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Content available remote Intelligent identification of cutting states by utilising Power Spectrum Density
EN
Purpose: This paper presents an in-process monitoring and identification of cutting states in turning process in order to realize the intelligent machine tools. Design/methodology/approach: The developed method utilizes the power spectrum density, or PSD of dynamic cutting force measured during the cutting. The experimentally obtained results suggested that there are basically three types of patterns of PSD when the cutting states are the continuous chip formation, the broken chip formation, and the chatter. The broken chip formation is desired to realize safe and reliable machining. The method proposed introduces three ratios, which are calculated and obtained by taking the ratios of cumulative PSD for a certain frequency range of three dynamic cutting force components corresponding to those three states of cutting, to classify the cutting states. The algorithm was developed to calculate the values of three ratios during the process in order to obtain the proper threshold values for classification of the cutting states. Findings: The method developed has been proved by series of cutting experiments that the states of cutting are well identified regardless of the cutting conditions. The broken chips are easily obtained by changing the cutting conditions during the processes referring to the algorithm developed. Practical implications: There are still not many systems being used in practice mainly due to the lack of general applicability such as a requirement of automated machining systems regardless of the cutting conditions. The aim of this research is to develop an in-process monitoring system for identification of continuous chip, broken chip, and chatter regardless of the cutting conditions by spectrum analysis based on the power spectrum density, or PSD of dynamic cutting force measured during the cutting. Originality/value: The largest potential advantage of the method proposed in this paper is that the states of cutting can be readily identified during the in-process cutting under any cutting conditions by simply mapping the experimentally obtained values of parameters referring to the proper threshold values in the reference feature spaces.
2
Content available remote In-process monitoring and control of microassembly by utilising force sensor
EN
Purpose: The aim of this research is to develop an in-process monitoring system to control the position of the shaft within a tolerance of ± 2.5 žm regardless of any conditions of the geometries of the shaft and the thrust plate. Design/methodology/approach: To realize an automated and intelligent microassembly process, a method has been developed to monitor and control the position of the shaft in the plate of the high-precision spindle motor for hard disk drive in order to reduce the shaft high problem. The force sensor is utilized and attached on the table of the microassembly machine under the jig of the plate to monitor the in-process pressing force. The experimentally obtained pressing force depends on the variations of the tolerance fitness and the geometrical roundness of the shaft and the plate but it is increased immediately when the shaft touches the stopper on the jig as the setting point. Hence, a method proposed introduces the reference voltage as the threshold value, which is calculated and obtained by taking the differentiation of the in-process pressing force. A slope detector is developed to calculate the output voltage based on the in-process pressing force, and the motor driver of machine is controlled when the obtained output voltage is larger than the reference voltage. Findings: It is proved that the shaft high problem is well controlled and reduced by the method developed regardless of any combinations of the geometries of the shaft and the plate. Practical implications: In this research, the microassembly process of the shaft into the plate of high-precision spindle motor for hard disk drive is showed. Originality/value: This paper has shown the developed the in-process monitoring system and control the manufacturing process automatically.
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