Pregled bibliografske jedinice broj: 38904
An Evolution and Biology Based Approach to the optimization of the Extrusion Process
An Evolution and Biology Based Approach to the optimization of the Extrusion Process // Proceedings of 7th International Aluminum Extrusion Technology Seminar / Peacock, Robert (ur.).
Chicago (IL): Aluminium Extruders Council, 2000. str. 109 - 118 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 38904 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
An Evolution and Biology Based Approach to the optimization of the Extrusion Process
Autori
Lozina, Željan ; Duplančić, Igor ; Prgin, Jere
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 7th International Aluminum Extrusion Technology Seminar
/ Peacock, Robert - Chicago (IL) : Aluminium Extruders Council, 2000, 109 - 118
Skup
ET 2000
Mjesto i datum
Chicago (IL), Sjedinjene Američke Države, 16.05.2000. - 19.05.2000
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
optimization;extrusion process; neural network; genetic algorithm
Sažetak
A new approach to the optimization of extrusion process has been performed with Neural Network (ANN) and Genetic Algorithm (GA) based procedure.
ANN trained on the technology process track record should be able to describe complex dependencies between influence parameters and product outgoing characteristics and is also supposed to be the fitness function. The optimization of the technological process based on genetic algorithm is proposed. The influence parameters are treated as heritage and combined with ANN forward pass being the fitness function.
The ANN is trained presenting extrusion experiment data-patterns. The objective (fitness) function value is calculated with ANN forward pass. The optimization results are compared with a more conservative approach using experiment planing and regression analysis. The mean error of the difference between regression and ANN approach is used as a comparison measure. The influence of different training parameters and choice of training patterns are investigated.
Optimization is performed on the extrusion of thick-walled AlZnMg (7000) hollow sections. Optimization variables are: billet temperature; extrusion rate; and Zr content. The output is tensile strength of the longitudinal welds.
The advantages of the method presented are its robustness and flexibility.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
023033
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split