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基于轻量级卷积网络的铣削粗糙度在机监测研究

刘岳开 高宏力 郭亮 由智超 李世超

刘岳开, 高宏力, 郭亮, 由智超, 李世超. 基于轻量级卷积网络的铣削粗糙度在机监测研究[J]. 西南交通大学学报, 2024, 59(1): 193-200. doi: 10.3969/j.issn.0258-2724.20210959
引用本文: 刘岳开, 高宏力, 郭亮, 由智超, 李世超. 基于轻量级卷积网络的铣削粗糙度在机监测研究[J]. 西南交通大学学报, 2024, 59(1): 193-200. doi: 10.3969/j.issn.0258-2724.20210959
LIU Yuekai, GAO Hongli, GUO Liang, YOU Zhichao, LI Shichao. In-situ Roughness Evaluation of Milling Machined Surface Based on Lightweight Deep Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 193-200. doi: 10.3969/j.issn.0258-2724.20210959
Citation: LIU Yuekai, GAO Hongli, GUO Liang, YOU Zhichao, LI Shichao. In-situ Roughness Evaluation of Milling Machined Surface Based on Lightweight Deep Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 193-200. doi: 10.3969/j.issn.0258-2724.20210959

基于轻量级卷积网络的铣削粗糙度在机监测研究

doi: 10.3969/j.issn.0258-2724.20210959
基金项目: 国家自然科学基金(51775452)
详细信息
    作者简介:

    刘岳开(1994—),男,博士研究生,研究方向为铣削加工状态监测,E-mail:lieuyk@my.swjtu.edu.cn

    通讯作者:

    高宏力(1971—),男,教授,研究方向为智能机械状态监测与故障诊断,E-mail:hongli_gao@swjtu.edu.cn

  • 中图分类号: TP277;TG547

In-situ Roughness Evaluation of Milling Machined Surface Based on Lightweight Deep Convolutional Neural Network

  • 摘要:

    传统机器学习类方法对光源类型、设备安装误差等因素较为敏感,需要反复调试与实验,难以实现规模化生产的自动检测. 针对上述问题,提出了一种铣削粗糙度在机监测方法,有效提升了检测效率和准确性. 首先,采用低感度参数设置的方向梯度直方图特征的候选框提取算子实现铣削工件的定位,并基于点匹配算法校正安装误差;然后,通过清晰度评价指标实现工业相机对焦过程优化;最后,构建了一种面向移动端实时计算的轻量级卷积神经网络模型,可对不同粗糙度工件表面纹理进行分类,并在立铣加工纹理数据集上进行了实验验证. 实验结果表明:相比普通卷积神经网络,在模型复杂度相似的情况下,以乘、加运算次数为指标,提出模型推理所需运算量减少55%;代价敏感函数的引入能有效提升粗糙度识别模型对不平衡数据的稳定性;所提方法与传统机器学习方法相比,在检测帧率、图像分辨率相同的实验条件下,精准率、召回率分别提高了8%、21%.

     

  • 图 1  可分离卷积结构

    Figure 1.  Structure of separable convolution

    图 2  立铣粗糙度在线监测模型构建

    Figure 2.  In-situ roughness evaluation model of end milling

    图 3  直方图均衡化前后纹理图像对比

    Figure 3.  Comparison of texture images before and after histogram equalization

    图 4  基于HOG直方图统计信息的特征提取示意

    Figure 4.  Feature extraction based on HOG

    图 5  特征点匹配结果

    Figure 5.  Feature point matching results

    图 6  代价敏感损失中置信度与损失值之间变化关系

    Figure 6.  Relationships between confidence and loss value in cost-sensitive loss

    图 7  不同倍率、粗糙度下铣削纹理图样

    Figure 7.  Texture images under different microscopic magnifications and roughness levels

    图 8  立铣加工监测实验的纹理图像采集与标注

    Figure 8.  Texture image collection and labeling in end milling monitoring experiment

    图 9  刀具进展性磨损下粗糙度变化

    Figure 9.  Roughness change under progressive tool wear

    图 10  不同清晰度指标的采集过程响应曲线

    Figure 10.  Responding curves of collection process with different clarity indexes

    图 11  提出模型的训练过程可视化

    Figure 11.  Training visualization of the proposed network

    图 12  对比实验的混淆矩阵

    Figure 12.  Confusion matrices for comparative experiment

    表  1  监测实验工况设置明细

    Table  1.   Details of monitoring experiment

    工况主轴转速/
    (r·min−1
    进给速度/
    (mm·min−1
    切削深度/mm刀具
    类型
    机床
    型号
    130002001C1KVC650
    240002001C2KVC650
    320002000.5C2VMC850
    下载: 导出CSV

    表  2  可分离卷积结构的消融实验结果

    Table  2.   Ablation experiment for separable convolution

    模型结构推理速度/ms准确率/%
    普通卷积33.777.6
    可分离卷积(α=0.2)25.275.7
    可分离卷积 2(α=0.3)25.172.1
    下载: 导出CSV

    表  3  普通交叉熵损失和代价敏感损失函数测试结果

    Table  3.   Test results of standard cross-entropy and cost-sensitive loss %

    损失函数精准率召回率
    工况 1工况 3工况 1工况 3
    普通交叉熵79.177.580.278.3
    代价敏感损失78.779.980.680.3
    下载: 导出CSV

    表  4  模型性能评估测试

    Table  4.   Model performance evaluation

    方法P/%R/%计算时间/(s·帧−1
    GLCM + SVM (RBF)73.765.30.208
    GLCM + SVM (linear)73.265.50.208
    GLCM + RF73.465.10.208
    提出方法81.686.20.150
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-22
  • 修回日期:  2022-04-21
  • 网络出版日期:  2023-02-07
  • 刊出日期:  2022-04-27

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