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URN etd-0816106-181718 Statistics This thesis had been viewed 3489 times. Download 1492 times. Author JU-CHUNG HSU Author's Email Address No Public. Department Mechanical Engineering Year 2005 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 97 Title AN ANALYTICAL EVALUATION OF THE CUTTING FORCES IN GUN DEEP DRILLING USING ARTIFICIAL NEURAL NETWORKS Keyword AIM Abductory Induction Mechanism networks Gun drills deep hole Taguchi Methods Taguchi Methods deep hole Gun drills Abductory Induction Mechanism networks AIM Abstract In order to increase the practicability and application of the deep -hole drilling, a cutting force model aimed at cutting geometries and working parameters is developed to find out the influences of the cutting impedance. First of all, Taguchi Method is utilized for the design of experimental array. The thrust force and torque of the main spindle are practically measured and thereby investigated via the factor response analyses as well as analysis of variance. The result shows that the rotational speed and feed speed of the main spnidle are the most important factor influencing the cutting impedances, when the bigger revolutionary feed rate causes the larger thrust force and torque. Also, the factor effect of the outer cutting angle is more significant than that of the inner cutting angle, when the larger degree of the outer cutting angle has the higher cutting impedances. Furthermore, the artificial neural network system is applied to develop the cutting force model. The results reveal that the inner incline angle, the outer incline angle and the position of the drill top have insignificant factor effect on the cutting impedances. Compared with the Taguchi linear model, the cutting model developed by applied neural network system has the better performance of prediction, whose errors are all under 10%. Advisor Committee Ching-Chih Tai - advisor
Files Date of Defense 2006-07-14 Date of Submission 2006-08-16