1998
DOI: 10.1108/02644409810236902
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A computer system for solving inverse and optimization problems

Abstract: Financial assistance for this work, provided by Ministry of Science and Technology, Republic of Slovenia under the contract number P23-1533/0112-005/15827/95, and INCO Copernicus under the contract number IC15-CT96-0709, is gratefully acknowledged.

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Cited by 9 publications
(5 citation statements)
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“…Finite element simulation tools were successfully utilized for calculation of influential parameters that are hard to measure directly. We anticipate enhancement of this approach by application of inverse identification techniques [37,38] and sensitivity analysis [39], but this exceeds the scope of the current work. CAE NN analysis gives us an insight into the spatial influence of the most important parameters of wear.…”
Section: Discussionmentioning
confidence: 98%
“…Finite element simulation tools were successfully utilized for calculation of influential parameters that are hard to measure directly. We anticipate enhancement of this approach by application of inverse identification techniques [37,38] and sensitivity analysis [39], but this exceeds the scope of the current work. CAE NN analysis gives us an insight into the spatial influence of the most important parameters of wear.…”
Section: Discussionmentioning
confidence: 98%
“…Fourment [18] suggested a shape optimisation method for a two-step forging process, while Vieilledent focused on the optimisation of the preform geometry using B-Splines [19]. The implementation of a shell for coupling FE analysis and shape die optimisation is described in the work of Rodic [20]. In a further contribution Ghouati and Gelin propose the optimal design of forming processes exploiting a seqential quadratic programming approach [21].…”
Section: State Of the Artmentioning
confidence: 99%
“…Stupkiewicz et al (2002) developed the sensitivity analyses for large-strain contact problems, Rodic and Gresovnik (1998) built a computational shell around the finite element system to solve inverse and optimisation problems, and Doltsinis and Rodic (1999) proposed gradient based methods for the optimisation of the forming process design. Kini (2004) applied the combination of FE model, response surface methodology (RSM), and stochastic simulation based on the Monte Carlo method in order to determine the influence of process parameters on the product dimensional accuracy.…”
Section: State Of the Art In Designing Reliable And Robust Cold Forgimentioning
confidence: 99%