Machine learning mechanical properties of steel sheets from an industrial production route

[1]  Adil Han Orta,et al.  Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks , 2019, Ironmaking & Steelmaking.

[2]  Emrehan Kutlug Sahin,et al.  Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest , 2020, SN Applied Sciences.

[3]  Leo S. Carlsson,et al.  Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace , 2020, steel research international.

[4]  Takuya Akiba,et al.  Optuna: A Next-generation Hyperparameter Optimization Framework , 2019, KDD.

[5]  Sibasis Sahoo,et al.  Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks , 2019 .

[6]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Hussain Alkharusi,et al.  Categorical Variables in Regression Analysis: A Comparison of Dummy and Effect Coding , 2012 .

[9]  S. Kheirandish,et al.  Affect of the tempering temperature on the microstructure and mechanical properties of dual phase steels , 2012 .

[10]  Shubhabrata Datta,et al.  Designing cold rolled IF steel sheets with optimized tensile properties using ANN and GA , 2011 .

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[13]  Amir Parsapour,et al.  Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks , 2010 .

[14]  Ernst Kozeschnik,et al.  Concurrent Precipitation of AlN and VN in Microalloyed Steel , 2010 .

[15]  E. Kozeschnik,et al.  Kinetics of AlN precipitation in microalloyed steel , 2010 .

[16]  Rich Caruana,et al.  An empirical evaluation of supervised learning in high dimensions , 2008, ICML '08.

[17]  M. Buscema,et al.  Introduction to artificial neural networks. , 2007, European journal of gastroenterology & hepatology.

[18]  R. Hudd Processing–Cold Working and Annealing , 2006 .

[19]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[20]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[21]  H. White,et al.  A Reality Check for Data Snooping , 2000 .

[22]  B. Buchmayr,et al.  Aluminum nitride precipitation and texture development in batch-annealed bake-hardening steel , 1999 .

[23]  Yuri F. Titovets,et al.  Analysis of aluminium nitride precipitation proceeding concurrently with recrystallization in low-carbon steel , 1998 .

[24]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[25]  D. Witmer,et al.  Effect of Nitrogen on the Mechanical Properties of Drawing-Quality Aluminum-Killed Sheet Steel , 1970 .

[26]  Jiajia Cai,et al.  Online prediction of mechanical properties of hot rolled steel plate using machine learning , 2021 .

[27]  Shivani Gupta,et al.  Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review , 2019, Procedia Computer Science.

[28]  Arndt Birkert,et al.  Plastizitätstheoretische und werkstofftechnische Grundlagen , 2013 .

[29]  F. G. Wilson,et al.  Aluminium nitride in steel , 1988 .

[30]  W. Hutchinson Development and control of annealing textures in low-carbon steels , 1984 .