计量与测试技术2024,Vol.51Issue(1) :24-27.DOI:10.15988/j.cnki.1004-6941.2024.1.008

基于稠密卷积网络的拉刀磨损在线预测方法

Online Prediction Method of Broach Wear Based on Dense Convolutional Network

张宇 田武郎 李宝明 郑华东 张顺琦
计量与测试技术2024,Vol.51Issue(1) :24-27.DOI:10.15988/j.cnki.1004-6941.2024.1.008

基于稠密卷积网络的拉刀磨损在线预测方法

Online Prediction Method of Broach Wear Based on Dense Convolutional Network

张宇 1田武郎 2李宝明 2郑华东 1张顺琦1
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作者信息

  • 1. 上海大学机电工程与自动化学院
  • 2. 浙江畅尔智能装备股份有限公司
  • 折叠

摘要

拉削是汽车制动钳支架槽的重要加工工艺.但加工过程中,若不能及时发现拉刀磨损异常,则会导致零件批量报废.本文提出一种拉刀磨损在线预测方法,采用拉刀信号振动特性,有效区分拉削过程与拉削间隙,并基于稠密卷积网络(DenseNet),构建拉刀磨损在线识别模型.结果表明:该方法自适应特征提取效果良好,泛化性和准确率均可实现实际加工过程拉刀磨损在线预测,对提高拉削生产效率和降低制造成本具有重要意义.

Abstract

Broaching is an important process of automobile brake caliper bracket groove.However,in the process of processing,if the broach wear anomaly can not be found in time,it will lead to batch scrap of parts.In this paper,an online prediction method for broach wear is proposed,which uses the vibration characteristics of broach signal to distinguish broach process and broach gap effectively,and constructs an online recognition model for broach wear based on DenseNet.The results show that the method has good adaptive feature extraction effect,and both generali-zation and accuracy can realize online prediction of broaching wear in actual machining process,which is of great significance for improving production efficiency and reducing manufacturing cost.

关键词

拉刀磨损在线监测/自适应特征提取/稠密卷积网络/注意力机制

Key words

broach wear on-line monitoring/adaptive feature extraction/dense convolutional network/attention mechanism

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基金项目

企业委托项目(TC220H05J)

出版年

2024
计量与测试技术
成都市计量监督检定测试所

计量与测试技术

影响因子:0.175
ISSN:1004-6941
参考文献量9
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