Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel). 2020 Oct 19;20(20):5907. doi: 10.3390/s20205907.
Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages-insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice.
刀具状态显著影响车床加工的产品质量和制造效率。本研究利用车床加工加速度计振动信号的功率谱密度分布，设计了一种适用于不同加工条件的刀具状态分类系统。对于四种常见的车床加工刀具状态（即积屑瘤、后刀面磨损、正常和断裂），本文采用两个阶段——刀具状态建模和加工模型融合，建立了刀具状态分类系统。在刀具状态建模阶段，根据加速度计振动信号的功率谱密度分布，提取分段频率的幅值特征。采用主成分分析和反向传播神经网络为不同的加工条件开发刀具状态模型。在加工模型融合阶段，采用反向传播神经网络建立加工条件和刀具状态模型之间的权函数。然后，根据所有刀具状态模型计算的权值对刀具状态进行分类。在数控机床（CNC）车床上进行了切削试验，验证了所设计的刀具状态分类系统的可行性。切削试验结果表明，所设计的系统能够在不同的加工条件下进行刀具状态分类，分类率超过 80%。使用三轴加速度计，所设计的刀具状态分类系统能够在不同加工条件下对四种常见刀具状态进行在线识别和分类，确保数控机床在实际生产中能够进一步提高制造质量和效率。
