A new technique for lubrication control based on friction conditions in CNC machine linear guideways using servomotor current signals in feed drive system / Mahdi Sparham

Mahdi, Sparham (2015) A new technique for lubrication control based on friction conditions in CNC machine linear guideways using servomotor current signals in feed drive system / Mahdi Sparham. PhD thesis, University of Malaya.

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Abstract

Computer Numerical Control (CNC) machining is currently one of the most important machining processes for manufacturing in industry. This is because the CNC machine offers favorable accuracy and precise machining. However, the frictional behavior in linear guideways is one of the most crucial challenges with CNC machining, as higher friction produces motion and accuracy problems. Lubrication in CNC machine guideways is used to overcome such problems. Currently, a predefined lubrication system via CNC coding command is used to lubricate guideways in CNC machines. This lubrication process is performed based on fixed times and amounts of oil injection regardless of cutting force value, weight and size of the workpiece, friction force on the guideways, and machining process conditions. This leads to less protection of guideways against high friction force or excessive oil consumption to attain less friction force in the guideways. For these reasons, a new lubricant control technique is introduced based on the friction conditions in CNC machine linear guideways. Controlling the lubricant amount and time based on friction force values in linear guideways exhibits a major difference between the new proposed lubrication technique and existing systems. First, the friction force values in dry lubrication conditions in linear guideways and in the X and Z directions are calculated from the cutting force measured via force analysis in the guideways. Second, servomotor current is predicted using adaptive neuro fuzzy inference system (ANFIS) modeling, as the ANFIS system is ideal for predicting motor current in dry lubrication conditions due to the nonlinear condition in the machining process. Third, the friction forces predicted with ANFIS modeling are verified using a new testing scheme to identify the average predicted error and accuracy, which were found to be 1.38% and 98.62% respectively, on the Z-axis. Because the predictive errors are low, the ANFIS model is used to build an oil feedback, lubrication control unit (LCU) to be able to predict friction force values based on a comparison between online measured and predicted servomotor current signals. The LCU sends a signal to the actuators to trigger the oil pump to inject oil. The oil injection amount and time (pump response interval (PRI)) are identified based on the ANFIS-predicted friction force values in the linear guideways. The predicted friction force values are categorized into five levels corresponding to five servomotor current levels. In each level, the PRI is defined optimally to protect the guideways. Finally, the controller’s performance is verified through a new set of experiments where the proposed lubrication technique results are compared with ordinary lubrication system results. In these tests, several parameters are monitored in both lubrication systems, including temperature, servomotor current, cutting force, PRI, and oil consumption. The verification experiments are prepared for five cutting force levels, including low, medium, and high, to ensure correctness and control accuracy in different machining conditions. According to the results, the measured temperature, servomotor current, and cutting force values are nearly the same in both lubrication processes. However, oil pumping time and amount are different. The PRI in the new lubrication control technique shows significant reduction of oil consumption by 67% for finishing, 33% for medium turning and 100% for rough turning compared with ordinary lubrication modes. The new technique can control not only the lubrication oil based on friction conditions in CNC machine linear guideways but is also useful in terms of cost effectiveness and preventing environmental pollution.

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