Study on DBN Prediction Model Driven by Tool Wear Sensing Data
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摘要: 针对制造车间数控刀具在连续作业过程中易出现过度使用或提前置换的现状, 对刀具磨损感知数据获取方法和磨损预测模型构建进行研究。为避免传感器噪声影响, 采用OPC技术直接与机床协同完成数控通信, 并设计一套双镜头垂直分布的感知数据获取系统; 为增强预测模型泛化能力, 采用Dropout优化后深度信念网络(DBN)作为预测模型, 先在特征提取阶段重构出优化权值, 再引入标签量训练特征匹配阶段。结果显示, 改进的DBN算法平均预测准确度约96.0%, 在预测精度和稳定性方面较传统模型显著改善。Abstract: Aiming at the current situation that Computer numerical control(CNC) tools in the manufacturing workshop are prone to overuse or replacement in the course of continuous operation, the method for obtaining tool wear perception data and the establishment of wear prediction model framework are studied. In order to avoid the influence of the sensors noise, OPC technology is used to directly cooperate with the machine tool to complete the CNC communication, and a set of dual-lens vertical distribution of the perceptual data acquisition system is designed; in order to enhance the generalization ability of the prediction model, Dropout optimized deep belief network (DBN) is used to establish the prediction model, the optimal weights are reconstructed at the feature extraction stage, and then the feature matching stage is introduced for training the tag amount. The results show that the average accuracy of the improved DBN algorithms prediction is about 96.0%, which is significantly improved comparing with the prediction accuracy and reliability of the traditional models.
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Key words:
- image acquisition /
- life cycle /
- data learning /
- dropout /
- tool wear prediction
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表 1 刀具全生命周期内检测数据对比
行程/mm 实验值/mm 真实值/mm 绝对误差/mm 1 000 0.035 0.039 0.004 2 000 0.122 4 0.119 0.003 4 3 000 0.146 4 0.145 0.001 4 ⋮ ⋮ ⋮ ⋮ 11 000 0.395 1 0.386 0.009 1 12 000 0.518 1 0.514 0.004 1 
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表 2 刀具磨损状态识别
刀面磨损VB/mm 刀具磨损状态 刀面磨损VB/mm 刀具磨损状态 [0, 0.04) 初期磨损阶段① 0.21, 0.24) 急剧磨损阶段⑤ [0.04, 0.11) 初期磨损阶段② [0.24, 0.3) 严重磨损阶段⑥ [0.11, 0.16) 正常磨损阶段③ [0.3, 0.4] 严重磨损阶段⑦ [0.16, 0.21) 急剧磨损阶段④ 大于0.4 废置阶段⑧
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表 3 样本数据采集
序号 因素变量 水平度 1 主轴转速N/(r·min-1) 3 2 切削速度Vc/(mm·min-1) 3 3 进给量f/(mm·r-1) 3 4 背吃刀量ap/mm 3 5 进给速度Vf/(mm·min-1) 3 6 冷却情况(开/关) 2 7 刀具磨损状态 7
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表 4 对比模型误差比较
网络模型 预测准确度/% MSE MAPE DBN 92.6 3.42×10-4 0.074 改进的DBN 96.0 1.05×10-4 0.040 BP 72.8 3.8×10-3 0.272 深度BP 88.5 1.05×10-3 0.115 SVR 91.8 3.01×10-4 0.082
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