wear resistance evaluation of cryogenically treated aisi–h11...

Indian Journal of Engineering & Materials Sciences Vol. 26, April 2019, pp. 112-125

Wear resistance evaluation of cryogenically treated AISI–H11 steel: An optimization approach

Sanjeev Katoch a*, Rakesh Sehgalb & Vishal Singhc

a Institute for Auto Parts & Hand Tools Technology, A-9, Phase V, Focal Point, Ludhiana 141 010, India b Department of Mechanical Engineering, National Institute of Technology, Hamirpur 177 005, India

c Centre for Materials Science and Engineering, National Institute of Technology, Hamirpur 177 005, India

Received: 18 October 2018

The consequences of key process parameters of cryogenic treatment (i.e. holding time and temperature) on the average coefficient of friction and wear rate in dry sliding condition at different level of load (60, 80, 100, 120 & 140 N) and sliding velocity (0.628, 0.942, 1.257, 1.571 & 1.885 m/s) have been explored through cubic regression mathematical modeling technique. An average error of 1% and 9.9% has been observed in the experimental and model given values for coefficient of friction and wear rate. Mathematical model equation capability is within the range of 95% confidence level. Moreover, desirability function approach has been employed to find out the process parameter to have lower wear resistance. Worn out samples have been examined with field emission scanning electron microscope (FESEM) and reveals that the operative mechanism for wear is delaminating and rupturing of matrix.

Keywords: Tool wear, Friction, Cryogenic processing, Tool steels, Modeling, Optimization

1 Introduction AISI-H11 steel is utilized for various engineering

applications and in particularly for moulds for die casting of light engineering alloys, inserts for forging dies and punches. To refine, improve and modify the microstructure and mechanical properties of steel quenching and tempering is mainly used. During their service lives, tools and dies experience severe operational conditions, which results in wear and their eventual failure. Therefore, material making and shaping industries seek for the treatments which can have ability to develop the better tribological and mechanical properties of die materials. To have low wear of tools different treatment techniques like, surface carbon enrichment, plasma-nitriding and nitriding have been utilized since long1,2. Nevertheless, these all are only surface modification treatments and get better wear resistance, surface hardness of the treated materials.

To reduce the internal stresses and improve the dimensional stability cold processing is well known. Cold processing is carried out at temperature3,4 ranging from −60 to −80 °C. In the past three decades, researchers put forward the material processing which carried out at temperature level around −196 °C at

slow cooling rate and named as cryogenic processing/ treatment. This treatment not only enhances the tool steel surface mechanical and tribological properties but also affects the bulk properties and its effects are permanent5-8. Major factor which affects the overhaul life of tools is morphology of its microstructure and dimensional stability. A number of researchers have mentioned the worth of cryogenic treatment (CT) for the alteration of tool steel microstructure and augment of their hardness e.g., Das et al.9-12 reported for the cold work tool steel, grade AISI-D2, Koneshlou et al.13 reported for hot die steel grade AISI H13; Li and Wu14 for tool steel and Katoch et al.15,16 studied the influence of CT on AISI-H11 and AISI-H13 steel for mechanical properties and evolution of microstructure. They reported that CT modified the microstructure and improves the mechanical properties in comparison to vacuum treated samples.

A reduction on austenite phase that was changed by the martensite phase. Hence, it shows that the micro structure changes related to CT aids to produce an austenite reduction and carbide precipitation. It leads to yield enhancements over material resistance by an increase its hardness17,18. Prieto et al.19 in martensitic stainless steel reported that CT samples having a higher amount of non-dissolved carbon in the martensitic matrix; consequently leading to an improved hardness and elastic limit in comparison

—————— *Corresponding author (E-mail: [email protected])

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with the conventionally treated ones. They are responsible for decreasing the friction and acts only marginally to decrease the wear coefficient.

During the CT decay of martensite leading to improved precipitation of secondary carbides with more consistent distribution20. As the fractional disbanding of brittle primary carbides is supposed to be accountable for the toughness since the decrease in soft retained austenite content21. Hence, with the appropriate selection of CT process parameters concurrent improvement of hardness and toughness can be achieved as demonstrated in the case of tool steel 45WCrV7 by Vahdat et al.22. Yao and Zhou23 studied the CT soak time effect on the “GB35CrMoV” steel and claimed that higher cryogenic soaking times generate significant improvements in hardness and wear resistance. The cryogenically processed samples receive the effect of deep cryogenic treatment that is produced in the acceleration on the Ostwald ripening process23.

Currently, researchers have cited the potential and worth of CT on tool steel with respect to better tribological behavior of tool and die steels which were subjected to CT8, 9,14, 24-28. Senthilkumar and Rajendran8 utilized L27 orthogonal approach of Taguchi technique to examine the effect of selected factors of CT on medium carbon low alloy steel grade 4140. They selected the soaking time range for the CT is 12, 18, 24 h and claimed that 24 h is optimum soaking time for the 4140 steel. They choose the 24 h maximum soak time due to low content of chromium (1.19 wt%) in 4140 steel and on the base of previous finding of Darwin et al.29 on martensitic grade stainless steel having 18% Cr. Darwin et al. demonstrated that holding period in the range of 6 to 24 h contributed 14% and 24% while holding period was in the range of 12 to 36 h in case of martensitic grade stainless steel having 18% Cr. Therefore, they claim the optimum soaking period was 36 h for stainless steel having 18% Cr 22.

The selection of the set of CT parameters (like, cryogenic treatment temperature, soaking period / time at cryogenic temperature, rate of cooling and heating during cryogenic treatment) are the tedious task for the metallurgist or heat treatment engineers to achieve the optimal set of mechanical and tribological properties. Review of literature also divulges that CT of the materials is under practice since last six decades but there is no systematic study available on the wear resistance evaluation of AISI-H11 steel subjected to different CT cycle through modeling and

optimization. Hence, the endeavor of this experimental study to develop the models for wear behavior of AISI-H11 steel subjected to varied CT under dry sliding against cold work steel.

This is the first study to explore the wear behavior of differently Cryogenically treated AISI-H11 steel utilizing Box-Cox technique for the buildup of mathematical model equations. Therefore, authors claim the novelty of this study. In various industrial applications such as flash cutting in hot forging process, sheet metal punching and piercing process hot die steel (AISI H11) is used as the die and cold work steel as punch (AISI D3) and thus these materials face the strong challenge of wear resistance. Hence this hot die steel–cold work steel tribo-pair is selected for the present experimental work. Due to higher global competition of market the industries want the long-life material forming and shaping tool at lower cost to remain competitive in the business. Hence, necessity felt to get better tribological and mechanical properties of tool materials used for material forming and shaping by exploring the better treatment and its process parameters in order to enhance efficiency and reduced costs.

2 Materials and Methods

2.1 Material, counter face and sample preparation 16 mm diameter rolled bar of die steel AISI-H11

tested on spark emission spectrometer (model: DV6; producer: BAIRD, USA) to ascertain its chemical composition following ASTM standards E 415-201430. The observed chemical composition in wt%: 0.40-C; 0.86-Si; 0.36-Mn; 5.05-Cr; 1.30-Mo; 0.98-V; 0.018-P; 0.007-S; balance – Fe)

Hardened (measured average hardness 52 HRC) counter face ring (AISI D3 steel) of outer diameter 60 mm, inner diameter 25 mm and thickness 20 mm was used. Hardening of counter face ring carried out in electric heated furnace at 890 °C with 30 min holding time and followed by oil quench to room temperature. Tempering of counter face ring carried out at 400 ±5 °C with 1 h holding time in electric heated furnace. To measure the average value of hardness five hardness readings were taken at different points.

Prismatic sample blocks (6.35 X 6.35 X 9 mm) were extracted using wire electrical discharge machine following ASTM standards G 77-201031.

2.2 Samples treatment Vacuum hardening of wear test samples of H11steel

was carried out in vacuum furnace, at a 1040 ± 5 °C

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with 30 min holding time at this temperature and followed by nitrogen gas quench at gas pressure of 5 bar (0.5 MPa) to room temperature. After first tempering at 550 °C with 2 h holding time samples were divided in to seven categories. A 3T: No cryogenic treatment and three tempering at 550 °C, 570 °C and 600 °C with 2 h holding time; A T C1(6)T: CT at -154 °C for holding time of 6 h and single tempering at 600 °C with 2 h holding time; A T C1(21)T: CT at -154 °C for holding time of 21 h and single tempering at 600 °C with 2 h holding time; A T C1(36)T: CT at -154 °C for holding time of 36 h and single tempering at 600 °C with 2 h holding time; A T C2(6)T: CT at -184 °C for holding time of 6 h and single tempering at 600 °C with 2 h holding time; A T C2(21)T: CT at -184 °C for holding time of 21 h and single tempering at 600 °C with 2 h holding time; A T C2(36)T: CT at -184 °C for holding time of 36 h and single tempering at 600 °C with 2 h holding time. CT of die steel AISI-H11 carried out in a cryogenic processor (Make: Primero Enserve, Chennai, India). In order to circumvent the thermal soaking of the surface and core of the material material owing to the rapid temperature incline sluggish ramp up and ramp down rate (1 °C/min) applied from the ambient temperature to selected CT temperature.

In industrial practice the hot die steel tempered three times after hardening to removes the retained austenite and to have better toughness. Hence, A3T samples tempered for three times and taken as the benchmark in this work.

2.3 Wear test Sliding wear test on hexane cleaned samples were

carried out on Multi Tribo Tester (model: TR-30M4; producer: DUCOM, India) (Fig. 1 shows the schematic of wear test set up) in dry condition by maintaining the gap of 2 mm between the rotating counter face ring and static rectangular block sample before conducting the experiment. Total duration for each experiment was 300 s. Rotating counter face ring average surface roughness (Ra) was maintained less than 0.2 μm by polishing it with silicon carbide emery paper.

Electronic analytical balance of accuracy 0.00001 g utilized to have samples weight before and after each

experiment to calculate weight loss and wear rate (WR) using Eq (1):

R

WW

2πrnt

… (1)

Where WR = wear rate in gm/m, ΔW = difference in the weight of wear test sample

before and after wear test in gm, 2πr = sliding distance in m, n = number of revolutions in second, t = time in second.

In this study responses for the average co-efficient of friction of the conventionally and cryogenically treated samples with rotating counter face ring at chosen set of operating parameters (sliding velocity: 0.628-1.885 m/s and normal load: 60-140 N) measured directly through the equipment software.

Table 1 presented all the factors and their levels that selected for this study. Full factorial design of experiment technique (software Design-Expert 7.1.5; Stat Ease, USA) was employed for the design of the whole experiments. This experiment design allows one to study the effect of each factor on the response variables, as well as their interaction with each other. Total 175 experimental runs performed to have response for “WR” and average coefficient of friction (μa) within selected range of operating parameters. Experimental variables for the wear test experiment and results of chosen response parameters of tribological experiments are shown in Table 2.

Fig. 1 — Schematic of wear test set up.

Table 1 — Experimental factors and their levels.

Factor Unit Levels Values

Treatment Type

- A3T ATC1(6)T ATC1(21)T ATC1(36)T ATC2(6)T ATC2(21)T ATC2(6)T

Load N 60 80 100 120 140 - - Sliding Speed m/s 0.628 0.942 1.257 1.571 1.885 - -


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Table 2 — Experimental variables with their different levels of input parameters and results for chosen response parameters.

S. No.

Type of Treatment

Load (N)

Sliding Velocity (m/s)

Wear Rate (gm/m)

Average Coefficient of Friction ( μa)

Sl. No.

Type of Treatment

Load (N)


Wear Rate (gm/m)


1. A3T 60 0.628 2.84E-06 0.231 56. ATC1(21)T 80 0.628 3.25E-06 0.2094 2. A3T 60 0.942 4.47E-06 0.2248 57. ATC1(21)T 80 0.942 4.95E-06 0.1995 3. A3T 60 1.257 1.79E-05 0.2199 58. ATC1(21)T 80 1.257 1.66E-05 0.1901 4. A3T 60 1.571 6.9E-05 0.21 59. ATC1(21)T 80 1.571 6.91E-05 0.1832 5. A3T 60 1.885 8.71E-05 0.201 60. ATC1(21)T 80 1.885 8.26E-05 0.179 6. A3T 80 0.628 4.49E-06 0.223 61. ATC1(21)T 100 0.628 5.28E-06 0.1976 7. A3T 80 0.942 6.12E-06 0.2147 62. ATC1(21)T 100 0.942 6.98E-06 0.1902 8. A3T 80 1.257 2.56E-05 0.2076 63. ATC1(21)T 100 1.257 2.55E-05 0.1824 9. A3T 80 1.571 8.18E-05 0.1995 64. ATC1(21)T 100 1.571 8.44E-05 0.1759 10. A3T 80 1.885 0.000109 0.191 65. ATC1(21)T 100 1.885 1.0 E-04 0.1703 11. A3T 100 0.628 7.47E-06 0.211 66. ATC1(21)T 120 0.628 8.34E-06 0.1846 12. A3T 100 0.942 1.01E-05 0.205 67. ATC1(21)T 120 0.942 1.16E-05 0.1804 13. A3T 100 1.257 3.21E-05 0.1995 68. ATC1(21)T 120 1.257 3.52E-05 0.1763 14. A3T 100 1.571 1.21 E-04 0.1905 69. ATC1(21)T 120 1.571 1.16 E-04 0.1706 15. A3T 100 1.885 1.36 E-04 0.18 70. ATC1(21)T 120 1.885 1.34 E-04 0.1665 16. A3T 120 0.628 1.09E-05 0.206 71. ATC1(21)T 140 0.628 1.5E-05 0.1753 17. A3T 120 0.942 1.54E-05 0.2012 72. ATC1(21)T 140 0.942 1.76E-05 0.1701 18. A3T 120 1.257 4.8E-05 0.1928 73. ATC1(21)T 140 1.257 4.9E-05 0.167 19. A3T 120 1.571 1.52 E-04 0.1824 74. ATC1(21)T 140 1.571 1.52 E-04 0.1607 20. A3T 120 1.885 1.7 E-04 0.175 75. ATC1(21)T 140 1.885 1.4 E-04 0.158 21. A3T 140 0.628 1.92E-05 0.1998 76. ATC1(36)T 60 0.628 1.9E-06 0.2145 22. A3T 140 0.942 2.35E-05 0.1923 77. ATC1(36)T 60 0.942 2.89E-06 0.2134 23. A3T 140 1.257 6.31E-05 0.1851 78. ATC1(36)T 60 1.257 1.21E-05 0.2119 24. A3T 140 1.571 1.98 E-04 0.1777 79. ATC1(36)T 60 1.571 4.82E-05 0.21 25. A3T 140 1.885 1.96 E-04 0.166 80. ATC1(36)T 60 1.885 6.94E-05 0.2086 26. ATC1(6)T 60 0.628 2.24E-06 0.2195 81. ATC1(36)T 80 0.628 3.21E-06 0.2088 27. ATC1(6)T 60 0.942 3.34E-06 0.2143 82. ATC1(36)T 80 0.942 4.87E-06 0.2042 28. ATC1(6)T 60 1.257 1.54E-05 0.2094 83. ATC1(36)T 80 1.257 1.62E-05 0.1995 29. ATC1(6)T 60 1.571 6.28E-05 0.2016 84. ATC1(36)T 80 1.571 6.7E-05 0.1987 30. ATC1(6)T 60 1.885 7.79E-05 0.1966 85. ATC1(36)T 80 1.885 8.99E-05 0.1955 31. ATC1(6)T 80 0.628 3.8E-06 0.2131 86. ATC1(36)T 100 0.628 5.2E-06 0.204 32. ATC1(6)T 80 0.942 5.12E-06 0.2083 87. ATC1(36)T 100 0.942 6.9E-06 0.2002 33. ATC1(6)T 80 1.257 2.19E-05 0.2026 88. ATC1(36)T 100 1.257 2.47E-05 0.1964 34. ATC1(6)T 80 1.571 7.34E-05 0.1978 89. ATC1(36)T 100 1.571 8.36E-05 0.1904 35. ATC1(6)T 80 1.885 8.96E-05 0.1916 90. ATC1(36)T 100 1.885 0.000103 0.1891 36. ATC1(6)T 100 0.628 6.35E-06 0.209 91. ATC1(36)T 120 0.628 7.93E-06 0.1943 37. ATC1(6)T 100 0.942 8.3E-06 0.2046 92. ATC1(36)T 120 0.942 1.15E-05 0.1897 38. ATC1(6)T 100 1.257 2.93E-05 0.1984 93. ATC1(36)T 120 1.257 3.2 E-05 0.1867 39. ATC1(6)T 100 1.571 9.72E-05 0.1919 94. ATC1(36)T 120 1.571 1.15 E-04 0.1822 40. ATC1(6)T 100 1.885 1.01 E-04 0.186 95. ATC1(36)T 120 1.885 1.23 E-04 0.179 41. ATC1(6)T 120 0.628 1.05E-05 0.201 96. ATC1(36)T 140 0.628 1.47E-05 0.1895 42. ATC1(6)T 120 0.942 1.32E-05 0.1975 97. ATC1(36)T 140 0.942 1.66E-05 0.183 43. ATC1(6)T 120 1.257 4.03E-05 0.1923 98. ATC1(36)T 140 1.257 4.34E-05 0.179 44. ATC1(6)T 120 1.571 1.38 E-04 0.1874 99. ATC1(36)T 140 1.571 1.44 E-04 0.1742 45. ATC1(6)T 120 1.885 1.33 E-04 0.1795 100. ATC1(36)T 140 1.885 1.46 E-04 0.1712 46. ATC1(6)T 140 0.628 1.74E-05 0.1953 101. ATC2(6)T 60 0.628 1.78E-06 0.1813 47. ATC1(6)T 140 0.942 2.06E-05 0.1912 102. ATC2(6)T 60 0.942 2.72E-06 0.1802 48. ATC1(6)T 140 1.257 5.84E-05 0.1863 103. ATC2(6)T 60 1.257 1.19E-05 0.1795 49. ATC1(6)T 140 1.571 1.55 E-04 0.1817 104. ATC2(6)T 60 1.571 4.53E-05 0.1786 50. ATC1(6)T 140 1.885 1.62 E-04 0.1759 105. ATC2(6)T 60 1.885 6.88E-05 0.178 51. ATC1(21)T 60 0.628 1.98E-06 0.2174 106. ATC2(6)T 80 0.628 2.95E-06 0.1794 52. ATC1(21)T 60 0.942 2.9E-06 0.2104 107. ATC2(6)T 80 0.942 4.15E-06 0.1788 53. ATC1(21)T 60 1.257 1.3E-05 0.2014 108. ATC2(6)T 80 1.257 1.51E-05 0.1782 54. ATC1(21)T 60 1.571 4.92E-05 0.1947 109. ATC2(6)T 80 1.571 5.99E-05 0.1774 55. ATC1(21)T 60 1.885 6.6 E-05 0.1866 110. ATC2(6)T 80 1.885 8.31E-05 0.1765

(Contd.)


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2.4 Hardness Samples apparent hardness measured with digital

micro Vicker hardness tester, following ASTM E384-08a32 standards. Indentation load applied in hardness test was 1000 gf (9.8 N) with a dwell time of 15 s.

Samples bulk hardness measured following ASTM standard E18-08b33. Tests were performed with digital Rockwell hardness tester, using diamond spheroconical indenter 120°, applied load 150 Kg and dwell time of 5 s. To measure the average value of hardness five hardness readings were taken at different points.

3 Results and Discussion

3.1 Hardness Figure 2 presents the microhardness value and bulk

hardness of conventionally and cryogenically treated samples. The ATC2(6)T2 treated sample has higher

micro- hardness, which is around 3.2% higher than the conventional treated sample. But in case of 36 h soaking time micro-hardness decreases in comparison to conventional treatment, the observed downfall is 2.2 and 1.6% for ATC1(36)T2 and ATC2(36)T2.

In the case of Rockwell hardness (bulk), ATC2(6)T2 treated sample has higher bulk hardness number, which is around 2% higher than the conventionally treated sample. But in the case of 36 h soaking time there is a decrease in the Rockwell hardness as compared to the conventional treatment and decrease is approximately 1.2% in case C1 treatment and C2 treatment. Koneshlou et al.13 and Amini et al.24 concluded that increase in the micro-hardness is owing to homogeneous and higher degree distribution of carbide with elimination of retained austenite. In our investigation we also found that there is increase in the micro- hardness up to the soaking

Table 2 — Experimental variables with their different levels of input parameters and results for chosen response parameters. — (Contd.)

S. No.

Type of Treatment

Load (N)


Wear Rate (gm/m)


Sl. No.

Type of Treatment

Load (N)


Wear Rate (gm/m)


111. ATC2(6)T 100 0.628 5.03E-06 0.1773 144. ATC2(21)T 120 1.571 7.98E-05 0.1733 112. ATC2(6)T 100 0.942 6.66E-06 0.1768 145. ATC2(21)T 120 1.885 9.41E-05 0.1729 113. ATC2(6)T 100 1.257 2.02E-05 0.1763 146. ATC2(21)T 140 0.628 9.29E-06 0.1742 114. ATC2(6)T 100 1.571 8.11E-05 0.1757 147. ATC2(21)T 140 0.942 1.26E-05 0.1739 115. ATC2(6)T 100 1.885 9.41E-05 0.175 148. ATC2(21)T 140 1.257 3.32E-05 0.173 116. ATC2(6)T 120 0.628 7.03E-06 0.1756 149. ATC2(21)T 140 1.571 1.0 E-04 0.1721 117. ATC2(6)T 120 0.942 1.01E-05 0.1751 150. ATC2(21)T 140 1.885 1.0E-04 0.1714 118. ATC2(6)T 120 1.257 2.81E-05 0.1747 151. ATC2(36)T 60 0.628 2.14E-06 0.1823 119. ATC2(6)T 120 1.571 1.01 E-04 0.1741 152. ATC2(36)T 60 0.942 3.55E-06 0.1799 120. ATC2(6)T 120 1.885 9.9E-05 0.1732 153. ATC2(36)T 60 1.257 1.52E-05 0.1782 121. ATC2(6)T 140 0.628 1.19E-05 0.1735 154. ATC2(36)T 60 1.571 5.99E-05 0.1772 122. ATC2(6)T 140 0.942 1.55E-05 0.1729 155. ATC2(36)T 60 1.885 7.35E-05 0.1765 123. ATC2(6)T 140 1.257 4E-05 0.172 156. ATC2(36)T 80 0.628 3.5E-06 0.1803 124. ATC2(6)T 140 1.571 1.12 E-04 0.1713 157. ATC2(36)T 80 0.942 5.76E-06 0.1784 125. ATC2(6)T 140 1.885 1.2 E-04 0.1705 158. ATC2(36)T 80 1.257 1.93E-05 0.177 126. ATC2(21)T 60 0.628 1.33E-06 0.1815 159. ATC2(36)T 80 1.571 7.92E-05 0.1761 127. ATC2(21)T 60 0.942 2.34E-06 0.1797 160. ATC2(36)T 80 1.885 1.0 E-04 0.1752 128. ATC2(21)T 60 1.257 9.11E-06 0.1783 161. ATC2(36)T 100 0.628 6.02E-06 0.1792 129. ATC2(21)T 60 1.571 3.84E-05 0.1772 162. ATC2(36)T 100 0.942 7.3E-06 0.1766 130. ATC2(21)T 60 1.885 5.14E-05 0.1763 163. ATC2(36)T 100 1.257 2.94E-05 0.1757 131. ATC2(21)T 80 0.628 2.4E-06 0.1797 164. ATC2(36)T 100 1.571 9.6E-05 0.1749 132. ATC2(21)T 80 0.942 3.46E-06 0.1782 165. ATC2(36)T 100 1.885 1.25 E-04 0.1741 133. ATC2(21)T 80 1.257 1.35E-05 0.1773 166. ATC2(36)T 120 0.628 8.9E-06 0.1764 134. ATC2(21)T 80 1.571 4.74E-05 0.1765 167. ATC2(36)T 120 0.942 1.29E-05 0.1752 135. ATC2(21)T 80 1.885 6.7E-05 0.1757 168. ATC2(36)T 120 1.257 3.73E-05 0.1743 136. ATC2(21)T 100 0.628 4.03E-06 0.1779 169. ATC2(36)T 120 1.571 1.35 E-04 0.1737 137. ATC2(21)T 100 0.942 5.4E-06 0.1769 170. ATC2(36)T 120 1.885 1.37 E-04 0.1725 138. ATC2(21)T 100 1.257 1.88E-05 0.1761 171. ATC2(36)T 140 0.628 1.74E-05 0.1748 139. ATC2(21)T 100 1.571 6.08E-05 0.1755 172. ATC2(36)T 140 0.942 2.01E-05 0.1739 140. ATC2(21)T 100 1.885 8.04E-05 0.1746 173. ATC2(36)T 140 1.257 5.09E-05 0.173 141. ATC2(21)T 120 0.628 6.02E-06 0.176 174. ATC2(36)T 140 1.571 1.73 E-04 0.1722 142. ATC2(21)T 120 0.942 7.04E-06 0.1754 175. ATC2(36)T 140 1.885 1.85 E-04 0.1715 143. ATC2(21)T 120 1.257 2.29E-05 0.1744


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time of 21 h in case of C1 treatment and 6 h in case of C2 treatments.

3.2 Predictive modeling of “WR” and “μa” Table 2 shows the chosen response parameters for

all the possible combinations of all levels and for all the factors as per full factorial design with response values for “WR” and “μa”. Response of “WR” to input variables varies from min. value 1.6910−5 to 1.48 10−4 whereas response of “μa” in the range of 0.17 to 0.22 with selected input variables.

Transformation of response data utilized to stabilize the variance and improve the normality of the process. The Box-Cox method determines the power transformation of the dependent variable34. Hence, Design-Expert 7.1 software utilized to get a Box-Cox plot for WR and μa (Fig. 3 and Fig. 4). In

Fig. 3 and Fig. 4 high and low interval limit at 95% confidence level for transformation (λ) shown by the point where U-shaped curve intersected by the short solid horizontal pink lines.

In case of “WR” the best “λ” value is in the limit of -0.06 to -0.02 with 95% confidence level and λ =1 not included in this interval (Fig. 3), this support the need for transformation. In this case software advised the log transformation (λ =0).

In Fig. 3 small vertical blue line at λ =0 specifies the existing transformation and is reasonably close to software advised λ = -0.02. Similarly, in Fig. 4 small vertical blue line at λ =1 shows present λ for “μa” and fairly near to the software advised λ = -1.31 with 95% confidence level the preeminent value of transformation (λ).

The model selected for investigation based upon the “R2” values. “R2” is the correlation coefficient and represent how close the data are to the fitted regression line. “R2” close to 1, represents that data fits the well in a regression line35-36. In case of “WR” the suggested model is cubic and adjusted “R2” = 0.9983 and predicted “R2” = 0.9977 at p-level less than 0.0001. Whereas, in case of “μa” the suggested model is linear and adjusted “R2” = 0.9973 and predicted “R2” = 0.9949 at p-level less than 0.0001. It means or indicates that 99% of total variation in results can be explained by linear relationship between experimental and predicted results in both cases (i.e., “WR” and “μa” )

A statistical analysis of variance (ANOVA) was done on the developed models for and to find out the

Fig. 2 — Hardness trend of conventionally and cryogenicallytreated samples.

Fig. 3 — Box-Cox plot for WR.

Fig. 4 — Box-Cox plot for μa.


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effects of factor on response parameters. To obtain a more desirous models for “WR” and “μa” all insignificant terms are eliminated. Significant terms of ANOVA analysis and coefficient of regression analysis for “WR” and “μa” are shown in Table 3 and Table 4.

In the case modified models for “WR” (Table 3), F value with 11 degrees of freedom improves to 8048.69 and its small probability value (Prob>F<0.050) proves that the model is significant for “WR”. The model terms having values > 0.1000 are significant model terms35. In the modified model the predicted “R2” = 0.9979 is in conformity with the adjusted “R2” = 0.9980 and adequate precision (AP) of 318.13. “AP” of reduced model is superior in comparison to full cubic model. The lack of fit is statistically significant as the values are less than 0.05 which means that model for “WR” is statistically significant at 95% confidence level. Hence, regression equations evolved with this model 2-8 can predict “WR”

A3T Log10(WR)=(-3.85369+0.014198*B-8.29029*C-5.47358E-003*B*C+8.66062*C2-2.32010*C3) … (2)

ATC1(6)T Log10(WR)=(-3.92496+0.014198*B-8.29029*C-5.47358E-003*B*C+8.66062*C2-2.32010*C3) … (3)

ATC1(21)T Log10(WR)=(-3.98425+0.014198*B-8.29029*C-5.47358E-003*B*C+8.66062*C2-2.32010*C3) ... (4)





Whereas, in case of “μa” After elimination of insignificant terms (Table 4) the model F-value with 41 degrees of freedom improved to 1143.37 and its small probability value (Prob>F<0.050) proves that the model is significant for “μa”. In the modified model of “μa” predicted “R2” = 0.9951 is in

conformity with the adjusted “R2” =0.9963 and AP of 166.62. “AP” of reduced model is superior in comparison to full cubic model. The lack of fit is statistically significant as the values are less than 0.05 which means that model for “μa” is statistically

Table 3 — Summary of ANOVA and regression analysis for WR

with transformation.

Source Sum of Squares

DF Mean Square

F Value p-value Prob > F

Model 58.58 11 5.33 8048.69 < 0.0001 A-Type of Treatment

1.18 6 0.2 297.88 < 0.0001

B-Load 7.5 1 7.5 11335.28 < 0.0001 C-Sliding Velocity

46.42 1 46.42 70158.64 < 0.0001

BC 0.83 1 0.83 1252.34 < 0.0001 C2 0.035 1 0.035 52.82 < 0.0001 C3 2.61 1 2.61 3949.29 < 0.0001 Residual 0.11 163 6.62E-04 Cor Total 58.69 174

Reduced cubic model Full cubic model Coefficient of regression Value Value

Std. Dev. 0.026 0.024 Mean -4.61 -4.61 C.V. % 0.56 0.52 PRESS 0.12 0.14 “R2” 0.9982 0.9987 Adj “R2” 0.998 0.9983 Pred “R2” 0.9979 0.9977 AP 318.135 175.661

Table 4 — Summary of ANOVA and regression analysis for μa with transformation.

Source Sum of Squares

DF Mean Square

F Value p-value Prob > F

Model 0.037 41 9.03E-04 1143.37 < 0.0001 A-Type of Treatment

0.017 6 2.88E-03 3649.16 < 0.0001

B-Load 8.86E-03 1 8.86E-03 11220.4 < 0.0001 C-Sliding Velocity

4.73E-03 1 4.73E-03 5985.64 < 0.0001

AB 3.47E-03 6 5.78E-04 731.69 < 0.0001 AC 2.52E-03 6 4.21E-04 532.43 < 0.0001 ABC 1.44E-04 6 2.41E-05 30.45 < 0.0001 AB2 3.69E-05 6 6.15E-06 7.79 < 0.0001 AC2 3.64E-05 6 6.07E-06 7.69 < 0.0001 Residual 1.04E-04 132 7.90E-07 Cor Total 0.037 173

Reduced cubic model Full cubic model Coefficient of regression Value Value Std. Dev. 8.89E-04 8.82E-04 Mean 0.19 0.19 C.V. % 0.48 0.47 PRESS 1.83E-04 1.90E-04 “R2” 0.9972 0.9973 Adj “R2” 0.9963 0.9964 Pred “R2” 0.9951 0.9949 AP 166.617 160.339


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significant at 95% confidence level. Hence, regression equations evolved with this model 9-15 can predict “μa”

A3T (μa)1= (+0.27701-7.34351E-004*B-7.72934E-003*C-2.60957E-005*B*C+1.78571E-006*B2-5.84984E-003*C2) … (9)

ATC1(6)T (μa)1 = (+0.2442-2.67138E-004*B-0.013575*C +3.30903E-005*B*C-2.60714E-007*B2-2.80503E-003 * C2) … (10)

ATC1(21)T (μa)1 = (+0.28003-7.14067E-004*B-0.042462*C+ 1.55439E-004*B*C+3.85714E-007*B2+2.77803E- 003*C2) … (11)

ATC1(36)T (μa)1 = (+0.24027-3.29625E-004*B-3.74090E-003* C -1.05635E-004*B*C+3.06776E-007*B2 +1.37743E-003 *C2) … (12)

ATC2(6)T (μa)1 = (+0.18510-2.90258E-005*B-1.98673E-003* C+4.13597E-006*B * C-3.42857E-007*B2-2.47592E-004*C2) … (13)

ATC2(21)T (μa)1 = (+0.18798-5.27031E-005*B-6.18622E-003*C+1.97269E-005*B*C-2.21429E-007*B2+ 5.04296E-004*C2) … (14)

ATC2(36)T (μa)1 = (+0.19259-9.48323E-005*B-0.010031*C+ 2.41838E-005*B*C-3.92857E-008* B2 +1.60352E-003* C2) … (15)

Further analysis of model validation for “WR” and “μa” performed with normal plots of residuals. Figure 5 presents the normal plots of residuals for WR and Fig. 6 for “μa”. Both the figures show that responses followed the almost straight line and are normally distributed. This signifies that developed models are adequate.

Figure 7 (a, b) demonstrates the analysis of the experimental run order with internally studentized residuals for “WR” and “μa”. In both the cases the responses are well fitted in the model within ± 3 standard deviation with 95% of confidence level.

This also confirms that process is stable and provides evidence for the adequacies of the developed model with the demonstration that model fulfill the postulation for the analysis of variance.

Furthermore, the plots of residual for experimental versus the response predicted in case of “WR” and

“μa” are plotted to find out outlier values (Fig. 8 and Fig. 9).

These figures show that all the data points for “WR” and “μa” are well spotted near the 45° reference line and no outlier response values present which prove the sufficiency of model above the series of data34,36.

3.3 Error analysis for developed mathematical predictive model of WR and μa at dry sliding condition

The experimental values of “WR” and “μa” of varied “CP” samples compared with values obtained from the developed mathematical model equations for particuler cryogenic treatment cycle (for WR Eqs (2-

Fig. 5 — Normal plots of residuals for WR.

Fig. 6 — Normal plot of residuals for μa.


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8); for μa Eqs (9-15) at varying sliding speeds and loads. The percentage maximum error (%ME) were calculated using Eq. (16) for the purpose of validation and confirmation of the developed mathematical model equations for WR and μa .

% ME = 100*

p

pe … (16)

here “e” represents the experimental response and “p” represents values obtained from the developed mathematical model equations.

The maximum error ovserved in case of “WR” is of 9.9 % and in the case of “μa” the observed maximum

error was 1%. This also validate the model equations acquired from this analysis for the “WR” and “μa”.

3.4 Effects of different “CT” on the “WR” and “μa”

3.4.1 Wear rate “WR” Figure 10 (a, b) shows the interaction plots for the

change in mean value of “WR” of AISI-H11 steel subjected to different CT (soaking temperature: -154 °C and -184 °C; soaking duration : 6, 21, and 36 h) with change in sliding velocity (0.628-1.885 m/s) at various loads (60-140 N). The “WR” depends on the sliding speed, load and microstructure of the material. Figure 9(a) depicts that “WR” of samples enhance with an increase in applied operating normal load at

Fig. 7 — Plots for residuals distribution over the experimentation runs (a) WR and (b) μa.


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all values of sliding speed. It can be explained that with an increase in load the contact pressure increases at the line of contact between block and roller due to which rubbing action between the block and roller

increases causing a heavy force and more friction between them which results in higher “WR”.

Figure 10 (b) shows that at lower sliding velocity 0.628 -1.257 m/s and all type of operating normal

Fig. 10 — Interaction plots of WR with (a) load and (b) sliding speed.

Fig. 8 — Predicted versus actual value for WR.

Fig. 9 — Predicted versus actual values for μa.


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load, “WR” of all type of treated samples goes up slowly, whereas, at sliding velocity more than 1.257 m/s at same set of operating normal loads, “WR” sharply increases up to 1.571 m/s sliding velocity. “WR” show downwards trends for all type of samples, at same operating set of operating conditions for sliding velocity more than 1.6 m/s. Increase in the wear resistance is observed up to the soaking time of 21 h in both C1 and C2 treatment. But in case of 36 h soaking time there is decrease in the wear resistance. Extended soaking period during the cryogenic treatment causes the decomposition of martensite thus reduces its tetragonality and hardness8,37. In this study also apparent and bulk hardness number of samples soaked for longer period shows downward trend in the case of C1 and C2 treatment. Amini et al.24 in tool steel 80CrMo12 5 and Gunes et al.28 in AISI 52100 bearing steel also reported the decrease in wear

resistance at longer soaking periods at the DCT temperature.

This study reveals that increase in the wear resistance observed up to the soaking time of 21 h in both C1 and C2 treatment. But in case of 36 h soaking time there is decrease in the wear resistance. The wear resistance of CT samples is higher than the A3T treated samples specimens at all levels of choosen operating set of conditions for the experiment.

3.4.2 Analysis of wear debris AISI H11 steel at dry sliding condition

After the wear experiments morphology study of the worn surfaces of the samples which exposed to minimum applied load (60 N) and maximum load ( 140 N) carried out at mid range of sliding velocity (1.571 m/s) for all type of the treatment. Figure 11 illustrates that worn surfaces of the samples have the fractured ridges, cracks, deformation lips and wear

Fig. 11 — Micrograph of worn sample surfaces generated under operating wear test parameters. Respective sample wear debris shown in insets.


123

debris in the shape of metallic plates. The debris metal plates size varies with the type of CT and applied load even at the same sliding speed. The debris metal plates size in for A3T samples larger in comparison to the CT samples. As the applied load increases the intensity of the fractured ridges, deformation lips and surface groves also amplified (shown in the right hand side images of the Fig. 11) The samples CT for holding time of 36 h have the sub-surfaced cracks, fractured ridges and deformation lips and these are the indication of delamination wear38. Samples cryogenically treated beyond 21 h soaking time (i.e. 36 h) shows the reduction in the wear resistance and hardness value (Fig. 2) in comparison to the other applied treatments in this study. Similar trends in reduction in wear resistance and mechanical properties for tool steel which hold for longer soak periods at cryogenic temperature reported in the previous study24,28,39. The main cause reported for this is the coarsening of martensitic matrix; agglomeration of carbide particles and distribution of carbides out of the martensite39.

3.4.3 Average coefficient of friction (μa) Figure 12 (a, b) shows the interaction plots for

the change in mean value of “μa”. The magnitude of

“μa” decreases with the increase in value of load and sliding velocity. At lower load fluctuation of the “μa” evidenced possibly due to low contact stress (1.48MPa) in comparison to higher load (3.47 MPa). “μa” of the cryogenically treated samples are significantly lower than the conventionally heat treated samples while sliding velocities are less than 1.6 m/s for all level of tested normal loads except load 140 N, whereas C1 treated samples for soaking period of 6 hours shows higher “μa” values than the conventionally heat treated samples after the sliding velocities of 1.2 m/s. Prieto et al.19 also reported in case of martensitic stainless steel that CT samples have the lower friction coefficient

3.4.4 Optimization RSM approach based on desirability function

employed in the present work to find out optimum CT process parameters to achieve lower values of “WR” and “μa”.

The individual responses for “WR” and “μa” within the the selected range operating conditions are modeled with different equation. Model for “WR” was natural log and the model of “μa” is linear. The goals of each input process parameter (i.e, applied normal load, sliding velocity chosen to be maximum and type of treatment which is categorial factor chosen as within range). Measured response “WR” and “μa” selected as minimize. Table 5 presented the criterian which is used to get highest desirability function for optimized processing parameter for the minimum “WR” and “μa”. The default value of “1” of

Fig.12 — Interaction plots of μa with (a) load and (b) slidingspeed.

Table 5 — Constraints for determining the optimum value tominimize WR and μa.

Parameter Goal Limits Weight ImportanceType of Treatment (A)

Is in range A3T≤ A ≤ ATC(36)T

1 3

B, N Maximize 60≤ B ≤ 140 1 3 C, m/s Maximize 0.628≤ C ≤ 1.885 1 3 WR, gm/m Minimize 1.3269 X10 -006 ≤

D ≤ 0.000198412 1 5

μa, Minimize 0.1580≤ E ≤ 0.2310

1 3

Table 6 — Optimization result for minimizing “WR” and “μa”.

S. No.

Type of Treatment, (A)

B, N C, m/s

WR , gm/m

μa Desirability

1 ATC2(21)T 140 1.06 1.71E-05 0.173 0.58 2 ATC2(21)T 140 1.06 1.69E-05 0.173 0.57 3 ATC2(6)T 140 1.05 1.98E-05 0.172 0.56

INDIAN J ENG MATER SCI, APRIL 2019 124

the weight is assigned to the variables to achieve the desirability function by giving equal weight to all goals34. For wear rate importance value “5” is assigned to keep wear rate as minimum as possible and for rest of variables importance value is considered as “3”. Table 6 shows the obtained 03 optimal solutions with highest desirability for lowest value of “WR” and “μa” within the specific designed space constraints.

A detail of desirability which is achieved for each factor and each response is represented as the ramp function graphs in Fig. 13. The spot on each ramp profile reveals the factor setting of that input variable and response prediction characteristics for the individual output response variable. The position of spot which is in terms of height for all the numeric variable shows the optimum value of that variable and also indicate the desirability level of that variable. The ramp graph of treatment type is shown by horizontal straight line. This indicates that it is categorical factor / variable. The divisions of this horizontal line are equal to the number of levels of the variable (treatment type), which is taken in range as indicated above (Table 6). The optimum level of the categorical factor is shown by position 6, which is ATC2(21)T treatment type.

Further, conformity tests were performed to verify the predicted value with the experimental response. Table 7 shows the experimental response values and the predicted responses from the mathematical model. This table also shows that the deviation among the predicted results and experimental values. The deviation is of the order of 4-5% for “WR” and “μa” within choosen set of operating parameter.

Contour plots of each of the combinations are shown in Fig. 14 for better understanding of the behavior of desirability with change in input variables. The most favorable region in this plot is the region where maximum value of the predicted desirability (i.e., 0.587) was positioned at right side of Fig. 14 and nearer to larger values of load and middle value of sliding velocity. This region has the overall

Table 7 — Result of conformity testing for tribological parameters.

Test No.

Process parameter Response parameter (Predicted Value)

Response parameter (Experimental Value)

%Error

Treatment type B (N)

C (m/s)

WR (gm/m)

μa WR gm/m

μa WR gm/m

μa

1 ATC2(21)T 140 1.06 1.71E-05 0.173 1.80E-05 0.18 5.2 4.0 2 ATC2(6)T 140 1.06 1.69E-05 0.173 1.78E-05 0.182 5.3 5.2 3 ATC2(6)T 140 1.05 1.98E-05 0.172 1.84E-05 0.181 -7.07 5.23

Fig. 13 — Ramp function graphs of desirability optimization.

Fig. 14 — Desirability plot.

greatest desirability of 0.587 at the right-hand side of plot.

4 Conclusions The main conclusions drawn from this

experimental work are: (i) The developed model utilizing Box-Cox

transformation for the modeling and evaluation of differently CT AISI-H11 has adequate precision and the entire points are in good agreement with model and within ± 3 standard deviation. Adequacy of developed model within confidence level of 95%.


125

(ii) Model predictive value has maximum an error of 9.9% and 1% with experimental value for “WR” and “μa”.

(iii) Influence of sliding velocity on wear rate is predominantly high in comparison to load in the chosen set of operating parameters (sliding velocity : 0.628-1.885 m/s and normal load: 60-140 N).

(iv) CT improves the wear resistance of AISI-H11 steel in comparison to A3T. At -184 °C CT for holding time of 21 hours maximum wear resistance evidenced.

(v) Soaking period during the cryogenic treatment most influential factor for the wear resistance and hardness.

(vi) The “μa” of the CT samples are lower than the “A3T” samples up to the sliding velocities 1.2 m/s for all level of tested normal loads.

(vii) The average co-efficient of friction values of samples ATC1 (21) T are significantly lower in comparison to “A3T” treated samples irrespective of load and velocity.

(viii) Optimization approach using desirability function divulge that optimal process parameters to have maximum wear resistance and lower “μa” for CT AISI-H11 steel is holding time of 21 hours at temperature -184 °C under maximum operating load of 140 N and the sliding velocity of 1.06 m/s in dry sliding.

(ix) The deviation between the predicted and experimental results are of the order of 5%, and 4% for “WR” and “μa” for the selected optimized CT cycle (ATC2(21)T).

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