Investigation on the effect of machining parameters on 42CrMo4 DPS steels

Abstract

Machining 42CrMo4 martensite—ferrite dual phase steel is challenging due to its high hardness and it is essential to determine the favorable requirements for the optimum machining condition. The ability to alter the martensite quantity in dual phase structure of steel leading to variation in bulk hardness is the motivation for machinability investigation. In heavy duty machining, the determination of tool life and surface roughness at various conditions of machining plays an important role in the manufacturing industry. In the present work, machinability tests are carried out on 42CrMo4 martensite—ferrite dual phase steel to assess the tool life and surface roughness. Speed, feed, and depth of cut are varied in different levels. The tests are carried out as per the full factorial method. A microstructure study is performed to correlate the various mechanical properties with phase morphology. The main objective of this study is to obtain the optimized machining process parameters for the turning operation of 42CrMo4 martensite—ferrite dual phase steel. All cutting tests are carried out under dry conditions using a carbide insert. Microstructure and mechanical property analysis shows an increase in the martensite quantity with the increase in the dual phase processing temperature. From the ANOVA results, it is found that the depth of cut is the major contributing factor to the variation of tool life and surface roughness within the range of values considered for the study. Microstructure analysis revealed the distribution of ferrite and martensite phases evenly. The optimum combination of machining parameters is calculated for obtaining a superior combination of tool life and surface roughness. The 42CrMo4 DPS steel may be used for structural applications with wide variation in the property range.

Keywords:

• ferrite
• bainite
• martensite
• dual phase
• machining parameters
• optimization
• microstructure

1. Introduction

The primary objective of any engineering production unit is to either increase the production rate or decrease production costs. To achieve this goal, high speed machining is very much relevant. High speed machining without compromising the quality of the product is solely dependent on the machinability of the workpiece. Machinability is a complicated aspect of machining and involves many criteria and processes. Even when the same material is used, different machining response is obtained for a different set of machining operations. To improve machinability, it is desired to have high material removal rate with lower forces.

Even slight variations in the composition of the steel and alterations in phases can have a drastic effect on its properties. Alterations in phases or property modification are possible by different heat treatment techniques. Steel is such a versatile material wherein minor composition change contributes a lot to its microstructure and formation/distribution of phases and therefore its properties can be tailored to a wide extent. Steel may be used from as simple as paperclips to load bearing members for bridge construction, cutting tools, dies, and large beams for columns and skyscrapers and many other applications.

Generally, plain carbon steel has two basic micro constituents at room temperature, which are cementite and ferrite as equilibrium phases. The property of steel is primarily controlled by number, type and wt. % of individual phases present in it (Alaneme et al., 2010; Ebrahimian & Ghasemi Banadkouki, 2017; Gurumurthy et al., 2020). The heat treatment process controls this change in phase and reliable proportion too. The particular grade of steel may be highly ductile so that easy mechanical working is possible to change its shape and size or it may be very hard and tough like cutting tools and dies. This change in phase is possible by tailoring the type and relative wt. % of phases present.

Dual phase medium carbon steel may be obtained by carrying out suitable heat treatment by controlling the process parameters. In ferrite phase, martensite is incorporated in dual phase steels. As-cast plain carbon steel typically has two potential phases: pearlite and proeutectoid ferrite. The eutectoid mixture known as pearlite is composed of cementite and ferrite in a lamellar configuration. If process parameters are properly designed, the unlikely possibility of martensite with a proeutectoid ferrite phase is attainable. At high temperatures, this amount of carbon permits ferrite to transition into austenite, which is then converted to martensite during cooling, resulting in a harder alloy (Senthil Kumar et al., 2006; Silva, P.R, 2010; Das and Chattopadhyay, 2009). Super martensitic stainless steels are based on ancient martensitic stainless-steel grades like low and medium carbon (Farrar, 2004). By adjusting the heat treatment process parameters, it is possible to regulate the proportion of tougher martensite and softer ferrite phases. The improvement in machinability may be attainable by managing the relative amounts of these phases. Especially, optimization of process parameters can be achieved by using Response Surface Methodology (RSM) technique.

Gurumurthy et al. (Cao et al., 2015) have investigated the effect of intercritical annealing treatment on the medium carbon DPS and its mechanical properties are tested. The material was heated at different intercritical temperature ranges from 770, 780 and 790°C. As the DPS temperature increased, tensile and hardness values were increased but the impact result was decreased. Microstructure reveals the quantity of ferrite and martensite content.

Sharma et al. (Li et al., 2015) experimented on medium carbon low alloy steel under different dual phase treatments. The material was heated at different intercritical temperature ranges from 770, 780 and 790 °C, and investigated the tensile, hardness, and impact strength of the DPS. As the austempering temperature increased, the presence of the martensite phase increased. The F-B DPS was characterized by a high strength and low yield ratio. The results suggested that long holding periods at intercritical temperatures of 790 °C condition yielded the better tensile and hardness value compared to the other two temperatures.

J. Min, et al. (Min et al., 2012) discussed the isothermal deformation on DPS. Isothermal UTS test and microstructure analysis were carried out for deformed ferrite bainite steel and also investigated different strain effects at deformation temperature on dual phase zone. As the degree of deformation increases, bainite nucleation sites switch from austenite grain borders to austenite grains themselves. The driving force for DPS transformation increases with a decrease in incubation time and a rise in deformation density.

Panel et al. (Kumar & Patel, 2017) studied the machinability process parameters under dry condition machining of AISI 4140 steel. ANOVA method is used for the analysis of flank wear and surface roughness of AISI 4140 steel using tungsten carbide and ceramic insert tools. Varying the cutting speeds from 150 to 220 m/min has significantly affected the surface roughness and flank wear of the tool. Microanalysis shows that ceramic inserts give better life compared to any other tool used in the study.

Günay et al. (Günay et al., 2020) investigated the carbide cutting tool performance in turning super alloys under different cutting surroundings. Cutting fluid was used for the analysis of tool life, tool wears monitoring and analysis of surface roughness of the machine surface. Nickel based super alloy was used in this study under different environmental conditions. Response surface methodology was used for the prediction of tool life and surface roughness under different environments. Compared to dry and air-cooling turning methods, oil spray turning methods have resulted in better tool life and surface roughness.

A.B. Kabra et al. (Paul, 2013) concentrated on the optimization of the cutting parameters (CS, feed and DoC) to minimize S_R, feed and radial forces during CNC turning of 42CrMo4 steel in dry conditions. An uncoated carbide tool insert was chosen for this purpose. Various statistical models like an orthogonal array, Signal to noise ratio and ANOVA were used to analyze the performance results. Optimum values of process parameters were obtained using Taguchi’s L9 orthogonal array through MINITAB software. Mathematical regression models were developed to examine the relationship between process parameters and turning parameters. Results revealed that DoC was the most significant factor influencing S_R, feed and radial forces followed by feed and CS standing least.

Few other studies have reported on the selection of various parameters while machining martensitic and hardened stainless steels, but they haven’t looked into tool wear processes. With no examination of tool wear mechanisms, Elmunafi et al. (Asiltürk & Akkuş, 2011) investigated the impact of cutting conditions (cutting speed and feed rate) on tool life, surface roughness, and cutting forces in hard turning of AISI 420 stainless steel (47–48 HRC). PcBN tools were employed by Sobiyi and Sigalas (Elmunafi et al., 2015) and reported about the facing, turning, grooving, and boring of AISI 440B martensitic stainless steel at high cutting speeds (350–500 m/min).

In the dry turning of hardened medium carbon steel employing a TiN coated carbide insert, the effects of machining parameters (CS, feed, and DoC) on machined surface characterization, such as SR, flank wear behavior, and chip morphology were examined. To investigate the impact of cutting parameters on S_R (Ra, Rq, and Rz) and flank wear mechanism, statistical models such as orthogonal array and ANOVA were utilized. Results showed that even while DoC had very little effect on flank wear, CS had the greatest influence on S_R, followed by feed rate. The 95% confidence level mathematical models for SR and flank wear were created using response surface methodology (RSM) (15–18).

Hence, in this study, an effort is made to determine the machinability aspects of 42CrMo4 martensite and ferrite dual phase steel by considering the tool wear and surface roughness. The literature collected shows that there is a gap in relating microstructure with the mechanical property of DPS. The correlation between the tool wear and surface roughness and the deviation between the experimental and theoretical results on these two outputs is shallow. Also, optimum machining parameters are obtained to machine the material with ease, which fills the existing research gap. Relating microstructure and mechanical property, optimization of machining parameters and dual phase temperature is the novelty of the work.

2. Materials and methods

2.1. 42CrMo4 steel

42CrMo4 is one of the most important materials in medium carbon steels. These steels are generally used in industrial and automotive applications. Table 1 shows the chemical composition of steel utilized in the present study.

Table 1. Elemental composition of 42CrMo4 steel

Element	Carbon	Manganese	Silica	Chromium	Molybdenum	Nickel	Iron
Wt.%	0.40	0.63	0.20	0.94	0.21	0.02	Remainder

Figure 1 shows the heat treatment procedure. Initially, all the specimens are heated to a normalized condition in order to get a uniform structure. In the second stage, all the normalized specimens are heated to dual phase conditions to get the ferrite and martensite (Avner, 1974; Gurumurthy et al., 2019).

Figure 1. Dual phase heat treatment.

Machining (finishing) is performed on the processed DPS using a vertical center. Initially, rough turning is performed to remove 1 mm layer of material to facilitate the removal of any scale if it is all performed during heat treatment. Lathe, the contact time of the tool with the workpiece is considered to measure the tool’s life in records. The critical flank wear is taken as the time to arrive the tool life.

Figure 2 shows the carbide tool, it is a double-sided 35° rhombic insert used for super-finishing. The tool controls chip flow at very low feed and depth of cut. It has also got excellent crater wear resistance.

Figure 2. Carbide tool.

2.2. Design of experiments

DOE is the most important and effective manner for analyzing the machining process parameters. It also reduces the economic burden for the researcher. In the present investigation, the full factorial method is used for analyzing the number of experiments to be conducted. Accordingly, the total number of trials is decided based on the formula, L^F i.e., 4 factors and 3 levels of variations are set for each factor. The effect of the variation in these 4 influences on the T_L and S_R. The details of the influencing factors selected are tempering temperature, speed and DoC. These ranges are selected based on the literature survey, tempering Temperature is from 750, 770 and 790°C, speed from 800, 1150 and 1500 m/min and feed from 0.12, 0.15 and 0.18 mm/rev, DoC 0.2, 0.4 and 0.6 mm respectively (Hegde et al., 2022; Krolczyk, G, et al., 2013; Ozler L et al., 2001; Trent et al., 2000, 2014).

3. Result and discussions

3.1. Mechanical properties

Table 2 shows the mechanical properties of 42CrMo4 DPS steel. It can be seen that the mechanical properties of the medium carbon low alloy steel are affected by the alloying element present in the steel. But, in this steel chromium is the major alloying element. Cr is an austenite stabilizer that helps to increase the amount of austenite transformation into martensite as the intercritical temperature increases (Çalik, 2009; Mehrabi et al., 2020; Pan et al., 2021; BM,G et al., 2022). From the results shown in the table, it is seen that, as the intercritical temperature increases, improvement in strength and hardness is observed. However, this has resulted in a decrease in elongation. Compared to normalized conditions, dual phase steel gives better results.

Table 2. Mechanical properties of 42CrMo4 steel at the different heat-treated conditions

Mechanical property	Hardness (HRC)	Tensile strength (N/mm^2)	Elongation (%)
Normalized	33	512	25.9
Ferrite-Martensite at 750°C	50	772	20.1
Ferrite-Martensite at 770°C	53	986	13.6
Ferrite-Martensite at 790°C	58	1022	8.7

3.2. Microstructure analysis of DPS

From Figure 3, it is seen that microstructure reveals the distribution of ferrite and martensite content in DPS concerning processing temperatures. Figure 3 (a) shows the microstructure at a lower level intercritical temperature in which ferrite and martensite distribution is observed. Less amount of martensite formation is observed at this intercritical processing (790° C) temperature (770 and 790° C) compared to the same at other intercritical temperatures. It is evident that the formation of martensite is dependent on processing temperature. Hence higher the processing temperature, the quantity of martensite formed increases as evident from Figure 3(a),(b) and(c), where 790° C DPS shows almost all martensite phase higher the processing temperature, more the austenite formed from the room temperature due to phase formation (29). An equal quantity of martensite forms while quenching austenite becomes the parent phase of martensite is austenite. the intercritical temperatures increases, the quantity of martensite content also increases (Gurumurthy et al., 2018].

1. T_L and S_R of 42CrMo4 F-M DPS

Figure 3. Ferrite-Martensite at (a) 750, (b) 770 (c) 790° C distribution.

The T_L is performed in dry condition i.e., without coolant. During the run, the machining (turning) is temporarily stopped at equal time intervals to measure the tool flank wear (wear land) with the Tool maker’s microscope. The checking process persisted till the critical tool flank wear land is attained. The time lapse to acquire this flank wear is identified and S_R value is noted as per ISO 4287 using profilometer.

Table 3 provides the T_L and S_R F-M DPS at different parameters used in the machinability test. By using Minitab software cutting conditions effects of CS, F_R and DoC on T_L and S_R of ferrite and martensite dual phase structure are analyzed.

Table 3. Experimental values of T_L and S_R of 42CRMO4 F-M DPS with the process parameters The temperature in °C, speed in m/min. feed in mm/rev and T_L in seconds and S_R in µm

Sl No	T	CS	FR	DoC	TL	SR
1	750	80	0.13	0.2	2180	4.35
2	750	80	0.13	0.2	2184	4.44
3	750	80	0.13	0.2	2207	4.34
4	750	115	0.15	0.4	1399	3.56
5	750	115	0.15	0.4	1398	3.59
6	750	115	0.15	0.4	1399	3.60
7	750	150	0.18	0.6	619	2.90
8	750	150	0.18	0.6	612	2.89
9	750	150	0.18	0.6	634	2.86
10	770	80	0.15	0.6	935	2.98
11	770	80	0.15	0.6	930	2.99
12	770	80	0.15	0.6	928	3.01
13	770	115	0.18	0.2	1835	3.46
14	770	115	0.18	0.2	1838	3.40
15	770	115	0.18	0.2	1825	3.41
16	770	150	0.13	0.4	1357	3.21
17	770	150	0.13	0.4	1356	3.22
18	770	150	0.13	0.4	1349	3.23
19	790	80	0.18	0.4	1321	2.71
20	790	80	0.18	0.4	1312	2.75
21	790	80	0.18	0.4	1322	2.69
22	790	115	0.13	0.6	632	2.75
23	790	115	0.13	0.6	630	2.77
24	790	115	0.13	0.6	623	2.71
25	790	150	0.15	0.2	1487	3.24
26	790	150	0.15	0.2	1485	3.23
27	790	150	0.15	0.2	1490	3.24

3.3. Statistical analysis

Initial screening exercising carried out in ANOVA technique is done for all four factors with their interactions, and it is formed that for T_L and S_R, linear terms contributed greater than 98 percent. To determine the relative impact of the factors on T_L and S_R, this method is used at 5% level of significance using only the linear terms.

The ANOVA results for T_L and S_R of the 42CrMO4 F-M DPS are presented in Tables 4 and 5 respectively. The SEM micrographs of dual phase treated steels revealed that martensite and ferrite make up the majority of the microstructure. The quantity of martensite is different in the three intercritical temperatures. The ferrite stabilizer (Cr) helps to increase the quantity of martensite in dual phase structure. At higher intercritical temperatures (790 °C), the quantity of martensite is more which leads to lesser T_L due to higher hardness and strength. Based on its mechanical properties and alloying elements effect, it is seen that DoC is having more effect with 85.08 and 44.71% for T_L and S_R respectively followed by temperature having 36.12% contribution to S_R and 5.45% on T_L. But CS has less effect (3.41%) on S_R and similarly for the T_L, is 8.13%. F_R also has less effect on both T_L and S_R.

Table 4. ANOVA for T_L of 42CrMO4 F-M DPS

Factors	Degree of freedom	Seq sum of square	Adj MS	P	% Contribution
T	2	359,153	179,577	<0.001	5.45
CS	2	536,201	268,100	<0.001	8.13
FR	2	62,643	31,321	<0.001	0.95
DoC	2	5,609,599	2,804,800	<0.001	85.08
Error	18	25,403	1411
Total	26	6,593,000

Table 5. ANOVA for S_R of 42CrMO4 F-M DPS

Factors	Degree of freedom	Seq sum of square	Adj MS	P	% Contribution
T	2	2.28376	1.14188	<0.001	36.12
CS	2	21,532	0.10766	<0.001	3.41
FR	2	0.96287	0.48144	<0.001	15.23
DoC	2	2.82750	1.41375	<0.001	44.71%
Error	18	0.03393	0.00189
Total	26	6.32339

3.4. Regression analysis for T_L and S_R

The four components and their ranges are considered while creating regression equations to forecast T_L and S_R. For T_L and S_R, respectively, equations 1 and 2 provide the regression equations.

TL = 8426–6.664 Temp—4.916 Speed—2068 Feed—2786.9 DoC;

SR = 19.438–0.017722 Temp—0.003079 Speed—9.164 Feed—1.9556 DoC;

The R-squared values for the regression models are shown in Table 6.

Table 6. Shows the regression values of T_L and S_R

R^2 model	R^2 Adj	R^2
TL	98.23%	98.49%
SR:	97.34%	97.75%

The R-Sq (Adj) values i.e., 98.23% for T_L and 97.34% for S_R indicate that the regression equations possess a good fit with the actual experimental results. The prediction of T_L and S_R has been analyzed through controlled factors.

3.5. Error analysis for T_L and S_R of 42CRMO4 F-M DPS

Statistical analysis is validated using regression equations to confirm the test results. Actual test results of T_L and S_R values are compared with the predicted results of regression equations. The difference between the actual and predicted results are shown as % Error.

Figures 4 and 5 show the error analysis for T_L and S_R respectively. It is observed that predicted and actual results are approximately the same for all the test trials. Variations of predicted and actual results are minimal and the experimental results tabulated prove that regression equations obtained for this study may be used to predict T_L and S_R values.

Figure 4. Error analysis for T_L of 42CrMO4 F-M DPS.

Figure 5. Error analysis for SR of 42CrMo4 F-M DPS.

3.6. Optimization of process parameters

Maximum T_L and lower S_R is the preferred condition for obtaining better machining i.e., high machinability. Hence, combined optimization is carried out to determine the optimum values for the machining parameters speed, feed and depth cut in order to get a higher tool life and lower surface roughness.

Figure 6 shows the detailed response optimization of T_L and S_R values. The composite desirability D value of 0.9086 shows that the optimized results have a good fit. From the results, it is seen that the following values would give the optimum combination of tool life and surface roughness while machining of 773 °C temperature treated F-M dual phase 42CRMO4 steel.

Figure 6. Response surface plot for T_L and S_R of 42CrMo4 F-M DPS.

Temperature = 773°C

Speed = 113 m/min

Feed = 0.16 mm/rev

Depth of Cut = 0.36 mm

Considering the speed, feed and DoC, to assess the viability of the model, a confirmation test is run at the level of the optimized parameters. Both T_L and S_R are determined at optimized machining parameters using experiment and regression equations. Experimental results for T_L have 1941 seconds and the theoretical value for T_L obtained is 1945.98 seconds. S_R was found by seeing the optimized process parameter, the experimental value of S_R is 2.9 µm and the theoretical value is 2.8 µm. This shows the actual and theoretical values at optimized conditions are closer and within the range. The difference between these values is less than 5%.

4. Conclusions

The microstructure and mechanical property analysis depicts that results are at par with each other and variation in physical parameters of the heat treatment affects the processing parameters of machining. The results show that statistical analysis of the machining and dual phase processing temperature are correlated with the experimental result. The study may be further extended on machinability concentrating upon the type of martensite phase, platelet size and morphology, machining parameters, machining condition and single point cutting tool signature. The microstructure, tensile and hardness test results acknowledge the formation of dual phase. Microstructure reveals the F-M association in which an increase in the dual phase temperature has led to an increase in martensite content. Enhancement in the strength and hardness of DPS is observed with the growth in the dual phase temperatures higher R² value obtained for the regression equations is an indication of better experimental results. These equations may be used to forecast the T_L and S_R for the machinability of DPS. The statistical result is concluded that depth of cut has the major effect with 85.08 and 44.71% on T_L and S_R respectively. Similarly, the temperature has a 36.12% contribution on S_R and 5.45% on T_L. But speed has less effect (3.41%) on S_R and similarly for the T_L, (8.13%). The feed has shallow effect on both T_L and S_R. Optimum T_L and S_R are observed for F-M dual phase 42CrMo4 steel treated at 773 °C temperature.

Abbreviations

Tool life - T_L, Surface roughness - S_R, Dual phase steel – DPS, Cutting speed -CS, Feed rate - F_R, temperature –T, Depth of cut-DoC

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

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Additional information

Funding

The authors received no direct funding for this research.