Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms

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dc.contributor.author Krimpenis, A en
dc.contributor.author Benardos, PG en
dc.contributor.author Vosniakos, GC en
dc.contributor.author Koukouvitaki, A en
dc.date.accessioned 2014-03-01T01:25:09Z
dc.date.available 2014-03-01T01:25:09Z
dc.date.issued 2006 en
dc.identifier.issn 0268-3768 en
dc.identifier.uri http://hdl.handle.net/123456789/17567
dc.subject genetic algorithms en
dc.subject neural networks en
dc.subject pressure die-casting en
dc.subject process conditions en
dc.subject simulation en
dc.subject.classification Automation & Control Systems en
dc.subject.classification Engineering, Manufacturing en
dc.subject.other OPTIMIZATION en
dc.subject.other PREDICTION en
dc.subject.other KNOWLEDGE en
dc.subject.other RUNNER en
dc.subject.other SYSTEM en
dc.subject.other DESIGN en
dc.title Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms en
heal.type journalArticle en
heal.identifier.primary 10.1007/s00170-004-2218-0 en
heal.identifier.secondary http://dx.doi.org/10.1007/s00170-004-2218-0 en
heal.language English en
heal.publicationDate 2006 en
heal.abstract Pressure die-casting condition selection mainly relies on the experience and expertise of individuals working in production industries. Systematic knowledge accumulation regarding the manufacturing process is essential in order to obtain optimal process conditions. It is not safe a priori to presume that rules of thumb, which are widely used on the shop floor, always lead to fast prototype production calibration and to increased productivity. Thus, neural network meta-models are suggested in this work in order to generalise from examples connecting input process variables, such as gate velocity, mould temperature, etc., to output variables, such as filling time, solidification time, defects, etc. These examples, or knowledge, are gathered from experiments conducted on casting simulation software, which are designed systematically using orthogonal arrays (DoE). They could also be based on experiments from industrial practice. Neural models derived in this way can help in avoiding excessive numbers of what-if scenarios examined on the casting simulation software, which can be very time-consuming. Furthermore, they can be employed in the fitness function of a genetic algorithm that can optimise the process, i.e. yield the combination of input parameters which achieves the best output parameter values. en
heal.publisher SPRINGER LONDON LTD en
dc.identifier.doi 10.1007/s00170-004-2218-0 en
dc.identifier.isi ISI:000233725700011 en
dc.identifier.volume 27 en
dc.identifier.issue 5-6 en
dc.identifier.spage 509 en
dc.identifier.epage 517 en

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