Study on High Temperature Wear Law and Wear Prediction Method of Hot Forging Die Steel H13

Bin  Chen; Yuteng  Zhang

doi:10.6180/jase.202409_27(9).0001

Study on High Temperature Wear Law and Wear Prediction Method of Hot Forging Die Steel H13

Material Engineering Computer Science and Information Engineering

Bin Chen^1,2This email address is being protected from spambots. You need JavaScript enabled to view it. and Yuteng Zhang²

¹School of Materials Science and Engineering, Anhui University of Technology, Ma’anshan, 243032, China

²School of Management Science and Engineering, Anhui University of Technology, Ma’anshan, 243032, China

Received: July 25, 2023
Accepted: November 2, 2023
Publication Date: November 30, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202409_27(9).0001

Hot forging dies are commonly encountered process equipment in the industrial setting. Due to repeated mechanical and alternating loads under high temperature for an extended period of time, these dies are prone to wear and tear, which can impact their service life and drive up production costs for enterprises. To address this issue, it is crucial to investigate the high temperature wear law and wear prediction methodology of hot forging dies. In this paper, hot forging die steel H13 (HFDS - H13) that has undergone heat treatment is employed for experimental research. The results demonstrate that within the range of experimental temperature and load, as the temperature rises, the material’s hardness decreases. Elevated temperatures and loads result in an overall decrease in the average friction coefficient, while the wear volume increases. Based on the test data, prediction models are designed using the modified Archard model and BP neural network. Both models display great prediction accuracy. However, under certain conditions, the BP neural network prediction model is superior. After utilizing interpolation algorithms to enhance the data, the BP neural network model demonstrates improved prediction accuracy by 2.11% and exhibits better stability. The article provides a reference for the study of high temperature wear law and wear prediction of other materials.

Keywords: hot forging die steel H13; high temperature wear; wear law; wear prediction; neural network

[1] Y. Yu, J. Zottis, M. Wolfgarten, and G. Hirt, (2019) “Investigation of applying protective sheet metal die covers for hot forging dies on a cross-forging geometry" The International Journal of Advanced Manufacturing Technology 102: 999–1007. DOI: 10.1007/s00170-018- 03250-4.
[2] S. Joshy, K. Jayadevan, M. S. George, and B. Prasad, (2022) “Influence of hot forging on microstructure in double tempered H11 hot forging dies" Metallurgical Research & Technology 119(1): 115. DOI: 10.1051/metal/2022004.
[3] X. Pan, H. Wang, Q. Liu, Z. Yao, J. Li, and C. Jiang, (2022) “High-Temperature Wear Properties of 35Ni15Cr Fe-Based Self-Lubricating Die Materials" Metals 12(12): 2016. DOI: 10.3390/met12122016.
[4] Y. Zhou, L. Chen, W. Jiang, S. Cui, and X. Cui, (2022) “Investigation on elevated-temperature wear performance and wear failure mechanism of a tungsten-system hotworking die steel" Surface Topography: Metrology and Properties 10(3): 035007. DOI: 10.1088/2051-672X/ac7a4f.
[5] Y. Shi, L. Yu, X. Guo, C. Cheng, P. Zuo, and Y. Dai, (2022) “High-temperature oxidation wear performance and mechanism of Cr–Mo (W)–V hot-work die steel" International Journal of Materials Research 113(1): 12–32. DOI: 10.1515/ijmr-2020-8074.
[6] Z. Bai, N. Su, H. Yang, and X. Wu, (2022) “Wear characteristics of austenitic steel and martensitic steel at high temperature" Materials Research Express 9(8): 086504. DOI: 10.1088/2053-1591/ac86b9.
[7] I. Stepankin and E. Pazdniakou, (2021) “Effect of Retained Austenite on the Wear Resistance of C80W1 and 90CrSi5 Tool Steels" Journal of Friction and Wear 42(4): 239–245. DOI: 10.3103/S1068366621040127.
[8] P. Chhabra and M. Kaur, (2020) “Elevated-temperature wear study of HVOF spray Cr3C2–NiCr-coated die steels" Journal of Tribology 142(6): 061401. DOI: 10.1115/1.4046017.
[9] K. Drozd, M. Walczak, M. Szala, and K. Gancarczyk, (2020) “Tribological behavior of AlCrSiN-coated tool steel K340 versus popular tool steel grades" Materials 13(21): 4895. DOI: 10.3390/ma13214895.
[10] S. A. Bakdemir, D. Özkan, C. Türküz, and S. Salman, (2023) “Wear performance under dry and lubricated conditions of duplex treatment TiN/TiCrN coatings deposited with different numbers of CrN interlayers on steel substrates" Wear 526: 204931. DOI: 10.1016/j.wear.2023. 204931.
[11] N. Sabangban, N. Mahayotsanun, S. Sucharitpwatskul, and S. Mahabunphachai, (2016) “Wear prediction of die coatings in strip ironing by finite element simulation" Transactions of the IMF 94(4): 199–203. DOI: 10.1080/00202967.2016.1180813.
[12] S. Chen, H. Ding, Z. Tang, S. Hao, and Y. Zhao, (2022) “Influence of rice straw forming factors on ring die wear and improved wear prediction model during briquetting" Biosystems Engineering 214: 122–137. DOI: 10.1016/j.biosystemseng.2021.12.012.
[13] A. Macioł, P. Macioł, and B. Mrzygłód, (2020) “Prediction of forging dies wear with the modified Takagi– Sugeno fuzzy identification method" Materials and Manufacturing Processes 35(6): 700–713. DOI: 10.1080/10426914.2020.1747627.
[14] X. Qiao, A. Cheng, X. Nie, and M. Ning, (2018) “A study on die wear prediction for automobile panels stamping based on dynamic model" The International Journal of Advanced Manufacturing Technology 97: 1823–1833. DOI: 10.1007/s00170-018-1811-6.
[15] C. Tan, E. Ghassemieh, and W. Goh, (2009) “Wear analysis and prediction of the life of a riveting die used in the automotive industry" Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223(11): 1395–1407. DOI: 10.1243/09544054JEM1518.
[16] V. Jagota and R. Sharma, (2018) “Interpreting H13 steel wear behavior for austenitizing temperature, tempering time and temperature" Journal of the Brazilian Society of Mechanical Sciences and Engineering 40(4): 219. DOI: 10.1007/s40430-018-1140-6.
[17] N. Tran Van, S. Yang, and A. Phung Tuan, (2018) “Microstructure and properties of Cu/TiB2 wear resistance composite coating on H13 steel prepared by in-situ laser cladding" Optics & Laser Technology 108: 480–486. DOI: 10.1016/j.optlastec.2018.07.036.
[18] D. Özkan, M. A. Yilmaz, D. Karakurt, M. Szala, M. Walczak, S. A. Bakdemir, C. Türküz, and E. Sulukan, (2023) “Effect of AISI H13 steel substrate nitriding on AlCrN, ZrN, TiSiN, and TiCrN multilayer PVD coatings wear and friction behaviors at a different temperature level" Materials 16(4): 1594. DOI: 10.3390/ma16041594.
[19] C.-l. Gui, (1990) “The Archard design calculation model and its application methods" Lubrication Engineering 15(1): 12–21.
[20] Z.-k. Yuan, L.-j. Wu, J. Wang, P. Zhang, and Z.-z. Wei, (2022) “Optimization of superhydrophobic coatings based on neural network and genetic algorithm" Surface Technology 51(1): 240–246. DOI: 10.16490/j.cnki.issn.1001-3660.2022.01.025.
[21] T.-t. He, S. Ruo-nan, J. Liu, S.-m. Du, Y.-z. Zhang, and S.-e. Deng, (2020) “Sliding friction and wear properties of GCr15 steel under different loads" Transactions of Materials and Heat Treatment 41(7): 105–110. DOI: 10.13289/j.issn.1009-6264.2019-0553.
[22] Z. Wang, M. Zhou, Y. Jiang, and Z. Liu, (2023) “Effects of in situ NbC on the microstructure and high-temperature friction wear properties of 4Cr5MoSiV1 steel" Journal of Materials Research and Technology 24: 6159–6173. DOI: 10.1016/j.jmrt.2023.04.221.
[23] S. Li, X. Wu, S. Chen, and J. Li, (2016) “Wear resistance of H13 and a new hot-work die steel at high temperature" Journal of Materials Engineering and Performance 25: 2993–3006. DOI: 10.1007/s11665-016-2124-2.
[24] S.-z. Wen, P. Huang, Y. Tian, and L.-r. Ma. Principles of tribology. Tsinghua University Press, 191–312.
[25] B.-l. Zhu, Z.-x. Yang, and J.-j. Liu, (1989) “The measurement of frictional temperature and its effect in sliding contact" Tribology 9(1): 23–29.
[26] W. Jiang, S. Wang, Y. Deng, and X. Guo, (2022) “Microstructure stability and high temperature wear behavior of an austenite aging steel coating by laser cladding" Materials Characterization 184: 111700. DOI: 10.1016/j.matchar.2021.111700.
[27] Y. Qian, Y. Liang, and R. Guan, (2014) “Improving activated sludge classification based on imbalanced data" Journal of Hydroinformatics 16(6): 1331–1342. DOI: 10.2166/hydro.2014.123.
[28] S. Feng, H. Zhou, and H. Dong, (2019) “Using deep neural network with small dataset to predict material defects" Materials & Design 162: 300–310. DOI: 10.1016/j.matdes.2018.11.060.
[29] L. Guo, J.-m. Pan, J.-l. Lu, and S. Yong-xian, (2010) “Application of interpolation algorithms in smart substation" Electric Power Automation Equipment 30(10): 103–105. DOI: 10.3969/j.issn.1006-6047.2010.10.023.
[30] T. Blu, P. Thévenaz, and M. Unser, (2004) “Linear interpolation revitalized" IEEE Transactions on Image Processing 13(5): 710–719. DOI: 10.1109/TIP.2004. 826093.