Experimental Investigation of Cutting Parameter Effects on Tool Wear in Surface Defect Machining of Hardened AISI 4340 Steel Using CBN Cutting Tools

Experimental Investigation of Cutting Parameter Effects on Tool Wear in Surface Defect Machining of Hardened AISI 4340 Steel Using CBN Cutting Tools

Hardik Nayak

Ph. D. Scholar, Department of Mechanical Engineering, Gujarat Technological University, Ahmedabad 382424, India

Kanti Rathod

Department of Mechanical Engineering, R. N. G. Patel Institute of Technology, Bardoli 394620, India

Abstract – Surface integrity and tool life are critical concerns when machining hardened steels. Hard turning using Cubic Boron Nitride (CBN) tools has become an effective alternative to conventional grinding due to its improved productivity and operational flexibility. However, excessive tool wear during machining of hardened materials remains a major challenge. In this work, an experimental investigation was carried out to study the influence of cutting parameters on tool wear during Surface Defect Machining (SDM) of hardened AISI 4340 steel. Artificial surface defects were generated on the workpiece using Electrical Discharge Machining (EDM) in order to create controlled intermittent cutting conditions. Turning experiments were performed on a CNC lathe under dry machining conditions while varying cutting speed and feed rate. Flank wear of the cutting tool was evaluated using scanning electron microscopy (SEM), and Response Surface Methodology (RSM) was employed to develop a predictive model. The experimental findings indicate that both cutting speed and feed rate significantly influence tool wear progression. Increased cutting speed and feed rate resulted in higher tool wear due to elevated cutting temperature and mechanical loading at the cutting interface. The developed cubic regression model showed high predictive accuracy with a coefficient of determination of 0.9991. The results demonstrate that the SDM approach can improve machining performance by reducing continuous toolworkpiece interaction and enhancing chip breakability.

Keywords – Surface Defect Machining (SDM), Hard Turning, CBN Cutting Tool, Tool Wear Behaviour, Machining Parameters, Hardened Steel

1. INTRODUCTIONMachining of hardened steels is widely required in modern manufacturing sectors such as automotive, aerospace, and tooling industries. Components produced from hardened alloys often demand excellent dimensional accuracy and superior surface integrity [1]. Traditionally, grinding has been used as the primary finishing process for such materials because of its capability to achieve high surface quality [2]. However, grinding operations generally involve higher machining time, increased energy consumption, and lower productivity compared with cutting processes. Hard turning has therefore gained considerable attention as an alternative finishing technique for hardened steels [3]. With the development of advanced cutting tools such as Cubic Boron Nitride (CBN), it has become possible to machine hardened materials efficiently while maintaining acceptable surface quality. CBN tools possess extremely high hardness, good thermal stability, and excellent resistance to wear, making them suitable for machining hardened steels [4]. Despite these advantages, tool wear remains a critical limitation during hard turning operations. Excessive wear of the cutting tool can negatively influence dimensional accuracy, surface finish, and tool life. The progression of tool wear is strongly affected by machining parameters such as cutting speed, feed rate and depth of cut [5]. Inappropriate selection of these parameters may accelerate wear mechanisms including abrasion, adhesion, and diffusion. Apart from machining parameters, the characteristics of the workpiece surface also influence the interaction between the tool and the work material. Recently, Surface Defect Machining (SDM) [6] has been proposed as an innovative technique in which controlled defects are introduced on the workpiece surface before machining. These defects interrupt continuous contact between the cutting tool and the workpiece, thereby creating intermittent cutting conditions. Intermittent cutting can reduce friction, decrease heat generation in the cutting zone and improve chip breakability [7]. Consequently, SDM has the potential to reduce tool wear and improve machining efficiency. However, only limited studies have explored the effect of machining parameters on tool wear behavior under SDM conditions, particularly when machining hardened AISI 4340 steel using CBN tools [8]. Therefore, the objective of the present work is to experimentally examine the influence of cutting speed and feed rate on tool wear during Surface Defect Machining of hardened AISI 4340 steel. Response Surface Methodology is used to analyze the relationship between machining parameters and tool wear, and to develop a predictive model for flank wear.
2. LITERATURE REVIEWHard turning has attracted significant attention as an alternative finishing process for hardened steels due to its capability to achieve high dimensional accuracy and improved productivity compared with traditional grinding operations. However, maintaining surface integrity and minimizing tool wear remain important challenges when machining hardened materials. Bouacha et al. [9] investigated

   the influence of cutting parameters on surface roughness and cutting forces during hard turning of AISI 52100 steel using CBN cutting tools. Their results indicated that feed rate had a strong influence on surface roughness, whereas cutting speed mainly affected tool wear progression and cutting forces. Aouici et al. [10] studied the machining performance of hardened X38CrMoV5- 1 steel using CBN tools under dry cutting conditions. Their findings showed that increasing cutting speed significantly accelerated flank wear due to higher cutting temperatures generated at the toolworkpiece interface. They emphasized that appropriate selection of machining parameters is essential for improving tool life. Dureja et al. [11] examined the machinability of cold work tool steel during hard turning and analyzed the influence of cutting parameters on tool wear and surface roughness. The authors reported that feed rate had the greatest effect on surface finish, while cutting speed played a dominant role in determining the rate of tool wear. Ozel et al. [12] conducted an investigation on tool wear mechanisms during hard turning with CBN tools. Their study revealed that abrasion and diffusion were the primary wear mechanisms observed when machining hardened steels, particularly at higher cutting speeds where thermal effects become significant. Lima et al. [13] evaluated the influence of machining conditions on tool life in hard turning processes. Their results indicated that high cutting speeds and feed rates increase tool wear due to elevated cutting temperature and mechanical stresses acting on the cutting edge. More et al. [14] analyzed the effect of cutting parameters on surface roughness and tool wear during machining of hardened AISI 4340 steel. Their study demonstrated that lower feed rates and moderate cutting speeds are beneficial for improving surface quality and extending tool life. Choudhury et al. [15] developed predictive models to estimate surface roughness and tool wear using statistical and artificial intelligence techniques. Their research highlighted the usefulness of modeling approaches in optimizing machining parameters for improved process performance. Huang and Liang

   [16] investigated the wear characteristics of CBN cutting tools during hard turning operations. They observed that flank wear increases rapidly with increasing cutting speed due to higher thermal loas generated in the cutting region. Grzesik et al. [17] examined the performance of various cutting tool materials during hard machining and reported that CBN tools provide superior wear resistance and longer service life compared with conventional carbide tools when machining hardened steels. Astakhov et al.
   [18] studied the wear behavior of CBN tools during machining of hardened alloys and reported that adhesive wear and abrasive wear are the dominant wear mechanisms during the cutting process. Although numerous studies have focused on tool wear behavior in conventional hard turning, relatively fewer investigations have explored the concept of Surface Defect Machining (SDM). In this technique, controlled defects are introduced on the workpiece surface before machining in order to create intermittent cutting conditions [19]. The presence of surface defects can interrupt the continuous contact between the cutting tool and the workpiece, which may help reduce friction, cutting temperature, and chip adhesion. As a result, intermittent cutting conditions have the potential to improve chip breakability and reduce tool wear. Despite these advantages, experimental studies examining the influence of machining parameters on tool wear during SDM operations are still limited. In particular, systematic investigations involving hardened AISI 4340 steel and CBN cutting tools remain scarce. Therefore, further experimental research is required to better understand the interaction between machining parameters and tool wear behavior during Surface Defect Machining. The present study addresses this gap by experimentally analyzing the effect of cutting speed and feed rate on tool wear during SDM of hardened AISI 4340 steel using CBN tools. In addition, Response Surface Methodology (RSM) is employed to develop a predictive model and evaluate the interaction between machining parameters.
3. MATERIALS AND METHODS
   1. Workpiece Material and Cutting Tool:The turning experiments were carried out using AISI 4340 alloy steel as the workpiece material. This alloy is commonly used in aerospace, automotive, and heavy-duty engineering components because of its high strength, toughness, and wear resistance. Cylindrical specimens having a diameter of 38 mm were prepared for the experiments. The material was heat treated to obtain a hardness of approximately 51 ± 1 HRC, representing typical conditions encountered in hard turning operations. The chemical composition of AISI 4340 steel is presented in Table 1. The presence of alloying elements such as chromium, nickel, and molybdenum contributes to its high mechanical strength and good hardenability. A Cubic Boron Nitride (CBN) insert was used as the cutting tool due to its excellent hardness, high thermal stability, and superior resistance to wear when machining hardened steels. The turning tests were performed using CBN grade 7115 inserts (DNGA 150408S01525H) manufactured by Sandvik. The insert had a nose radius of 0.8 mm and was mounted on a DDJNL 2525M 1504 tool holder. The tool holder geometry consisted of a cutting edge angle of 93°, a rake angle of 6°, and an inclination angle of 7°, which are commonly recommended for hard turning applications.

      Table 1: Chemical compositions of AISI 4340 steel

      Element C Cr Ni Mo Mn S P Si V Al Cu Ti (Wt %) 0.420 0.82 1.725 0.219 0.65 0.027 0.012 0.284 0.044 0.043 0.067 0.021

   2. Experimental SetupAll machining experiments were conducted on a CNC turning center (JYOTI 200DX-3A) having a spindle power of 7.5 kW. The experiments were performed under dry cutting conditions without the use of coolant in order to simulate typical industrial hard turning environments as shown Figure 1. To implement the Surface Defect Machining (SDM) approach, artificial surface defects were intentionally generated on the workpiece prior to the turning operation. These defects were produced using a CNC Electrical Discharge Machining (EDM) system (Mitsubishi EA28VA). Experimental set up for hole generation using EDM machine is shown in Figure 2. A specially fabricated copper electrode with a diameter of 0.8 mm and length of 3 mm was used to create small holes on the workpiece surface. The generated defects had an approximate diameter of 0.9 mm and a depth of about 0.17 mm as shown in Figure 4. The holes were arranged along the circumference of the workpiece with a spacing of 10 mm between consecutive defects.

      Tool holder

      CBN Insert

      Work piece

      Figure 1: Experimental arrangement for the hard turning operation

      Tool holder

      Electrode

      Workpiece Figure 3: Fabricated copper electrode

      employed for defect generation

      Figure 2: EDM setup used to generate artificial surface defects

      To verify the geometry of the defects produced by EDM, the workpiece was sectioned at the center of the holes and examined using Scanning Electron Microscopy (SEM) are shown in Figure 5 and Figure 6.. The SEM observations were performed using a Zeiss FE-SEM (Ultra-55) microscope.

      Figure 4: Machined workpiece showing the location of EDM-produced holes

      Figure 5: SEM micrograph illustrating the diameter of the generated hole

   3. Selection of Machining ParametersFigure 6: SEM micrograph showing the depth profile of the EDM hole

      During the experimental trials, cutting speed and feed rate were selected as the primary process variables, while the depth of cut was maintained at a constant value of 0.2 mm throughout all machining tests.The range of cutting parameters used in this study was selected based on tool manufacturer recommendations and information available in the literature. The levels of cutting speed and feed rate used during the experiments are listed in Table 2.

      Table 2: Levels of cutting parameters for AISI 4340 steel using CBN tool

      Factor	Unit	Level 1	Level 2	Level 3	Level 4
      A: Cutting speed (V)	m/min	120	150	180	210
      B: Feed rate (f)	mm/rev	0.07	0.1	0.13	0.16

      These machining parameters were applied for both conventional hard turning and SDM conditions in order to compare their influence on machining performance.

   4. Experimental DesignResponse Surface Methodology (RSM) was employed to design the experiments and analyze the relationship between machining parameters and the response variable. A full factorial design with two factors at four levels (4² design) was adopted, resulting in a total of sixteen experimental runs are shown in Table 3. This design approach allowed the investigation of both the individual effects of cutting speed and feed rate as well as their interaction effects on tool wear behavior.

      Table 3: Four level full factorial design for AISI 4340 steel using CBN tools

      Exp.	A:V	B:f	Exp.	A:V	B:f
      No.	(m/min)	(mm/rev)	No.	(m/min)	(mm/rev)
      1	210	0.16	9	120	0.07
      2	150	0.16	10	150	0.1
      3	210	0.13	11	180	0.16
      4	210	0.1	12	180	0.07
      5	150	0.07	13	150	0.3
      6	210	0.07	14	120	0.1
      7	180	0.1	15	120	0.16
      8	180	0.13	16	120	0.13

   5. Measurement of Surface CharacteristicsAfter completion of the machining process, the surface quality of the workpiece was assessed using a Mitutoyo SJ-410 surface roughness tester. The measurements were performed with a cut-off length of 0.8 mm and five sampling lengths to ensure reliable evaluation of the surface profile. Surface roughness readings were obtained directly on the machined workpiece without removing it from the machine tool in order to avoid positioning errors and maintain measurement consistency. For accurate assessment, the effective machined length of the workpiece was divided into four equal sections of approximately 50 mm each, and multiple measurements were recorded from each section. In addition, the workpiece was rotated by 90° to obtain supplementary readings from a different circumferential position, thereby improving the reliability of the measured data. A similar measurement methodology was previously adopted in earlier work reported in the literature [19].
4. RESULT AND DISCUSSION
   1. Analysis of Flank Wear AnalysisFlank wear is one of the most important indicators of tool degradation during machining operations. In hard turning processes using Cubic Boron Nitride (CBN) tools, flank wear significantly influences machining accuracy, surface integrity, and overall tool life. In the present study, flank wear was measured after each experimental run to evaluate the influence of cutting parameters during Surface Defect Machining (SDM). Figure 7 illustrates the variation of flank wear (VB) with respect to cutting speed at different feed rates. The results clearly indicate that tool wear increases progressively with an increase in cutting speed for all feed rate conditions. At a cutting speed of 120 m/min, the lowest tool wear value of approximately 16.01 µm was observed at the lowest feed rate of 0.07 mm/rev. As the feed rate increased to 0.16 mm/rev, the flank wear increased to approximately 21.17 µm. This behavior indicates that increasing feed rate results in higher chip load and cutting forces, which accelerate tool wear. When the cutting speed increased to 150 m/min and 180 m/min, the flank wear values showed a gradual rise. The increase in cutting speed leads to higher temperatures in the cutting zone due to increased friction and plastic deformation of the work material. These elevated temperatures contribute to thermal softening of the cutting edge and promote wear mechanisms such as adhesion and diffusion.

      Tool Wear

      Cutting Speed

      Figure 7: Effect of cutting speed and feed rate on flank wear during SDM

      At the highest cutting speed of 210 m/min, the maximum flank wear values were recorded for all feed rates. The measured flank wear values were approximately 27.06 µm, 28.16 µm, 29.19 µm, and 31.41 µm for feed rates of 0.07 mm/rev, 0.10 mm/rev, 0.13 mm/rev, and 0.16 mm/rev respectively. This significant increase in wear can be attributed to the combined effects of high cutting temperature, increased mechanical load, and intensified toolworkpiece interaction. Overall, the experimental results indicate that both cutting speed and feed rate have a strong influence on flank wear during SDM operations. Lower cutting speeds combined with lower feed rates produce minimal tool wear, while higher machining parameters accelerate tool degradation.

   2. SEM Analysis of Tool WearScanning Electron Microscopy (SEM) was used to analyze the wear patterns on the CBN cutting inserts after each machining experiment. SEM analysis provides detailed information about the wear mechanisms occurring at the toolworkpiece interface.

      Figure 8 presents SEM images of the CBN inserts corresponding to different experimental conditions defined in the experimental design matrix.

      Figure 8: SEM observations of worn CBN inserts corresponding to experimental runs (Table 3)

      The SEM observations reveal that the dominant wear mechanisms during SDM include abrasive wear, adhesive wear, and edge chipping. Abrasive wear occurs due to the interaction between the hard particles present in the hardened steel and the cutting edge of the tool. Adhesive wear is caused by localized bonding between the tool and workpiece material at high temperatures and pressures. In addition, small edge chipping was observed in some cases, particularly at higher cutting speeds and feed rates. The presence of surface defects introduced by EDM resulted in intermittent cutting conditions during machining. This intermittent contact reduced the continuous sliding interaction between the tool and workpiece, which helped in lowering friction and cutting temperature to some extent. As a result, SDM demonstrated improved machining behavior compared to conventional hard turning conditions.

   3. Model Summary Statistics for Flank wear (VB)Statistical analysis was performed using Response Surface Methodology (RSM) to develop predictive models for flank wear. Several regression models including linear, interaction, quadratic, cubic, and quartic models were evaluated to determine the best fit for the experimental data. The model summary statistics are presented in Table 4. Among the tested models, the cubic model provided the best statistical performance with a coefficient of determination (R²) of 0.9991, adjusted R² of 0.9979, and predicted R² of 0.9955. The low standard deviation value of 0.1893 and the small PRESS value of 1.14 indicate minimal prediction error.

      Table 4: Model Summary Statistics of VB by using SDM

      Source	Std. Dev.	R²	Adjusted R²	Predicted R²	PRESS
      Linear	0.7653	0.9696	0.9649	0.9520	12.03
      2FI	0.7866	0.9704	0.9629	0.9358	16.08
      Quadratic	0.5917	0.9860	0.9790	0.9427	14.34
      Cubic	0.1893	0.9991	0.9979	0.9955	1.14	Suggested
      Quartic	0.2454	0.9993	0.9964	0.9749	6.28	Aliased

      These results demonstrate that the cubic model provides a highly accurate representation of the relationship between machining parameters and flank wear under SDM conditions.

   4. ANOVA for Flank WearAnalysis of Variance (ANOVA) was conducted to determine the significance of machining parameters affecting flank wear. The ANOVA results are presented in Table 5. The results indicate that the developed regression model is highly significant, with an F- value of 775.83 and a p-value less than 0.0001. Among the investigated paameters, cutting speed was found to be the most influential factor affecting flank wear, contributing approximately 44.83% to the total variation. Feed rate was identified as the second most significant factor, contributing approximately 6.35%.

      Table 0: ANOVA for flank wear (VB) by using SDM

      Source	Sum of Squares	df	Mean Square	F-value	p-value % Cont.
      Model	250.28	9	27.81	775.83	< 0.0001 significant
      A-V	6.89	1	6.89	192.11	< 0.0001 44.83 significant
      B-f	0.9763	1	0.9763	27.24	0.0020 6.35 significant
      AB	0.6271	1	0.6271	17.49	0.0058 4.08 Not significant
      A²	3.21	1	3.21	89.58	< 0.0001 20.89 significant
      B²	0.1596	1	0.1596	4.45	0.0793 1.04 Not significant
      A²B	1.06	1	1.06	29.52	0.0016 6.90 significant
      AB²	1.21	1	1.21	33.70	0.0011 7.87 significant
      A³	0.9095	1	0.9095	25.37	0.0024 5.92 significant
      B³	0.1110	1	0.1110	3.10	0.1289 0.72 Not significant
      Residual	0.2151	6	0.0358
      Cor Total	250.50	15

      R2 statistic R2 = 99.91% Adj. R² =99.79% Pred. R² = 99.55%

      Higher-order interaction terms such as A²B, AB², and A³ were also found to be statistically significant, indicating the nonlinear nature of tool wear behavior during machining. The small residual error value of 0.2151 confirms the reliability of the developed regression model. A cubic regression model was developed to predict average flank wear (VB) during hard turning using CBN tools. The model accounts for the effects of cutting speed (V), feed rate (f) and their interactions, capturing the wear caused mainly by mechanical abrasion and edge chipping. The regression equation is shown in Equation 1.

       

   5. Model ValidationTo validate the developed regression model, predicted flank wear values were compared with experimental results. The comparison between predicted and experimental values is shown in Figure 9. The plot demonstrates that the predicted values closely follow the experimental data and lie near the 45° reference line. The percentage deviation between predicted and experimental values was found to be within ±5%, confirming the high accuracy of the model. Residual analysis also showed that the residuals were randomly distributed around zero, indicating that the model assumptions of normality and independence were satisfied.

      Figure 9: Residual analysis plot comparing predicted and experimental flank wear values

   6. Response Surface AnalysisFigure 10: Three-dimensional response surface showing combined effect of cutting speed and feed rate on flank

      wear

      A three-dimensional response surface plot was generated to illustrate the combined effect of cutting speed and feed rate on flank wear. The response surface plot is presented in Figure 10. The response surface indicates that flank wear increases significantly with increasing feed rate. Higher feed rates result in increased chip load and cutting forces, which accelerate tool wear. Cutting speed also influences tool wear due to thermal effects. At moderate cutting speeds and lower feed rates, the minimum flank wear values were observed. However, excessive cutting speeds lead to increased temperature in the cutting zone, which accelerates wear mechanisms such as diffusion and adhesion. These results confirm that proper selection of machining parameters is essential for minimizing tool wear and improving machining efficiency during SDM.

5. CONCLUSIONThis research experimentally examined the influence of machining parameters on tool wear during hard turning and Surface Defect Machining (SDM) of hardened AISI 4340 steel using CBN cutting tools. Artificial surface defects were introduced on the workpiece

   using an EDM process to create controlled intermittent cutting conditions. Based on the experimental observations and statistical analysis, the following conclusions can be summarized:

   • The experimental results confirm that machining parameters significantly affect tool wear behavior during hard turning of hardened AISI 4340 steel with CBN inserts.
   • Cutting speed was found to be one of the most influential factors governing flank wear. Increasing cutting speed resulted in higher cutting zone temperatures, which accelerated wear mechanisms such as adhesion and diffusion at the toolworkpiece interface.
   • Feed rate also had a considerable impact on tool wear progression. Higher feed rates increased chip load and cutting forces, producing greater mechanical stress on the cutting edge and consequently increasing flank wear.
   • Minimum tool wear occurred at lower combinations of cutting speed and feed rate, whereas maximum wear was observed when both parameters were at higher levels, highlighting the importance of selecting appropriate machining conditions.
   • SEM observations indicated that abrasive wear, adhesive wear, and localized edge chipping were the primary wear mechanisms affecting the CBN cutting inserts during machining of hardened AISI 4340 steel.
   • The Response Surface Methodology (RSM) approach successfully modeled the relationship between machining parameters and flank wear. The developed cubic regression model showed excellent predictive capability with a coefficient of determination (R² = 0.9991).
   • The introduction of controlled surface defects through the SDM technique produced intermittent cutting conditions that reduced continuous toolworkpiece interaction and improved chip breakability. This contributed to improved machining performance and slower tool wear progression.Overall, the results of this investigation demonstrate that the combination of optimized machining parameters and the SDM technique can significantly enhance tool life and machining efficiency in the hard turning of hardened steels using CBN cutting tools.
6. FUTURE SCOPE

Although the present study provides useful insights into the influence of machining parameters on tool wear during Surface Defect Machining, further research can be conducted to expand the understanding and practical application of this technique. The following directions are suggested for future work.

• Future investigations can examine the influence of additional machining parameters such as depth of cut, tool geometry, and cutting environment in order to further optimize the machining performanceof SDM.
• Additional response variables including cutting forces, temperature distribution in the cutting zone, chip morphology, and surface integrity may be analyzed to obtain a more comprehensive understanding of the SDM mechanism.
• The applicability of the SDM approach can also be evaluated using different cutting tool materials such as coated carbide, ceramic tools, and PCBN inserts to compare their machining performance.
• Advanced predictive modeling techniques such as artificial neural networks (ANN), machine learning algorithms, or hybrid optimization approaches may be applied to develop more accurate models for predicting tool wear and optimizing machining parameters.

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