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Design and implementation of a process optimizer: a case study on monitoring molding operations
Author(s) -
Lau H.C.W.,
Lee C.K.M.,
Ip W.H.,
Chan F.T.S.,
Leung R.W.K.
Publication year - 2005
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2005.00289.x
Subject(s) - computer science , process (computing) , molding (decorative) , complement (music) , domain (mathematical analysis) , set (abstract data type) , fuzzy logic , genetic algorithm , quality (philosophy) , industrial engineering , artificial intelligence , machine learning , mechanical engineering , mathematics , programming language , engineering , gene , phenotype , operating system , mathematical analysis , biochemistry , chemistry , philosophy , epistemology , complementation
Abstract: To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper.