Advanced technologies and manufacturing are basics of countries development in industries, whereas lubrication is one the foremost important consideration in machine and technology reliability and reproducibility. The frictional behavior in the linear guideways is one of the most crucial challenges for CNC machining accuracy, as higher friction produces motion and accuracy problems due to stick-slip motion in CNC machine linear guideways. Lubrication would avoid excessive friction, which fails and damages equipment and affects accuracy. However, controlling the lubrication oil consumption is very important for preventing environmental pollution. In this research work, an environmentally friendly intelligent lubrication technique based on friction and lubrication conditions in CNC machine linear guideways will be introduced. Initially, in dry machining condition, the friction forces will be calculated via cutting force analysis in guideways, while the servomotor current signals will be measured. Later, the servomotor current signals will be used to identify the status of the lubrication conditions in guideways, comparing the signals in both dry and wet machining conditions. Next, the adaptive neuro fuzzy inference system (ANFIS) will be used to build a reliable predictive model for both of friction force and servomotor current values, as a function of cutting parameters. Then, these two ANFIS models will be used to build an oil feedback lubrication control unit (LCU). The LCU sends a signal to the actuators to trigger the oil pump to inject oil based on both predicted friction force value in dry condition at certain cutting parameters and the actual lubrication status on guideways which is identified by the servomotor signals. Finally, the controllers performance will be verified through a new set of experiments to show the significant reduction of oil consumption compared with ordinary lubrication modes. The new technique will not only be able to control the lubrication oil consumption, but also it will be useful in terms of cost effectiveness and preventing environmental pollution.
|Effective start/end date
|1/04/20 → 1/04/23
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