Prediction of the main degradation mechanisms in a hot forging steel die: Optical scanning, simulation, microstructural evolution, and neural network modeling
This paper presents a framework for assessing degradation mechanisms and life service of an H21 (ISO-EN X30WCrV9-3; 3Cr2W8V Chinese standard) carbon steel die for hot forging. Four main failure mechanisms are considered: abrasive wear, thermal cracking, plastic deformation, and mechanical cracking....
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Published in | Journal of materials research and technology Vol. 37; pp. 432 - 443 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.07.2025
Elsevier |
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Abstract | This paper presents a framework for assessing degradation mechanisms and life service of an H21 (ISO-EN X30WCrV9-3; 3Cr2W8V Chinese standard) carbon steel die for hot forging. Four main failure mechanisms are considered: abrasive wear, thermal cracking, plastic deformation, and mechanical cracking. Optical scanning, finite element method (FEM), nano-indentation, scanning electron microscopy (SEM), optical micrography, and electron backscatter diffraction (EBSD) are used to identify and analyze failure mechanisms in the exposed areas. Accordingly, computational models employing artificial neural networks (ANN) simulate each failure mechanism. The experimental data gathered from optical scanning and microstructure analysis show that the three regions of the die surface are subject to major failure mechanisms. Notably, ANN models developed for each degeneration/failure mechanism are accurate and reliable, and their outputs agree with experimental data. Since unexpected tool failures can increase final manufacturing costs from 15 to 30 %, using the current ANN prediction models developed here may help reduce costs by up to 15 %. This financial benefit can be achieved by preventing sudden stops of product lines and dedicating the chance to wear tools for treatment before breaking. |
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AbstractList | This paper presents a framework for assessing degradation mechanisms and life service of an H21 (ISO-EN X30WCrV9-3; 3Cr2W8V Chinese standard) carbon steel die for hot forging. Four main failure mechanisms are considered: abrasive wear, thermal cracking, plastic deformation, and mechanical cracking. Optical scanning, finite element method (FEM), nano-indentation, scanning electron microscopy (SEM), optical micrography, and electron backscatter diffraction (EBSD) are used to identify and analyze failure mechanisms in the exposed areas. Accordingly, computational models employing artificial neural networks (ANN) simulate each failure mechanism. The experimental data gathered from optical scanning and microstructure analysis show that the three regions of the die surface are subject to major failure mechanisms. Notably, ANN models developed for each degeneration/failure mechanism are accurate and reliable, and their outputs agree with experimental data. Since unexpected tool failures can increase final manufacturing costs from 15 to 30 %, using the current ANN prediction models developed here may help reduce costs by up to 15 %. This financial benefit can be achieved by preventing sudden stops of product lines and dedicating the chance to wear tools for treatment before breaking. |
Author | Emamverdian, Aliakbar Lamberti, Luciano Pruncu, Catalin Liu, Hongsheng Rahimzadeh, Atabak |
Author_xml | – sequence: 1 givenname: Aliakbar surname: Emamverdian fullname: Emamverdian, Aliakbar email: ali.em@hqu.edu.cn organization: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, Fujian Province, China – sequence: 2 givenname: Catalin surname: Pruncu fullname: Pruncu, Catalin organization: Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Viale Orabona 4, 70125, Bari, Italy – sequence: 3 givenname: Hongsheng surname: Liu fullname: Liu, Hongsheng organization: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, Fujian Province, China – sequence: 4 givenname: Atabak surname: Rahimzadeh fullname: Rahimzadeh, Atabak organization: Department of Mechanical Engineering, Girne American University, Kyrenia, 99428, Cyprus – sequence: 5 givenname: Luciano surname: Lamberti fullname: Lamberti, Luciano organization: Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Viale Orabona 4, 70125, Bari, Italy |
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Cites_doi | 10.1016/j.matpr.2017.01.131 10.1016/j.engfailanal.2024.108661 10.17531/ein.2018.2.01 10.2478/msp-2021-0020 10.26628/wtr.v92i3.1103 10.1016/j.jmrt.2021.08.022 10.1016/j.jmrt.2020.11.058 10.1007/s00170-020-05641-y 10.1088/2053-1591/abc4f7 10.3390/ma17123005 10.1007/s11665-021-05536-3 10.2478/msp-2024-0011 10.1016/j.acme.2018.02.010 10.1016/j.matpr.2019.05.426 10.3390/met14050554 10.3390/met13040815 10.1016/j.engfailanal.2021.105678 10.1007/s42243-019-00230-0 |
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Keywords | Hot forging process Optical scanning Microstructure analysis Artificial neural network Tool service life Degeneration/failure mechanisms |
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SubjectTerms | Artificial neural network Degeneration/failure mechanisms Hot forging process Microstructure analysis Optical scanning Tool service life |
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Title | Prediction of the main degradation mechanisms in a hot forging steel die: Optical scanning, simulation, microstructural evolution, and neural network modeling |
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