40 KB378 KB31 KB199920252011Majstorović, Vidosav D.Soković, MirkoŠibalija, Tatajanaštevilka:4letnik:57str. 357-365ISSN:0039-2480COBISSID:11837467URN:URN:NBN:SI:doc-SQR2Q2AZenAssociation of Mechanical Engineers and Technicians of Slovenia et al.Zveza strojnih inženirjev in tehnikov Slovenije et al.Strojniški vestnikgenetic algorithmgenetski algoritemhistorical dataneural networksnevronske mrežeoptimisationoptimizacijapredhodni podatkiTaguchi methodTaguchi metodaTaguchi-based and intelligent optimisation of a multi-response process using historical data|Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchis quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisationČlanek predstavlja nov, generični pristop k optimiranju parametrov procesa z več odzivi, ki temelji na predhodnih podatkih. Pristop sestoji iz dveh delov. Prvi del temelji na Taguchi funkciji izgube kakovosti (QL) in multivariantnih statističnih metodah PCA in GRA za nekorelirane in sestavljene odgovore znotraj posameznih meritev zmogljivosti procesa. Na osnovi tega je razvit drugi del z uporabo tehnik umetne inteligence (AI): umetnih nevronskih mrež (ANNs) za izvajanje modeliranja procesa in genetskega algoritma (GA), ki poišče optimalno izbiro parametrov v zveznem prostoruTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo