Extension of experimentally assembled processing maps of 10CrMo9-10 steel via a predicted dataset and the influence on overall informative possibilities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F19%3A10243077" target="_blank" >RIV/61989100:27360/19:10243077 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2075-4701/9/11/1218/htm" target="_blank" >https://www.mdpi.com/2075-4701/9/11/1218/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/met9111218" target="_blank" >10.3390/met9111218</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extension of experimentally assembled processing maps of 10CrMo9-10 steel via a predicted dataset and the influence on overall informative possibilities

Popis výsledku v původním jazyce

Processing maps embody a supportive tool for the optimization of hot forming processes. In the present work, based on the dynamic material model, the processing maps of 10CrMo9-10 low-alloy steel were assembled with the use of two flow curve datasets. The first one was obtained on the basis of uniaxial hot compression tests in a temperature range of 1073-1523 K and a strain rate range of 0.1-100 1/s. This experimental dataset was subsequently approximated by means of an artificial neural network approach. Based on this approximation, the second dataset was calculated. An important finding was that the additional dataset contributed significantly to improving the informative ability of the assembled processing maps in terms of revealing potentially inappropriate forming conditions. (C) 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

Extension of experimentally assembled processing maps of 10CrMo9-10 steel via a predicted dataset and the influence on overall informative possibilities

Popis výsledku anglicky

Processing maps embody a supportive tool for the optimization of hot forming processes. In the present work, based on the dynamic material model, the processing maps of 10CrMo9-10 low-alloy steel were assembled with the use of two flow curve datasets. The first one was obtained on the basis of uniaxial hot compression tests in a temperature range of 1073-1523 K and a strain rate range of 0.1-100 1/s. This experimental dataset was subsequently approximated by means of an artificial neural network approach. Based on this approximation, the second dataset was calculated. An important finding was that the additional dataset contributed significantly to improving the informative ability of the assembled processing maps in terms of revealing potentially inappropriate forming conditions. (C) 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20500 - Materials engineering

Projekt

<a href="/cs/project/ED2.1.00%2F19.0387" target="_blank" >ED2.1.00/19.0387: Rozvoj výzkumně vývojové základny RMTVC</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Metals

ISSN

2075-4701

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000504411600086

EID výsledku v databázi Scopus

2-s2.0-85075065051