TY - GEN
T1 - Machining Parameters Selection for High Speed Processing
AU - Kuo, Wei Feng
AU - Lee, Ching Hung
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In this study, we introduce the 'CNC Assistant' for machining parameters selection for high speed requirement in accuracy and surface roughness constraints. It involves modeling for experimental data of accuracy, surface roughness and machining time on a five-axis CNC machine tool (HM3025L) published by CHMER, coupled with a controller (M6HN) which is launched by GENTEC, based on processing parameters (feed rate, acceleration after interpolation time constant, acceleration and S-curve time constant). In order to predict and optimize the processing parameters combination, we use the data-driven approach to establish the back-propagation neural network (BPNN), and apply the particle swarm optimization (PSO) algorithm to search the processing parameters based on the constraints of accuracy and surface roughness. That is, users can set the specified conditions of accuracy and surface roughness, then the CNC assistant has the ability to obtain the corresponding machining parameters, not only leading to the shortest machining time but also meeting the design conditions. As above, the CNC assistant provide the machinery industry become more intelligent and convenient to improve the efficiency of CNC machine tools.
AB - In this study, we introduce the 'CNC Assistant' for machining parameters selection for high speed requirement in accuracy and surface roughness constraints. It involves modeling for experimental data of accuracy, surface roughness and machining time on a five-axis CNC machine tool (HM3025L) published by CHMER, coupled with a controller (M6HN) which is launched by GENTEC, based on processing parameters (feed rate, acceleration after interpolation time constant, acceleration and S-curve time constant). In order to predict and optimize the processing parameters combination, we use the data-driven approach to establish the back-propagation neural network (BPNN), and apply the particle swarm optimization (PSO) algorithm to search the processing parameters based on the constraints of accuracy and surface roughness. That is, users can set the specified conditions of accuracy and surface roughness, then the CNC assistant has the ability to obtain the corresponding machining parameters, not only leading to the shortest machining time but also meeting the design conditions. As above, the CNC assistant provide the machinery industry become more intelligent and convenient to improve the efficiency of CNC machine tools.
KW - Accuracy
KW - CNC machine parameter
KW - CNC machine tools
KW - High speed processing
KW - Neural Network
KW - Particle Swarm Optimization (PSO)
KW - Surface roughness
UR - http://www.scopus.com/inward/record.url?scp=85073872644&partnerID=8YFLogxK
U2 - 10.1109/ICESI.2019.8862997
DO - 10.1109/ICESI.2019.8862997
M3 - Conference contribution
AN - SCOPUS:85073872644
T3 - 2019 International Conference on Engineering, Science, and Industrial Applications, ICESI 2019
BT - 2019 International Conference on Engineering, Science, and Industrial Applications, ICESI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Engineering, Science, and Industrial Applications, ICESI 2019
Y2 - 22 August 2019 through 24 August 2019
ER -