Papers by Author: Somkiat Tangjitsitcharoen

Abstract: This paper presents the additional work of the previous research in order to investigate the relations of the cutting conditions and the various air blow applications which affect the surface roughness. The suitable cutting condition is determined for the aluminum (Al6063) with the ball end milling by utilizing the response surface analysis referring to the minimum surface roughness. The cutting force is monitored during the cutting to analyze the surface roughness. The dynamometer is employed and installed on the table of 5-axis CNC maching center to measure the in-process cutting force. The models of surface roughness and cutting force are calculated by using the multiple regression analysis with the least squared method at 95% significant level. The experimentally obtained results showed that the surface roughness can be well explained by the in-process cutting force. The prediction accuracy and the prediction interval have been presented to verify the obtained surface roughness model at 95% confident level.

Abstract: The objective of this research is to develop the surface roughness and cutting force models by using the air blow cutting of the aluminum in the ball-end milling process. The air blow cutting proposed in order to reduce the use of the cutting fluid. The surface roughness and cuttting force models are proposed in the exponential forms which consist of the cutting speed, the feed rate, the depth of cut, the tool diameter, and the air blow pressure. The coefficients of the surface roughness and cutting force models are calculated by utilizing the multiple regression with the least squared method at 95% significant level. The effects of cutting parameters on the cutting force are investigated and measured to analyze the relation between the surface roughness and the cutting conditions. The experimentally obtained results showed that the cutting force has the same trend with the surface roughness. The surface plots are constructed to determine the optimum cutting condition referring to the minimum surface roughness.

Abstract: The aim of this research is to propose and develop the in-process monitoring system of the tool wear for the carbon steel (S45C) in CNC turning process by utilizing the multi-sensor which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. The progress of the tool wear results in the larger cutting force, the higher amplitude of the acceleration signal, and the higher power spectrum densities of sound and acoustic emission signals. Hence, their signals have been integrated via the neural network with the back propagation technique to monitor the tool wear. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level with the higher accuracy and reliability.

Abstract: As the intelligent machine and manufacturing system plays an important role in the near future, the monitoring system in turning process is required to improve the productivity during the cutting process. Hence, the aim of this research is to propose and develop the in-process monitoring system of the tool wear and the cutting states of chip and chatter for the carbon steel in CNC turning process by utilizing the sensor fusion which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. Their signals have been integrated via the neural network with the back propagation and the pattern recognition technique to monitor the tool wear and detect the cutting states which are the continuous chip, the broken chip and the chatter occurred. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level and identify the chip breaking and the chatter with the higher accuracy and reliability.