dc.contributor.author | Al-Refaie, Abbas | |
dc.contributor.author | Lepkova, Natalija | |
dc.contributor.author | Abbasi, Ghaleb | |
dc.contributor.author | Bani Domi, Ghaith | |
dc.date.accessioned | 2023-09-18T20:34:22Z | |
dc.date.available | 2023-09-18T20:34:22Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1854-6250 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/150961 | |
dc.description.abstract | This research developed mathematical models to optimize process performance for multiple pentagon fuzzy quality responses. Initially, each quality response was represented by a pentagon membership function. Then, the combination of optimal factor levels was obtained for each response replicate. Those optimal combinations were then used to construct pentagon regression models for each response. A pentagon fuzzy optimization model was formulated and solved to determine the combination of optimal factor levels at each element of pentagon response’s fuzzy number. Two real case studies, i.e. wire-electrical discharge machining and sputtering process, were provided for illustration. Optimal results of the two case studies revealed that the proposed procedure effectively optimized performance under uncertainty and provided larger improvement in multiple quality characteristics. In conclusion, the proposed procedure may enhance the process engineer’s knowledge about effects of uncertainty on process/product performance and help practitioners decide the proper adjustments of factor levels in order to enhance performance of electrical discharge machining and sputtering process. | eng |
dc.format | PDF | |
dc.format.extent | p. 307-317 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | CSA (ProQuest) | |
dc.relation.isreferencedby | TOC Premier | |
dc.relation.isreferencedby | Academic Search Complete | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | http://www.apem-journal.org/Archives/2020/APEM15-3_307-317.pdf | |
dc.source.uri | http://www.apem-journal.org/Archives/2020/Abstract-APEM15-3_307-317.html | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:73989211/datastreams/MAIN/content | |
dc.title | Optimization of process performance by multiple pentagon fuzzy responses: Case studies of wire-electrical discharge machining and sputtering process | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.references | 18 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | The University of Jordan | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Statybos fakultetas / Faculty of Civil Engineering | |
dc.subject.researchfield | T 002 - Statybos inžinerija / Construction and engineering | |
dc.subject.studydirection | E05 - Statybos inžinerija / Civil engineering | |
dc.subject.vgtuprioritizedfields | SD0404 - Statinių skaitmeninis modeliavimas ir tvarus gyvavimo ciklas / BIM and Sustainable lifecycle of the structures | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | modeling and optimization | |
dc.subject.en | fuzzy goal programming | |
dc.subject.en | Pentagon regression modelling | |
dc.subject.en | Pentagon fuzzy numbers | |
dc.subject.en | wire electro-discharge machining (WEDM) | |
dc.subject.en | Surface roughness (SR) | |
dc.subject.en | Material removal rate (MRR) | |
dc.subject.en | sputtering process | |
dc.subject.en | Gallium-doped ZnO (GZO) | |
dcterms.sourcetitle | Advances in production engineering & management | |
dc.description.issue | iss. 3 | |
dc.description.volume | vol. 15 | |
dc.publisher.name | University of Maribor | |
dc.publisher.city | Maribor | |
dc.identifier.doi | 000589890200005 | |
dc.identifier.doi | 10.14743/apem2020.3.367 | |
dc.identifier.elaba | 73989211 | |