Study On Cutting Process Monitoring And Optimization Based On Multi-Sensors

In the cutting process, efficiency of processing and the quality ofproducts will be badly influenced by any faults of machine tools andequipments and the cost of operation will be added. Consequently, Usingthe monitoring and optimization to the processing of machining has theimportant effect on improving efficiency of the lathe and quality ofproduct, reducing the rate of waster, the cost of machining and theconsume of the materials, easing the intension of labor and improvingthe safety of the produce.This paper particularly introduce signal processing technologyincluding pretrement methods, analysis and modeling of time-domain,analysis and modeling of frequency-domain and wavelet analysis;monitoring algorithm including fuzzy theory and ANN; optimizationalgorithm including GA. These signal processing technology andcorrelative algorithm act as the foundation of for cutting processmonitoring and its optimization. Because of the importance of cuttingforce, the paper achieve the development on two systems of cuttingforce acquisition based on PCI and USB by Labview according to threeparts which are choice of hardware, demarcating of sensor and writingof software. These systems act as the foundation of development on test model of cutting force, processing optimization and tool wearmonitoring.In cutting process optimization, based on the analysis of cuttingdosage's effect on cutting force, put forward that cutting force forecastmodel based on Least Two-multiply regression and RBF ANN andvalidate its feasibility and feasibility; besides, bring forward that cuttingparameters optimization scheme aiming to maximal efficiency satisfyingquality and achieve its by GA. Experimentation prove that it has muchmore theory visualizability and maneuverability.In cutting process monitoring, the object of development is tool wearcondition monitoring which is the key technology of cutting processmonitoring, according the testing and academic angles, research the toolwear condition's effect on steady and dynamic cutting force; research therelevancy between the AE signal's characteristic parameters and toolwear; research tool wear condition's effect on AC signals by AR modelso as to acquire the characteristic parameters which can effect the toolwear. Based on the characteristic vector that reflect tool wear conditionis made of those characteristic parameters which are sixth waveletanalysis coefficient of AE signals, remnant of fifth rank AR model ACsignals, breadth value of curly libration frequency of tool (14kHz).Achieve the tool wear state identification by fuzzy theory method.Experimentation prove that it has much more theory visualizability and maneuverability than the usual prediction method which based on ANN,especially suit subsequent tool wear monitoring.