Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F22%3A10250032" target="_blank" >RIV/61989100:27360/22:10250032 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000861662000001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000861662000001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matdes.2022.110880" target="_blank" >10.1016/j.matdes.2022.110880</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Popis výsledku v původním jazyce

In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an Auto-Encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response - especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

Název v anglickém jazyce

Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Popis výsledku anglicky

In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an Auto-Encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response - especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20500 - Materials engineering

Projekt

<a href="/cs/project/EF17_049%2F0008399" target="_blank" >EF17_049/0008399: Rozvoj mezisektorové spolupráce RMTVC s aplikační sférou v oblasti výzkumu progresivních a inovací klasických kovových materiálů a technologií s využitím metod modelování</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

2022

Kód důvěrnosti údajů

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

Název periodika

Materials and Design

ISSN

0264-1275

e-ISSN

1873-4197

Svazek periodika

220

Číslo periodika v rámci svazku

110880

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

nestrankovano

Kód UT WoS článku

000861662000001

EID výsledku v databázi Scopus

2-s2.0-85132875961