Design Of Cloud Monitoring System For Robotic Hole Milling And Triming For Bathtub

With the increasing level of national consumption,people’s demand for high-end sanitary products is also rising.Bathtub has gradually become a necessary sanitary product for all households.The scale of bathtub market is also growing and has broad prospects.Under this supply-demand relationship,domestic bathtub manufacturers began to increase production capacity by replacing people with machines.Although a large number of bathtub manufacturers have transformed to automatic production,their level of informatization and intelligence is not high.When the bathtub processing robot occurs faults or abnormalities in the automatic processing process,because the historical processing data in the hole milling and trimming process are not retained and fully utilized,the robot cannot make correct feedback and early warning on the faults or abnormalities,technicians often have no way to know the exact cause of the fault or abnormality,and can only guess by experience.Aiming at the above problems,this thesis designs and develops a set of robot hole milling and trimming cloud monitoring system for bathtub,based on the cloud platform,the cloud anomaly recognition of bathtub hole positioning,the cloud correction of hole positioning teaching results,and the failure form cloud diagnosis service of bathtub cutting brazing sheet are realized.The system management platform is built and applied to enterprises.Firstly,starting from the hardware module and processing technology of the bathtub processing robot,this thesis analyzes the requirements of the cloud monitoring system,designs the data transmission architecture of the system,selects the data acquisition hardware according to the data transmission architecture,and designs the cloud architecture of the system according to the data monitoring and analysis function of the system.Then,aiming at the problem of abnormal hole positioning caused by special bathtub surface features of bathtub processing robot,a bathtub hole positioning anomaly recognition method based on PSO-SVM algorithm is proposed.The feature extraction and model training are carried out on the bathtub hole positioning data scanned by point laser displacement sensor,and the optimal model for cloud anomaly recognition is obtained.Aiming at the problem that the current hole positioning algorithm of PLC has poor effect when applied to the positioning of white smooth concave face,an improved algorithm is proposed,which optimizes the inflection point detection part of PLC algorithm,and compares the hole positioning operation results of the two algorithms through experiments.The results show that the solution result of the improved algorithm is closer to the target value.Then,aiming at the three common failure forms of diamond brazed sheets used for bathtub trimming,a failure form diagnosis method of brazed sheets based on feature recognition model is proposed.The vibration signals generated by different forms of brazed sheets are analyzed by wavelet packet analysis and empirical mode decomposition.57 elements are selected as eigenvalues,and the training results of different models are compared,The optimal RNN neural network model is selected for the cloud diagnosis service of brazed sheet failure form.Finally,the hardware construction of the system is completed,and the cloud data monitoring and analysis function of the system is realized.The hole positioning cloud anomaly recognition function of the system is verified through experiments,and the cloud correction function of hole positioning teaching results is realized.After hole milling,it is further verified that this method improves the hole positioning accuracy.Secondly,the feasibility and reliability of the cloud diagnosis service of brazing sheet failure form are verified by the bathtub trimming experiment.In addition,a system management platform is built based on springboot and Vue and runs in the cloud,realizing the practical application of the platform in enterprises.