Modelling of thermal shrinkage of seamless steel pipes using artificial neural networks (ANN) focussing on the influence of the ANN architecture-Reference-Cited by-同舟云学术

Modelling of thermal shrinkage of seamless steel pipes using artificial neural networks (ANN) focussing on the influence of the ANN architecture

Published:2023-03 Issue: Volume:17 Page:100999
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Langbauer Raphael^ORCID,Nunner Georg,Zmek Thomas,Klarner Jürgen,Prieler René,Hochenauer Christoph

Publisher

Elsevier BV

Subject

General Engineering

Reference52 articles.

1. Effect of cold and hot rolling and normalising on the structure and properties of welded joints;Gopinathan;J. Mech. Work. Technol.,1978

2. Mechanical properties and microstructure characterization of GTAW of micro-alloyed hot rolled ferritic XPF800 steel, Engineering Science and Technology;Korkmaz;Int. J.,2021

3. Analysis and optimization of water distribution systems: a case study of Kurudu post service housing estate, Abuja, Nigeria;Awe;Results in Engineering,2020

4. Analysis of the thermal inertia of pipelines in SHIP;Barnetche;Results in Engineering,2023

5. Numerical investigation of Bi-model slurry transportation in a straight pipe;Joshi;Results in Engineering,2023

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analysis of super Cr13 stainless-steel internal fracture growth effects during skew mill piercing process;Results in Engineering;2024-03

2. Modeling of CO2 solubility in piperazine (PZ) and diethanolamine (DEA) solution via machine learning approach and response surface methodology;Case Studies in Chemical and Environmental Engineering;2023-12

3. Finite element analysis of cold-formed steel stud wall subjected to blast load and validated using artificial neural network combined with response surface method;Asian Journal of Civil Engineering;2023-11-27

4. Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles;Results in Engineering;2023-09