Abstract: Aiming at the uncertainty of various factors on surface roughness,a surface roughness prediction model based on Bayesian network was proposed.The energy features of time domain and frequency domain were extracted directly from cutting force and workpiece vibration.The association relationship between surface roughness and sensor signal features were mined with Bayesian network learning process,and the monitoring result was obtained according to the probability distribution of roughness range.The effectiveness of proposed method was verified by the roughness experiment of milling process.

Key words: surface roughness monitoring, Bayesian network, sensory features, machine learning, cutting machining

摘要： 针对加工过程中各种因素对表面粗糙度影响的不确定性,提出一种基于贝叶斯网络的表面粗糙度监测模型。直接从切削力和工件振动的传感器监测信号提取时域和频域能量特征,基于贝叶斯网络学习过程挖掘出表面粗糙度状态与信号特征的关联关系,从而根据粗糙度值域的概率分布得到监测结果。通过铣削加工过程的粗糙度监测实验验证了所提模型的有效性。

关键词: 表面粗糙度监测, 贝叶斯网络, 传感器信号特征, 机器学习, 切削加工