理石表面粗度 (Ra) 和波纹 (Wa) 后钻石丝.
在预测表面质量方面解决经验和线性模型的局限性.
为了优化反向传播 (BP) 神经网络,使用一种新的算法来提高性能.

主要方法:

在不同的工艺参数下,采用钻石丝切割对大理石表面质量 (Ra,Wa) 的实验数据收集.
开发一个反向传播 (BP) 神经网络模型.
优化BP网络的初始权重和值,使用混合策略改进的鱼优化算法 (IWOA),创建IWOA-BP模型.

主要成果:

IWOA-BP神经网络模型在表面粗度 (Ra) 和波状度 (Wa) 两方面都表现出卓越的预测准确性.
实现了最小的预测误差:根平均平方误差 (RMSE) 为0.0342%,平均绝对误差 (MAE) 为0.0284%,平均绝对百分比误差 (MAPE) 为1.5614%.
与传统方法相比,验证证实了IWOA-BP模型的增强预测能力.

结论:

IWOA-BP神经网络提供了一种强大而准确的方法,用于预测钻石丝后的大理石表面质量.
这种预测能力支持在大理石生产中提高后续加工效率和经济效益.
这项研究提供了宝贵的实验数据和技术见解,用于优化钻石丝工艺.

关键词:

在BP神经网络中,神经网络钻石丝的切割钻石丝的切割改进了鱼优化算法.大理石大理石是什么?大理石大理石是什么表面质量表面质量

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