TY - JOUR
T1 - Resources optimization deployment approach for complicated parts in networked manufacturing
AU - Yao, Chang Feng
AU - Zhang, Ding Hua
AU - Peng, Wen Li
AU - Li, Shan
PY - 2006/7
Y1 - 2006/7
N2 - To solve resources optimization deployment problem for complicated parts in networked manufacturing, an approach taking the process flow of part as kernel was proposed. In this approach, the design of Logical Manufacturing Process (LMP) and Logical Manufacturing Unit (LMU) was studied according to the process flow of part, and manufacturing tasks were decomposed and described based on LMP and LMU. To obtain the best Executive Manufacturing Process (EMP) for LMP, the multi -objective manufacturing resources optimization deployment problem was modeled considering the operation Cost, Time and machining Quality (CTQ). During solving the model by genetic algorithm, the eigenvalue matrix of CTQ was transformed into the relative membership degree matrix based on the influence degree coefficient in every generation, and the fitness value was calculated by taking the minimal sum of the square of hamming weighted distance of an individual to the ideal excellent individual and the square of hamming weighted distance of an individual to the ideal inferior individual as optimization rule. Finally, the validity and practicability of the approach were verified by an example of engine blade in networked manufacturing.
AB - To solve resources optimization deployment problem for complicated parts in networked manufacturing, an approach taking the process flow of part as kernel was proposed. In this approach, the design of Logical Manufacturing Process (LMP) and Logical Manufacturing Unit (LMU) was studied according to the process flow of part, and manufacturing tasks were decomposed and described based on LMP and LMU. To obtain the best Executive Manufacturing Process (EMP) for LMP, the multi -objective manufacturing resources optimization deployment problem was modeled considering the operation Cost, Time and machining Quality (CTQ). During solving the model by genetic algorithm, the eigenvalue matrix of CTQ was transformed into the relative membership degree matrix based on the influence degree coefficient in every generation, and the fitness value was calculated by taking the minimal sum of the square of hamming weighted distance of an individual to the ideal excellent individual and the square of hamming weighted distance of an individual to the ideal inferior individual as optimization rule. Finally, the validity and practicability of the approach were verified by an example of engine blade in networked manufacturing.
KW - Executive manufacturing process
KW - Genetic algorithm
KW - Logical manufacturing process
KW - Logical manufacturing unit
KW - Physical manufacturing unit
KW - Relative membership degree
KW - Resources optimization deployment
UR - http://www.scopus.com/inward/record.url?scp=33748676692&partnerID=8YFLogxK
M3 - 文章
AN - SCOPUS:33748676692
SN - 1006-5911
VL - 12
SP - 1060
EP - 1067
JO - Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
JF - Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
IS - 7
ER -