Browsing by Author "CHIANG, ST"

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ADAPTIVE-CONTROL OPTIMIZATION IN END MILLING USING NEURAL NETWORKS
(1995-04-01)
CHIANG, ST
;
LIU, DI
;
LEE, AC
;
CHIENG, WH
;
交大名義發表
In this paper, we propose an architecture with two different kinds of neural networks for on-line determination of optimal cutting conditions. A back-propagation network with three inputs and four outputs is used to model the cutting process. A second network, which parallelizes the augmented Lagrange multiplier algorithm, determines the corresponding optimal cutting parameters by maximizing the material removal rate according to appropriate operating constraints. Due to its parallelism, this architecture can greatly reduce processing time and make real-time control possible. Numerical simulations and a series of experiments are conducted on end milling to confirm the feasibility of this architecture.
ANALYSIS OF CHATTER VIBRATION IN A CUTTER-WORKPIECE SYSTEM
(1991)
LEE, AC
;
LIU, CS
;
CHIANG, ST
;
交大名義發表
;
機械工程學系
The phenomenon of chatter vibration in milling has been investigated by many authors, but the consideration of workpiece has never been paid any attention to. In this paper, this defect has been excluded by involving workpiece dynamic effect on chatter vibration. A general form of time domain simulation and a simplified form of frequency domain analysis referred as circle criterion will be proposed to examine such a system. The new phenomenon of chatter vibration is derived by both of these methods and experiments also demonstrate the validity of this new phenomenon.
ANALYSIS OF CUTTING FORCES IN BALL-END MILLING
(1995-01-01)
CHIANG, ST
;
TSAI, CM
;
LEE, AC
;
機械工程學系
;
Department of Mechanical Engineering
A cutting force model for ball end milling is developed, the model being based mainly on the assumption that the cutting force is equal to the product of the cutting area and the specific cutting force. The formulation of the cutting area is derived from the geometry of the mill and the workpiece, whilst the specific cutting force is obtained by third-order curve-fitting of data measured in experiments. The model established in this paper predicts the cutting forces in ball-end milling much more accurately than to previous models.