首页|基于IDRSN-BiLSTM的铣削加工表面粗糙度预测方法

基于IDRSN-BiLSTM的铣削加工表面粗糙度预测方法

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针对传统的表面粗糙度预测方法过度依赖人工提取特征以及预测精度较低的问题,提出一种基于Inception模块改进的深度残差收缩网络(IDRSN)和双向长短时记忆网络(BiLSTM)的表面粗糙度预测方法.首先,利用深度残差收缩网络(DRSN)中软阈值化结构和注意力机制对输入信号进行降噪处理.其次,引入Inception模块构建IDRSN以提升网络的多尺度信息获取能力,实现自适应多尺度特征提取.然后,引入反向长短期记忆(LSTM)构建BiLSTM预测网络,利用正反两个LSTM提高网络捕捉历史和未来完整信息的能力.最后,进行实验验证,分别对比IDRSN、DRSN、BiLSTM和人工提取特征 4 种方法的提取特征效果,以及BiLSTM、卷积神经网络(CNN)、DRSN和CNN-LSTM 4 种表面粗糙度预测模型的预测精度.结果表明所提方法具有较高的预测精度,为铣削加工表面粗糙度预测奠定了方法基础.
Roughness prediction method of milling surface based on IDRSN-BiLSTM
To address the problem that the traditional milling surface roughness prediction method relies excessively on signal processing knowledge to extract features and has low prediction accuracy,a surface roughness prediction method based on a deep residual shrinkage network improved by the inception module(IDRSN)and a bidirectional long-short-term memory network(BiLSTM)is proposed.Firstly,the input signal is noise reduced using the soft thresholding structure and attention mechanism in the deep residual shrinkage network.Secondly,the Inception module is introduced to build IDRSN to enhance the multiscale information acquisition capability of the network for adaptive multiscale feature extraction.Then,a bidirectional recurrent network structure is introduced to construct a BiLSTM prediction network,which utilizes both positive and negative LSTM to improve the network's ability to capture complete information about the past and the future.Finally,experiments verify the effects of four methods of extracting features,IDRSN,DRSN,BiLSTM and manually extract features,and the prediction accuracy of four surface roughness prediction models,BiLSTM,CNN,DRSN,and CNN-LSTM,are compared respectively.It is shown that the proposed method has a high prediction accuracy and establishes a method basis for surface roughness prediction of milling machining.

surface roughness predictiondeep residual shrinkage networkinception moduleadaptive feature extractionidirectional long-term short-term memory network

陈佳琳、尚志武、张雷

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天津工业大学天津市现代机电装备技术重点实验室 天津 300387

天津商业大学机械工程学院 天津 300134

天津市天森智能设备有限公司 天津 300300

粗糙度预测 深度残差收缩网络 Inception模块 自适应特征提取 双向长短时记忆网络

天津市高等学校科技发展基金天津市自然科学基金重点项目

2021KJ17621JCZDJC00770

2024

10.19650/j.cnki.cjsi.J2312030

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心
影响因子：2.372
ISSN：0254-3087
年,卷(期)：2024.45(4)
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