Research graph
References from Electrochemical Endpoint Determination and Machine-Learning Prediction of Pickling Time for Hot-Rolled Automotive High-Strength Steel. Local targets link to admitted publications; unresolved targets remain external evidence.
Advanced lightweight materials for automobiles: A review
10.1016/j.matdes.2022.110994 · 2022 · External reference
Special issue on automotive lightweight
10.1007/s42154-020-00117-x · 2020 · External reference
10.3390/ma14174970
10.3390/ma14174970 · External reference
High strength steel sheets for weight reduction of automotives
2019 · External reference
Oxide scale structure of hot rolled structural steel and its effect on pickling quality
2022 · External reference
A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems
10.1016/j.ins.2021.12.063 · 2022 · External reference
Online prediction of mechanical properties of hot rolled steel plate using machine learning
10.1016/j.matdes.2020.109201 · 2021 · External reference
Effect of Cu additions on scale structure and descaling efficiency of low C steel reheated in a combustion gas atmosphere
10.1007/s11085-022-10125-3 · 2022 · External reference
Review of oxide scale in hot-rolling process
10.2355/tetsutohagane.tetsu-2022-085 · 2023 · External reference
Research and development trend of shape control for cold rolling strip
10.1007/s10033-017-0163-8 · 2017 · External reference
The Fe–O (iron–oxygen) system
10.1007/bf02645713 · 1991 · External reference
Advancement in understanding of descalability during high pressure descaling
10.4028/www.scientific.net/kem.622-623.29 · 2014 · External reference
Study on formation of “easy to remove oxide scale” during mechanical descaling of high carbon wire rods
10.1016/j.surfcoat.2009.03.006 · 2009 · External reference
10.3390/ma16041745
10.3390/ma16041745 · External reference
10.3390/ma15041534
10.3390/ma15041534 · External reference
Oxide-scale structures formed on commercial hot-rolled steel strip and their formation mechanisms
10.1023/a:1010395419981 · 2001 · External reference
Empirical modeling of iron oxide dissolution in sulphuric and hydrochloric acid
10.1007/s11663-013-9893-x · 2013 · External reference
Prediction of under pickling defects on steel strip surface
10.7321/jscse.v1.n1.2 · 2011 · External reference
Characterization of iron oxides commonly formed as corrosion products on steel
10.1023/a:1011076308501 · 1998 · External reference
Dual-frequency ultrasonic cleaning with diluted phosphoric acid solution for removing oxide scale of uncoated steel sheets in hot stamping
10.1007/s00170-021-08015-0 · 2022 · External reference
Biogenic iron oxides for phosphate removal
10.1080/09593330.2018.1496147 · 2020 · External reference
Insight into reduction mechanism of the oxide scale formed on hot-rolled advanced high-strength steel in 5% H2–N2 atmosphere
10.1016/j.corsci.2026.114022 · 2026 · External reference
10.3390/coatings10090805
10.3390/coatings10090805 · External reference
On the arithmetic precision for implementing back-propagation networks on FPGA: A case study
10.1007/0-387-28487-7_2 · 2006 · External reference
A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast
10.1515/nleng-2022-0025 · 2022 · External reference
Small obstacle size prediction based on a GA-BP neural network
10.1364/ao.443535 · 2022 · External reference
A review on extreme learning machine
10.1007/s11042-021-11007-7 · 2022 · External reference
Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance
10.1007/s00366-020-01225-2 · 2022 · External reference
Intelligent prediction of coal mine water inrush based on optimized SAPSO-ELM model under the influence of multiple factors
10.1007/s12517-022-09756-2 · 2022 · External reference
Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
10.5194/gmd-15-5481-2022 · 2022 · External reference
Mean absolute percentage error for regression models
10.1016/j.neucom.2015.12.114 · 2016 · External reference
Improved shrinkage estimation of squared multiple correlation coefficient and squared cross-validity coefficient
10.1177/1094428106292901 · 2008 · External reference
On the arithmetic precision for implementing back-propagation networks on FPGA: A case study
10.1007/0-387-28487-7_2 · ExternalCitation · doi-reference
The Fe–O (iron–oxygen) system
10.1007/bf02645713 · ExternalCitation · doi-reference
Dual-frequency ultrasonic cleaning with diluted phosphoric acid solution for removing oxide scale of uncoated steel sheets in hot stamping
10.1007/s00170-021-08015-0 · ExternalCitation · doi-reference
Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance
10.1007/s00366-020-01225-2 · ExternalCitation · doi-reference
Research and development trend of shape control for cold rolling strip
10.1007/s10033-017-0163-8 · ExternalCitation · doi-reference
A review on extreme learning machine
10.1007/s11042-021-11007-7 · ExternalCitation · doi-reference
Effect of Cu additions on scale structure and descaling efficiency of low C steel reheated in a combustion gas atmosphere
10.1007/s11085-022-10125-3 · ExternalCitation · doi-reference
Empirical modeling of iron oxide dissolution in sulphuric and hydrochloric acid
10.1007/s11663-013-9893-x · ExternalCitation · doi-reference
Intelligent prediction of coal mine water inrush based on optimized SAPSO-ELM model under the influence of multiple factors
10.1007/s12517-022-09756-2 · ExternalCitation · doi-reference
Special issue on automotive lightweight
10.1007/s42154-020-00117-x · ExternalCitation · doi-reference
Insight into reduction mechanism of the oxide scale formed on hot-rolled advanced high-strength steel in 5% H2–N2 atmosphere
10.1016/j.corsci.2026.114022 · ExternalCitation · doi-reference
A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems
10.1016/j.ins.2021.12.063 · ExternalCitation · doi-reference
Online prediction of mechanical properties of hot rolled steel plate using machine learning
10.1016/j.matdes.2020.109201 · ExternalCitation · doi-reference
Advanced lightweight materials for automobiles: A review
10.1016/j.matdes.2022.110994 · ExternalCitation · doi-reference
Mean absolute percentage error for regression models
10.1016/j.neucom.2015.12.114 · ExternalCitation · doi-reference
Study on formation of “easy to remove oxide scale” during mechanical descaling of high carbon wire rods
10.1016/j.surfcoat.2009.03.006 · ExternalCitation · doi-reference
Oxide-scale structures formed on commercial hot-rolled steel strip and their formation mechanisms
10.1023/a:1010395419981 · ExternalCitation · doi-reference
Characterization of iron oxides commonly formed as corrosion products on steel
10.1023/a:1011076308501 · ExternalCitation · doi-reference
Biogenic iron oxides for phosphate removal
10.1080/09593330.2018.1496147 · ExternalCitation · doi-reference
Improved shrinkage estimation of squared multiple correlation coefficient and squared cross-validity coefficient
10.1177/1094428106292901 · ExternalCitation · doi-reference
Small obstacle size prediction based on a GA-BP neural network
10.1364/ao.443535 · ExternalCitation · doi-reference
A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast
10.1515/nleng-2022-0025 · ExternalCitation · doi-reference
Review of oxide scale in hot-rolling process
10.2355/tetsutohagane.tetsu-2022-085 · ExternalCitation · doi-reference
10.3390/coatings10090805
10.3390/coatings10090805 · ExternalCitation · doi-reference
10.3390/ma14174970
10.3390/ma14174970 · ExternalCitation · doi-reference
10.3390/ma15041534
10.3390/ma15041534 · ExternalCitation · doi-reference
10.3390/ma16041745
10.3390/ma16041745 · ExternalCitation · doi-reference
Advancement in understanding of descalability during high pressure descaling
10.4028/www.scientific.net/kem.622-623.29 · ExternalCitation · doi-reference
Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not
10.5194/gmd-15-5481-2022 · ExternalCitation · doi-reference
Prediction of under pickling defects on steel strip surface
10.7321/jscse.v1.n1.2 · ExternalCitation · doi-reference
