Surface Roughness Prediction Method of CNC Milling Based on Multi-source Heterogeneous Data

To overcome the poor generalization and low accuracy of the traditional surface roughness prediction model of CNC milling, a novel surface roughness prediction method of CNC milling was proposed based on multi-source heterogeneous data. Firstly, the static data such as processing parameters, tool diameter and workpiece material and dynamic data such as vibration signals, force signals and power signals were collected in CNC milling with variable technologies. Then, particle swarm optimization(PSO) algorithm was used to optimize the network structure parameters of CNN for obtaining PSO-CNN, which might adaptively extract the features of dynamic data. Features of static data were manually extracted. A shallow neural network was carried out to fuse the features of multi-source heterogeneous data such as dynamic data and static data, which might be used to build surface roughness prediction model of CNC milling with variable technologies. Finally, the superiority of the proposed method was demonstrated according to the performance comparison tests with different surface roughness prediction models. And, the validity of the proposed method was verified by the example of two workpiece machining. © 2022, China Mechanical Engineering Magazine Office. All right reserved.