TY - GEN
T1 - Optimum design of machine tool structures based on BP neural network and genetic algorithm
AU - Yuan, Songmei
AU - Zhan, Zhongfei
AU - Yao, Li
PY - 2013
Y1 - 2013
N2 - In order to estimate and optimize the static and dynamic characteristics of machine tool, the full parameterized FEM model of it is established and studied in the paper. After the FEM analysis of bed, this paper takes a machine bed as example, presents a method of combination of BP Neural-Network(NN) and Genetic-Algorithm(GA) to optimize dynamic characteristics and realizes the structural optimization of the bed. It proved that this method takes less time, and more precision compared to traditional method.
AB - In order to estimate and optimize the static and dynamic characteristics of machine tool, the full parameterized FEM model of it is established and studied in the paper. After the FEM analysis of bed, this paper takes a machine bed as example, presents a method of combination of BP Neural-Network(NN) and Genetic-Algorithm(GA) to optimize dynamic characteristics and realizes the structural optimization of the bed. It proved that this method takes less time, and more precision compared to traditional method.
KW - BP-NN
KW - Dynamic characteristics
KW - GA
KW - Structural optimization
UR - https://www.scopus.com/pages/publications/84874200171
U2 - 10.4028/www.scientific.net/AMR.655-657.1291
DO - 10.4028/www.scientific.net/AMR.655-657.1291
M3 - 会议稿件
AN - SCOPUS:84874200171
SN - 9783037856482
T3 - Advanced Materials Research
SP - 1291
EP - 1295
BT - Engineering Solutions for Manufacturing Processes
T2 - 2012 3rd International Conference on Advances in Materials and Manufacturing Processes, ICAMMP 2012
Y2 - 22 December 2012 through 23 December 2012
ER -