Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Virtanen, E.
Van Tyne, C.J.
Levy, B.S.
and
Brada, G.
2013.
The tempering parameter for evaluating softening of hot and warm forging die steels.
Journal of Materials Processing Technology,
Vol. 213,
Issue. 8,
p.
1364.
Mahadevan, S.
Manojkumar, R.
Jayakumar, T.
Das, C. R.
and
Rao, B. P. C.
2016.
Precipitation-Induced Changes in Microstrain and Its Relation with Hardness and Tempering Parameter in 17-4 PH Stainless Steel.
Metallurgical and Materials Transactions A,
Vol. 47,
Issue. 6,
p.
3109.
Kaiser, Daniel
de Graaff, Bernhard
Dietrich, Stefan
Schulze, Volker
Delaunois, Fabienne
Vitry, Véronique
and
Roudet, Francine
2018.
Investigation of the precipitation kinetics and microstructure evolution of martensitic AISI 4140 steel during tempering with high heating rates.
Metallurgical Research & Technology,
Vol. 115,
Issue. 4,
p.
404.
Kaiser, D.
Damon, J.
Mühl, F.
de Graaff, B.
Kiefer, D.
Dietrich, S.
and
Schulze, V.
2020.
Experimental investigation and finite-element modeling of the short-time induction quench-and-temper process of AISI 4140.
Journal of Materials Processing Technology,
Vol. 279,
Issue. ,
p.
116485.
Euser, Virginia Katherine
Williamson, Don Lee
Clarke, Amy Jean
and
Speer, John Gordon
2020.
Limiting Retained Austenite Decomposition in Quenched and Tempered Steels: Influences of Rapid Tempering and Silicon.
ISIJ International,
Vol. 60,
Issue. 12,
p.
2990.
Du, Ningyu
Liu, Hongwei
Fu, Paixian
Liu, Hanghang
Sun, Chen
Cao, Yanfei
and
Li, Dianzhong
2020.
Microstructural Stability and Softening Resistance of a Novel Hot-Work Die Steel.
Crystals,
Vol. 10,
Issue. 4,
p.
238.
Correa, Ramon Santos
Sampaio, Patricia Teixeira
Braga, Rafael Utsch
Lambertucci, Victor Alberto
Almeida, Gustavo Matheus
and
Braga, Antonio Padua
2020.
Prediction of Mechanical Properties of Seamless Steel Tubes Using Artificial Neural Networks.
International Journal of Computational Intelligence and Applications,
Vol. 19,
Issue. 04,
Carneiro, Marcelo V.
Salis, Turíbio T.
Almeida, Gustavo M.
and
Braga, Antonio P.
2021.
Prediction of Mechanical Properties of Steel Tubes Using a Machine Learning Approach.
Journal of Materials Engineering and Performance,
Vol. 30,
Issue. 1,
p.
434.
Kohls, Ewald
Heinzel, Carsten
and
Eich, Marco
2021.
Evaluation of Hardness and Residual Stress Changes of AISI 4140 Steel Due to Thermal Load during Surface Grinding.
Journal of Manufacturing and Materials Processing,
Vol. 5,
Issue. 3,
p.
73.
Duraaes, Ramon Gomes
Salis, Turibio Tanus
Coelho, Frederico Gualberto Ferreira
and
Braga, Antoonio de Padua
2022.
Explainability Analysis of a Machine Learning Model for Industrial Applications.
p.
1.
Jun, Yi
Tao, Yi
Zongfu, Guo
Zhifeng, Gong
and
Bing, Chen
2022.
Analytical modeling and experimental verification of the depth of subsurface heat-affected layer in gear profile grinding.
The International Journal of Advanced Manufacturing Technology,
Vol. 121,
Issue. 5-6,
p.
4141.
Schüßler, Philipp
Damon, James
Mühl, Fabian
Dietrich, Stefan
and
Schulze, Volker
2023.
Laser surface hardening: A simulative study of tempering mechanisms on hardness and residual stress.
Computational Materials Science,
Vol. 221,
Issue. ,
p.
112079.
Gao, Junkang
Li, Jun
Li, Junwan
Li, Shaohong
and
Li, Zhimin
2024.
Research progress on thermal aging of reactor pressure vessel steel.
Materials Science and Technology,