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 inJournal of materials research and technology Vol. 37; pp. 432 - 443
Main Authors Emamverdian, Aliakbar, Pruncu, Catalin, Liu, Hongsheng, Rahimzadeh, Atabak, Lamberti, Luciano
Format Journal Article
LanguageEnglish
Published 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.
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
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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
Language English
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References Chander, Chawla (bib3) 2017; 4
Mrzygłód, Hawryluk, Gronostajski, Opaliński, Kaszuba, Polak (bib14) 2018; 18
Jin, Zhao, Zhang, Luo, Wang (bib17) 2021; 30
Rajendran, Yurgel, Misiolek, Alves de Sousa (bib24) 2023; 13
Al Omar, Català, Alcelay, Peña (bib20) 2024; 14
Hawryluk, Dudkiewicz, Borowski, Marzec, Tkocz (bib13) 2024; 17
Sun, Wang (bib19) 2020; 7
Emamverdian, Sun, Cao, Pruncu, Wang (bib1) 2021; 129
Hawryluk, Rychlik, Ziemba, Jasiak, Lewandowski (bib6) 2021; 39
Kumar, Purushothaman, Chandramohan, Dushyantraj, Sathish (bib16) 2020; 21
Binder, Klocke, Doebbeler (bib2) 2017; 376–377
Kaszuba (bib7) 2020; 1
Hawryluk, Dudkiewicz, Jabłońska, Polak, Marzec (bib9) 2024; 164
Hawryluk, Marzec, Le, Justyna, Lu, Konrad (bib12) 1994; 18
Al, Mesnaoui, Scholar (bib18) 2024
Hawryluk, Dudkiewicz, Zwierzchowski, Polak, Lachowicz, Ziemba (bib10) 2024; 42
Hawryluk, Mrzyglód (bib25) 2017; 19
Hawryluk, Gronostajski, Ziemba, Dworzak, Jabłoński, Rychlik (bib11) 2018; 20
Kaszuba, Widomski, Kiełczawa, Gronostajski (bib8) 2020; 92
Hawryluk, Rychlik, Wieclaw, Jablonski (bib5) 2021; 5
Emamverdian, Sun, Chunping (bib21) 2021; 15
Lisiecka-Graca, Lisiecki, Zyguła, Wojtaszek (bib4) 2024; 42
yu, zhe, min, Zhang, Liu, Shang (bib23) 2019; 26
Heidarzadeh, Radi, Yapici (bib22) 2020; 9
Mrzygłód, Hawryluk, Janik, Olejarczyk-Wożeńska (bib15) 2020; 109
Lisiecka-Graca (10.1016/j.jmrt.2025.05.229_bib4) 2024; 42
Kumar (10.1016/j.jmrt.2025.05.229_bib16) 2020; 21
Hawryluk (10.1016/j.jmrt.2025.05.229_bib9) 2024; 164
Hawryluk (10.1016/j.jmrt.2025.05.229_bib6) 2021; 39
Emamverdian (10.1016/j.jmrt.2025.05.229_bib21) 2021; 15
Mrzygłód (10.1016/j.jmrt.2025.05.229_bib14) 2018; 18
Heidarzadeh (10.1016/j.jmrt.2025.05.229_bib22) 2020; 9
Hawryluk (10.1016/j.jmrt.2025.05.229_bib11) 2018; 20
Emamverdian (10.1016/j.jmrt.2025.05.229_bib1) 2021; 129
Kaszuba (10.1016/j.jmrt.2025.05.229_bib8) 2020; 92
Hawryluk (10.1016/j.jmrt.2025.05.229_bib12) 1994; 18
Kaszuba (10.1016/j.jmrt.2025.05.229_bib7) 2020; 1
Hawryluk (10.1016/j.jmrt.2025.05.229_bib13) 2024; 17
Al Omar (10.1016/j.jmrt.2025.05.229_bib20) 2024; 14
Jin (10.1016/j.jmrt.2025.05.229_bib17) 2021; 30
Chander (10.1016/j.jmrt.2025.05.229_bib3) 2017; 4
Hawryluk (10.1016/j.jmrt.2025.05.229_bib25) 2017; 19
yu (10.1016/j.jmrt.2025.05.229_bib23) 2019; 26
Rajendran (10.1016/j.jmrt.2025.05.229_bib24) 2023; 13
Binder (10.1016/j.jmrt.2025.05.229_bib2) 2017; 376–377
Hawryluk (10.1016/j.jmrt.2025.05.229_bib10) 2024; 42
Sun (10.1016/j.jmrt.2025.05.229_bib19) 2020; 7
Hawryluk (10.1016/j.jmrt.2025.05.229_bib5) 2021; 5
Al (10.1016/j.jmrt.2025.05.229_bib18)
Mrzygłód (10.1016/j.jmrt.2025.05.229_bib15) 2020; 109
References_xml – volume: 30
  start-page: 2708
  year: 2021
  end-page: 2719
  ident: bib17
  article-title: Research on neural network prediction of multidirectional forging microstructure evolution of GH4169 superalloy
  publication-title: J Mater Eng Perform
– volume: 42
  start-page: 171
  year: 2024
  end-page: 185
  ident: bib4
  article-title: Evaluation of cracking risk of 80MnSi8-6 nanobainitic steel during hot forging in the range of lower temperature limits
  publication-title: Mater Sci Pol
– volume: 129
  year: 2021
  ident: bib1
  article-title: Current failure mechanisms and treatment methods of hot forging tools (dies) - a review
  publication-title: Eng Fail Anal
– volume: 21
  start-page: 263
  year: 2020
  end-page: 267
  ident: bib16
  article-title: Materials Today : proceedings ANN-AGCS for the prediction of temperature distribution and required energy in hot forging process using finite element analysis
  publication-title: Mater Today Proc
– volume: 26
  start-page: 154
  year: 2019
  end-page: 161
  ident: bib23
  article-title: Softening and recrystallization behavior of a new class of ferritic steel
  publication-title: J Iron Steel Res Int
– year: 2024
  ident: bib18
  article-title: Development of neural networks to study the behavior of a micro-alloyed medium carbon steel during hot forming
– volume: 9
  start-page: 15874
  year: 2020
  end-page: 15879
  ident: bib22
  article-title: Formation of nano-sized compounds during friction stir welding of CueZn alloys: effect of tool composition
  publication-title: J Mater Res Technol
– volume: 39
  year: 2021
  ident: bib6
  article-title: Analysis of the production process of the forked forging used in the excavator drive system in order to improve the currently implemented technology by the use of numerical modeling
  publication-title: Mater Sci Pol
– volume: 20
  start-page: 169
  year: 2018
  end-page: 176
  ident: bib11
  article-title: Analysis of the influence of lubrication conditions on tool wear used in hot die forging processes
  publication-title: Eksploat i Niezawodn
– volume: 13
  start-page: 815
  year: 2023
  ident: bib24
  article-title: Hot forging die design optimization using FEM analysis for near-net forming of 18CrNiMo7-6 steel pinion shaft
  publication-title: Metals
– volume: 1
  start-page: 1
  year: 2020
  end-page: 13
  ident: bib7
  article-title: The application of a new , innovative , hybrid technology combining hardfacing and nitriding to increase the durability of forging tools
  publication-title: Arch Civ Mech Eng
– volume: 5
  start-page: 32
  year: 2021
  ident: bib5
  article-title: Analysis of the industrial process of producing a hub forging used in motorcar power transmission systems — a case study. 2021
  publication-title: J Manuf Mater Process
– volume: 42
  start-page: 113
  year: 2024
  end-page: 130
  ident: bib10
  article-title: Influence of the nitriding process on the durability of tools used in the production of automotive forgings in industrial hot die forging processes on hammers
  publication-title: Mater Sci
– volume: 18
  year: 1994
  ident: bib12
  article-title: Analysis of the wear of forming tools in the process of extruding ceramic bands using selected research methods for evaluating operational durability
  publication-title: Materials 2025
– volume: 19
  start-page: 338
  year: 2017
  end-page: 348
  ident: bib25
  article-title: A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks.Eksploatacja i Niezawodnosc – Maintenance and
  publication-title: Reliability
– volume: 376–377
  start-page: 165
  year: 2017
  end-page: 171
  ident: bib2
  article-title: Abrasive wear behavior under metal cutting conditions
  publication-title: Wear (Lausanne)
– volume: 109
  start-page: 1385
  year: 2020
  end-page: 1395
  ident: bib15
  article-title: Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel
  publication-title: Int J Adv Manuf Technol
– volume: 14
  start-page: 554
  year: 2024
  ident: bib20
  article-title: Development of neural networks to study flow behavior of medium carbon microalloyed steel during hot forming
  publication-title: Metals
– volume: 18
  start-page: 1079
  year: 2018
  end-page: 1091
  ident: bib14
  article-title: Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks
  publication-title: Arch Civ Mech Eng
– volume: 4
  start-page: 1147
  year: 2017
  end-page: 1157
  ident: bib3
  article-title: Failure of hot forging dies-an updated perspective
  publication-title: Mater Today Proc
– volume: 92
  start-page: 23
  year: 2020
  end-page: 32
  ident: bib8
  article-title: The use of a measuring arm with a laser scanner for analysis and support of regenerative surfacing processes of forging dies
  publication-title: Weld Technol Rev
– volume: 7
  start-page: 116509
  year: 2020
  ident: bib19
  article-title: A screening strategy for hot forging combining high-throughput forging experiment and machine learning
  publication-title: Mater. Res. Express
– volume: 15
  start-page: 268
  year: 2021
  end-page: 277
  ident: bib21
  article-title: Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: simulation, mechanical properties, and microstructural evolution
  publication-title: J Mater Res Technol
– volume: 164
  year: 2024
  ident: bib9
  article-title: Analysis of the destruction of a die insert used in the industrial process of hot die forging to produce a yoke forging
  publication-title: Eng Fail Anal
– volume: 17
  start-page: 3005
  year: 2024
  ident: bib13
  article-title: Increasing the working time of forging tools used in the industrial process of producing a disk-type forging assigned for a gearbox through the application of hybrid layers
  publication-title: Materials
– volume: 376–377
  start-page: 165
  year: 2017
  ident: 10.1016/j.jmrt.2025.05.229_bib2
  article-title: Abrasive wear behavior under metal cutting conditions
  publication-title: Wear (Lausanne)
– volume: 4
  start-page: 1147
  year: 2017
  ident: 10.1016/j.jmrt.2025.05.229_bib3
  article-title: Failure of hot forging dies-an updated perspective
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2017.01.131
– volume: 164
  year: 2024
  ident: 10.1016/j.jmrt.2025.05.229_bib9
  article-title: Analysis of the destruction of a die insert used in the industrial process of hot die forging to produce a yoke forging
  publication-title: Eng Fail Anal
  doi: 10.1016/j.engfailanal.2024.108661
– volume: 18
  issue: 9
  year: 1994
  ident: 10.1016/j.jmrt.2025.05.229_bib12
  article-title: Analysis of the wear of forming tools in the process of extruding ceramic bands using selected research methods for evaluating operational durability
  publication-title: Materials 2025
– volume: 20
  start-page: 169
  year: 2018
  ident: 10.1016/j.jmrt.2025.05.229_bib11
  article-title: Analysis of the influence of lubrication conditions on tool wear used in hot die forging processes
  publication-title: Eksploat i Niezawodn
  doi: 10.17531/ein.2018.2.01
– volume: 39
  issue: 2
  year: 2021
  ident: 10.1016/j.jmrt.2025.05.229_bib6
  article-title: Analysis of the production process of the forked forging used in the excavator drive system in order to improve the currently implemented technology by the use of numerical modeling
  publication-title: Mater Sci Pol
  doi: 10.2478/msp-2021-0020
– volume: 92
  start-page: 23
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib8
  article-title: The use of a measuring arm with a laser scanner for analysis and support of regenerative surfacing processes of forging dies
  publication-title: Weld Technol Rev
  doi: 10.26628/wtr.v92i3.1103
– volume: 15
  start-page: 268
  year: 2021
  ident: 10.1016/j.jmrt.2025.05.229_bib21
  article-title: Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: simulation, mechanical properties, and microstructural evolution
  publication-title: J Mater Res Technol
  doi: 10.1016/j.jmrt.2021.08.022
– volume: 9
  start-page: 15874
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib22
  article-title: Formation of nano-sized compounds during friction stir welding of CueZn alloys: effect of tool composition
  publication-title: J Mater Res Technol
  doi: 10.1016/j.jmrt.2020.11.058
– volume: 109
  start-page: 1385
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib15
  article-title: Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel
  publication-title: Int J Adv Manuf Technol
  doi: 10.1007/s00170-020-05641-y
– volume: 19
  start-page: 338
  issue: 3
  year: 2017
  ident: 10.1016/j.jmrt.2025.05.229_bib25
  article-title: A durability analysis of forging tools for different operating conditions with application of a decision support system based on artificial neural networks.Eksploatacja i Niezawodnosc – Maintenance and
  publication-title: Reliability
– volume: 7
  start-page: 116509
  issue: 11
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib19
  article-title: A screening strategy for hot forging combining high-throughput forging experiment and machine learning
  publication-title: Mater. Res. Express
  doi: 10.1088/2053-1591/abc4f7
– volume: 17
  start-page: 3005
  issue: 12
  year: 2024
  ident: 10.1016/j.jmrt.2025.05.229_bib13
  article-title: Increasing the working time of forging tools used in the industrial process of producing a disk-type forging assigned for a gearbox through the application of hybrid layers
  publication-title: Materials
  doi: 10.3390/ma17123005
– volume: 30
  start-page: 2708
  year: 2021
  ident: 10.1016/j.jmrt.2025.05.229_bib17
  article-title: Research on neural network prediction of multidirectional forging microstructure evolution of GH4169 superalloy
  publication-title: J Mater Eng Perform
  doi: 10.1007/s11665-021-05536-3
– volume: 42
  start-page: 171
  year: 2024
  ident: 10.1016/j.jmrt.2025.05.229_bib4
  article-title: Evaluation of cracking risk of 80MnSi8-6 nanobainitic steel during hot forging in the range of lower temperature limits
  publication-title: Mater Sci Pol
  doi: 10.2478/msp-2024-0011
– volume: 1
  start-page: 1
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib7
  article-title: The application of a new , innovative , hybrid technology combining hardfacing and nitriding to increase the durability of forging tools
  publication-title: Arch Civ Mech Eng
– volume: 42
  start-page: 113
  year: 2024
  ident: 10.1016/j.jmrt.2025.05.229_bib10
  article-title: Influence of the nitriding process on the durability of tools used in the production of automotive forgings in industrial hot die forging processes on hammers
  publication-title: Mater Sci
– volume: 18
  start-page: 1079
  year: 2018
  ident: 10.1016/j.jmrt.2025.05.229_bib14
  article-title: Durability analysis of forging tools after different variants of surface treatment using a decision-support system based on artificial neural networks
  publication-title: Arch Civ Mech Eng
  doi: 10.1016/j.acme.2018.02.010
– volume: 5
  start-page: 32
  issue: 2
  year: 2021
  ident: 10.1016/j.jmrt.2025.05.229_bib5
  article-title: Analysis of the industrial process of producing a hub forging used in motorcar power transmission systems — a case study. 2021
  publication-title: J Manuf Mater Process
– volume: 21
  start-page: 263
  year: 2020
  ident: 10.1016/j.jmrt.2025.05.229_bib16
  article-title: Materials Today : proceedings ANN-AGCS for the prediction of temperature distribution and required energy in hot forging process using finite element analysis
  publication-title: Mater Today Proc
  doi: 10.1016/j.matpr.2019.05.426
– volume: 14
  start-page: 554
  issue: 5
  year: 2024
  ident: 10.1016/j.jmrt.2025.05.229_bib20
  article-title: Development of neural networks to study flow behavior of medium carbon microalloyed steel during hot forming
  publication-title: Metals
  doi: 10.3390/met14050554
– volume: 13
  start-page: 815
  issue: 4
  year: 2023
  ident: 10.1016/j.jmrt.2025.05.229_bib24
  article-title: Hot forging die design optimization using FEM analysis for near-net forming of 18CrNiMo7-6 steel pinion shaft
  publication-title: Metals
  doi: 10.3390/met13040815
– ident: 10.1016/j.jmrt.2025.05.229_bib18
– volume: 129
  year: 2021
  ident: 10.1016/j.jmrt.2025.05.229_bib1
  article-title: Current failure mechanisms and treatment methods of hot forging tools (dies) - a review
  publication-title: Eng Fail Anal
  doi: 10.1016/j.engfailanal.2021.105678
– volume: 26
  start-page: 154
  year: 2019
  ident: 10.1016/j.jmrt.2025.05.229_bib23
  article-title: Softening and recrystallization behavior of a new class of ferritic steel
  publication-title: J Iron Steel Res Int
  doi: 10.1007/s42243-019-00230-0
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Snippet 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...
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StartPage 432
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
URI https://dx.doi.org/10.1016/j.jmrt.2025.05.229
https://doaj.org/article/5c62585c714f440fa98aa2db56648462
Volume 37
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