Experimental investigation of wear-hardness trade-off mapping for quenched HSS tools under different quenching media and austenitizing temperatures

Razali Razali; Reinaldi Teguh Setyawan; Alfazan Yendra Yendra

doi:10.30811/jpl.v24i2.8733

Experimental investigation of wear-hardness trade-off mapping for quenched HSS tools under different quenching media and austenitizing temperatures

Razali Razali, Reinaldi Teguh Setyawan, Alfazan Yendra Yendra

Abstract

This study investigates the wear-hardness trade-off of quenched AISI M2 High-Speed Steel (HSS) cutting tools subjected to three austenitizing temperatures (800, 900, and 1000°C) and three quenching media (salt water, Bromus oil, and SAE 20 oil), without subsequent tempering treatment. A factorial dataset consisting of three austenitizing temperatures (800, 900, and 1000°C) and three quenching media (salt water, Bromus oil, and SAE 20 oil) was analyzed under fixed turning parameters (n=300 rpm, f=0.19 mm/rev, a_p=1.5 mm). Tool performance was evaluated using flank wear (VB) and Rockwell hardness (HRC), with five hardness readings per condition to quantify repeatability. The results show a dominant temperature effect: VB decreases monotonically as austenitizing temperature increases, while hardness rises markedly at 1000°C, forming a favorable region with simultaneously low wear and high hardness. The best combined performance was obtained at 1000°C with SAE 20 oil, achieving VB = 0.066 mm and HRC ≈ 82.8. A desirability-style composite index D, derived from normalized VB (smaller-is-better) and HRC (larger-is-better), produced decision and rank maps that consistently identified the 1000°C region as the optimal operating window, with oil quenching preferred when high-temperature austenitizing is feasible. Hardness repeatability metrics further indicated that the 1000°C conditions exhibit the lowest scatter, strengthening their practical robustness. The proposed mapping framework offers a compact, reproducible approach to visualize the wear–hardness trade-off and to rank heat-treatment conditions using existing experimental data.

Keywords

High-speed steel; quenching medium; heat map mapping; trade-off analysis; austenitizing temperature

Full Text:

PDF

References

J. L. Dossett and G. E. Totten, Eds., ASM Handbook, Volume 4A: Steel Heat Treating Fundamentals and Processes. Materials Park, OH, USA: ASM International, 2013.

G. A. Roberts, G. Krauss, and R. Kennedy, Tool Steels, 5th ed. Materials Park, OH, USA: ASM International, 1998.

I. Hutchings and P. Shipway, Tribology: Friction and Wear of Engineering Materials, 2nd ed. Oxford, UK: Butterworth-Heinemann, 2017.

J. Derringer and R. Suich, “Simultaneous Optimization of Several Response Variables,” J. Qual. Technol., vol. 12, no. 4, pp. 214–219, 1980, doi: 10.1080/00224065.1980.11980968.

C. Luo, Z. Huang, Z. Gao, L. Wang, M. Xiao, and Y. Zhao, “Effects of Austenitizing Conditions on the Microstructure and Mechanical Properties of High-Speed Steel,” Metals, vol. 7, no. 1, p. 27, 2017, doi: 10.3390/met7010027.

H. Zapata, M. R. Buitrago, and J. J. Coronado, “Wear and thermal behaviour of M2 high-speed steel, completely and partially quenched,” J. Mater. Sci., vol. 33, pp. 3219–3225, 1998, doi: 10.1023/A:1004324729342.

E. Fu, G. E. Totten, and J. Dossett, “The Effects of Tempering on the Toughness of an As-Cast High-Speed Steel Work Roll,” J. Mater. Eng. Perform., vol. 17, pp. 535–542, 2008, doi: 10.1007/s11665-007-9174-4.

J. H. Kang and Y. K. Lee, “Microstructure and Wear Resistance of High-Speed Steel Roll,” Metall. Mater. Trans. A, vol. 47, pp. 3365–3374, 2016, doi: 10.1007/s11661-016-3536-1.

Y. Zhou, F. Xiong, J. Xu, C. Wang, Y. Jiang, and Z. Fan, “Morphology and Properties of M2C Eutectic Carbides in AISI M2 Steel,” ISIJ Int., vol. 50, pp. 1151–1157, 2010, doi: 10.2355/isijinternational.50.1151.

K. C. Hwang, S. Lee, and H. C. Lee, “Effects of alloying elements on microstructure and fracture properties of cast high speed steel rolls. Part I: Microstructural analysis,” Mater. Sci. Eng. A, vol. 254, pp. 282–295, 1998, doi: 10.1016/S0921-5093(98)00626-1.

E. Gordo, J. M. Torralba, and R. Velasco, “Wear mechanisms in high-speed steels reinforced with NbC/TaC,” Wear, vol. 239, pp. 251–259, 2000, doi: 10.1016/S0043-1648(00)00329-X.

M. X. Wei, Y. T. Luo, F. Wang, and K. M. Chen, “Effects of carbon and vanadium on microstructures and abrasive wear resistance of high speed steel,” Tribol. Int., vol. 39, pp. 641–648, 2006, doi: 10.1016/j.triboint.2005.04.035.

Z. Zalisz, S. Zalisz, J. Kusiński, M. Rozmus-Górnikowska, L. Stobierski, and J. Borysiuk, “Friction and wear of lubricated M3 Class 2 sintered high speed steel with additions of (NbC and TaC) and VC,” Wear, vol. 258, pp. 701–711, 2005, doi: 10.1016/j.wear.2004.09.069.

V. B. Baglyuk and L. A. Poznyak, “Ultradispersed composition of secondary hardening of high-speed steel,” Powder Metall. Met. Ceram., vol. 41, pp. 366–368, 2002, doi: 10.1023/A:1021113025628.

A. Akhbarizadeh, H. Saghafian, and G. Shafyei, “Effects of cryogenic treatment on wear behavior of D6 tool steel,” Mater. Des., vol. 30, no. 8, pp. 3259–3264, 2009, doi: 10.1016/j.matdes.2008.11.016.

A. Akhbarizadeh, K. Amini, and S. Javadpour, “Effects of applying an external magnetic field during the deep cryogenic heat treatment on the corrosion resistance and wear behavior of 1.2080 tool steel,” Mater. Des., vol. 41, pp. 114–123, 2012, doi: 10.1016/j.matdes.2012.03.045.

A. Molinari, M. Pellizzari, S. Gialanella, G. Straffelini, and K. H. Stiasny, “Effect of deep cryogenic treatment on the mechanical properties of tool steels,” J. Mater. Process. Technol., vol. 118, pp. 350–355, 2001, doi: 10.1016/S0924-0136(01)00973-6.

J. Y. Huang, Y. T. Zhu, X. Z. Liao, I. J. Beyerlein, M. A. Bourke, and T. E. Mitchell, “Microstructure of cryogenic treated M2 tool steel,” Mater. Sci. Eng. A, vol. 339, pp. 241–244, 2003, doi: 10.1016/S0921-5093(02)00165-X.

D. C. Montgomery, Design and Analysis of Experiments, 10th ed. Hoboken, NJ, USA: Wiley, 2019.

G. Derringer and R. Suich, “Simultaneous optimization of several response variables,” Journal of Quality Technology, vol. 12, no. 4, pp. 214–219, 1980, doi: 10.1080/00224065.1980.11980968.

S. J. S. Chelladurai, K. Murugan, A. P. Ray, M. Upadhyaya, V. Narasimharaj, and S. Gnanasekaran, “Optimization of process parameters using response surface methodology: A review,” Materials Today: Proceedings, vol. 37, pt. 2, pp. 1301–1304, 2021, doi: 10.1016/j.matpr.2020.06.466.

C. Camposeco-Negrete, “Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum,” Journal of Cleaner Production, vol. 91, pp. 109–117, 2015, doi: 10.1016/j.jclepro.2014.12.017.

R. T. Setyawan, E. Muthoriq, and S. Syahrizal, “Multi-objective tribological and energy optimization of an automatic valve lapping machine using a hybrid RSM NSGA-II approach,” Jurnal Polimesin, vol. 24, no. 1, pp. 119–131, 2026, doi: 10.30811/jpl.v24i1.8454.

V. K. Sharma, M. Rana, T. Singh, A. K. Singh, and K. Chattopadhyay, “Multi-response optimization of process parameters using Desirability Function Analysis during machining of EN31 steel under different machining environments,” Materials Today: Proceedings, vol. 44, pp. 3121–3126, 2021, doi: 10.1016/j.matpr.2021.02.809.

A. M. Rahman, S. M. A. Rob, and A. K. Srivastava, “Modeling and optimization of process parameters in face milling of Ti6Al4V alloy using Taguchi and grey relational analysis,” Procedia Manufacturing, vol. 53, pp. 204–212, 2021, doi: 10.1016/j.promfg.2021.06.023.

Y. Lin et al., “Multi-response optimization of process parameters in nitrogen-containing gray cast iron milling process based on application of non-dominated ranking genetic algorithm,” Heliyon, vol. 8, no. 11, Art. no. e11629, 2022, doi: 10.1016/j.heliyon.2022.e11629.

A. Faveto, F. Lombardi, P. Chiabert, and F. Segonds, “A circular approach to foster additive manufacturing early design stages sustainability: A methodological proposal,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 18, pp. 815–836, 2024, doi: 10.1007/s12008-023-01577-1.

A. Chakrabarti, F. Ahmad, M. Jarke, and C. Quix, “Monitoring large scale production processes using a rule-based visualization recommendation system,” SN Computer Science, vol. 4, Art. no. 32, 2023, doi: 10.1007/s42979-022-01419-z.

Z. Sherif and K. Salonitis, “A systematic review of decision tools for process selection and performance improvement in manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 141, pp. 1113–1141, 2025, doi: 10.1007/s00170-025-16806-y.

DOI: http://dx.doi.org/10.30811/jpl.v24i2.8733