- Original Paper
- Open Access
Effect of cutting parameters on the dimensional accuracy and surface finish in the hard turning of MDN250 steel with cubic boron nitride tool, for developing a knowledged base expert system
© The Author(s). 2019
- Received: 14 August 2018
- Accepted: 14 December 2018
- Published: 7 January 2019
In the machining operations, final surface finish and dimensional accuracy are the most specified customer requirements. Hard turning machining operation using cubic boron nitride tool as an alternative of grinding process is a type of turning operation in which hardened steel are machined with the hardness greater than 45 HRc. During the hard turning operation because of the hard condition, the variations of surface finish and dimensional accuracy are completely different from that of the traditional turning operation. Thus, the variation of surface finish and dimensional accuracy under various cutting parameters has been investigated in the hard turning with cubic boron nitride tools. The extracted knolwdge can be used for developing a knowledged base expert system. In order to have a comprehensive study, the variation of vibration, cutting forces, and tool wear has also been considered. The obtained results showed that depth of cut and spindle speed have the greatest effect on the dimensional accuracy, while feed rate is the most important factor affecting the surface roughness. The analysis of the vibration and tool wear proved that the flank wear has insignificant influence on the dimensional accuracy, whereas the vibration effect is considerable. The experimental results showed that when the feed rate is gradually increased from 0.08 to 0.32, the dimensional deviation first decreases unexpectedly until the lowest value is achieved at 0.16 mm/rev, then by further increasing the feed from 0.16 to 0.32 mm/rev, the dimensional deviation increases significantly. It was also seen that the best dimensional accuracy is achieved at the lowest level of the cutting depth, the medium level of the feed rate, and the spindle speed lower than its moderate level. The best surface roughness of 0.312 μm was obtained at 0.08 mm/rev feed rate, 0.5 mm depth of cut, 2000-rpm speed, and 1.2 mm insert nose radius, which is comparable with the surface finish obtained by the grinding operation.
- Cutting parameters
- Surface finish
- Dimensional accuracy
- Expert system
- Cutting force
- Optimal condition
- Hard turning
Hard turning with cubic boron nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface quality and accuracy (Lalwani et al. 2008; Yousefi et al. 2017; Zohoor and Yousefi 2018; Yousefi and Zohoor 2018; Thiele and Melkote 1999). Hard turning eliminates the additional heat treatment and grinding operations, and as a result, decreases lead-time, total production cost, and also improves the productivity and final quality. Consequently, substantial reduction in production cost and appropriate surface quality and accuracy can be achieved simultaneously. Hard turning in the dry condition causes the elimination of using environmentally high-risk lubricant (Saini et al. 2012; Sandvik Coromant 2010; Asiltürk and Akkuş 2011; Özel and Karpat 2005; Elbah et al. 2011).
Expert systems as an artificial intelligent system, are important tools in manufacturing and production systems. An expert system is a computer program that uses artificial intelligence technologies to simulate the behavior of a human that has expert knowledge and experience in a particular field. In this study, the final results of the experiments, as expert rules can be used for developing the knowledge base of a expert system. Cubic boron nitride (CBN) due to its high thermal shock resistance and hot hardness at elevated temperature is suitable for the turning of hardened steels with the hardness over 45 HRC (Yousefi and Zohoor 2018). The criteria like surface roughness, dimensional accuracy, cutting force, wear, and power consumed are usually used for the evaluation of the hard turning performance (Chavoshi and Tajdari 2010; Khrais and Lin 2007; Das et al. 2015; Aslan et al. 2007; Dhar et al. 2006; Dhar et al. 2007; Risbood et al. 2003; Dhar and Kamruzzaman 2007; Dhar et al. 2002; Shahabi and Ratnam 2010; Revel et al. 2016; Bartarya and Choudhury 2012). Nowadays, in metal cutting industries, special attention is paid to find the best-desired final surface quality and dimensional accuracy (Lalwani et al. 2008; Yousefi et al. 2017; Zohoor and Yousefi 2018; Yousefi and Zohoor 2018; Thiele and Melkote 1999; Saini et al. 2012).
The surface roughness (Ra) is an important factor that affects the properties of the machined component, like fatigue life, corrosion resistance, friction coefficient, wear resistance, and lubrication ability (Yousefi and Zohoor 2018). The dimensional accuracy is another important quality criterion, which significantly affects the machining cost and time. By selecting appropriate processing parameters, the dimensional accuracy improves, and therefore, the additional cutting pass is reduced. As a result, the machining cost and time will be reduced significantly. The factors, which have significant effect on the surface roughness and dimensional accuracy, can be categorized as workpiece properties, cutting tool geometry, cutting variables (i.e., cutting depth, cutting speed, and feed rate), and machine tool characteristics.
Among all of the mentioned factors, cutting depth, spindle speed, feed rate, and insert nose radius are the most effective controllable factors for the operator to achieve the desired surface roughness and dimensional accuracy during the turning operation (Zohoor and Yousefi 2018; Yousefi and Zohoor 2018; Thiele and Melkote 1999).
Despite the importance of dimensional accuracy in the dry hard turning, there are no any comprehensive studies in this field. In addition, the effect of vibration, cutting forces, and tool wear on the surface quality and dimensional accuracy in the hard turning using CBN is another important subject that should be studied for different hard steels.
Various research attempts have been performed for hard turning but none of them considered these issues simultaneously. Thiele and Melkote (1999) determined the effects of tool cutting edge geometry and workpiece hardness on the surface roughness and cutting forces in the finish hard turning of AISI 52100 steel. Cubic boron nitride inserts with various representative cutting edge preparations and through-hardened AISI 52100 steel bars were used as the cutting tools and workpiece material, respectively. Asiltürk and Akkuş (2011) determined the effect of cutting depth, feed rate, and cutting speed on the final surface of AISI 4140 with 51 HRc hardness. Their analysis showed that the feed rate is the most important factor that affects the Ra and Rz.
Özel and Karpat (2005) used neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in the finish hard turning of hardened AISI H-13 steel. Regression models were also developed to capture processing parameters.
Elbah et al. (2011) by using different ceramic inserts analyzed the surface roughness alteration for AISI 4140 with 60 HRC hardness. They found that for both conventional and wiper inserts, the quality of the machined surface is significantly affected by varying feed rate and cutting depth. They proposed the lowest feed rate and cutting depth and the highest spindle speed as the ideal combination to achieve a minimum surface roughness. In a similar research work, Chavoshi and Tajdari (2010) by using CBN inserts studied the effect of spindle speed and material hardness variations on the surface roughness of hardened AISI4140 steel. They found that the surface roughness variations strongly depend on the workpiece hardness, whereas speed has no critical effect. In addition, their results revealed that in the range of 35 to 55 HRC, increasing the workpiece hardness leads to increase in the surface finish, but further increase in the hardness deteriorates the surface finish. Some other researchers studied experimentally the variations of surface finish and tool wear during the hard turning of AISI4140 (Khrais and Lin 2007; Das et al. 2015; Aslan et al. 2007). Compared with the surface finish, the dimensional accuracy in the dry hard turning has been studied even more limited.
For example, Dhar et al. (2006; 2007) studied the influence of minimum quantity lubrication (MQL) on the dimensional accuracy and surface roughness of AISI4340 and AISI1040 steel during the conventional turning operation. In another research work, Risbood et al. (2003) demonstrated that the dimensional accuracy can be predicted accurately by measuring the cutting force and tool vibration. They proposed the neural network as an effective tool for the prediction of the dimensional deviation of carbon steel with 130 BHN hardness.
Some other researchers under cryogenic conditions studied the tool wear, dimensional accuracy, and surface roughness of alloy steel (Dhar and Kamruzzaman 2007; Dhar et al. 2002). They concluded that the cryogenic cooling by liquid nitrogen jets significantly decreases the tool wear, dimensional deviation, and surface roughness, compared to the dry and wet conventional turning. In addition, Shahabi and Ratnam (2010) used a machine vision approach to predict the surface roughness and dimensional accuracy in the conventional turning of AISI 304 stainless steel. They used 2D images of cutting tools to develop mathematical models for predicting the surface roughness and dimensional deviation.
In the hard turning process, due to the high workpiece hardness, small cutting depths is selected, and therefore, usually the cutting depth becomes lower than or equal to the insert nose radius, and the cutting zone is mainly limited within the tool nose area. Thus, unlike the traditional turning, the effect of the nose radius on the surface finish and dimensional accuracy becomes complicated. Thus, in this paper, the effect of processing parameters such as tool nose radius (rε), depth of cut (d), spindle/cutting speed (N/V), and feed rate (f) on the dimensional accuracy (Δd) and surface roughness (Ra) of hardened steel using CBN cutting tool is studied. Then, the influence of cutting forces, tool wear, and vibration is investigated on the surface finish and dimensional accuracy. Finally, the optimal combinations, which yield the best dimensional accuracy and surface finish, are proposed experimentally.
The experimental work was carried out on a CNC lathe (5.5 kW spindle power and 3500 rpm maximum rotational speed). Due to the importance of the dry machining in the modern industry, the tests were performed under the dry condition without any fluid, gas, and/or solid coolant. Standard CBN inserts (ISO code CNGA120404S010-30A, CNGA120408S010-30A, CNGA120412S010-30A) made by Sandvik™ Coromant were used as the cutting tools with the following geometry: clearance angle = 0°, chamfer angle = 30°, chamfer width = 0.10 mm, edge thickness = 4.76 mm, edge length = 12 mm, and rhombic tip angle = 80°. The same inserts with different nose radius of 0.4 mm, 0.8 mm, and 1.2 mm were used for analysis of the nose radius effect. In order to have a stable cutting condition, each experiment was performed with a new sharp insert. The inserts grade was CB7025, which is completely suitable for hard turning operation. The tool holder used in the experiments was standard DCBNR/L-2525 M. According to the cutting tool manufacturer’s recommendation, the inserts were clamped on the holder with the tightening torque of 3.9 Nm. Round bars of MDN250 steel with 35 mm diameter and 85 mm length were used for the experiments. In order to increase the hardness of the steel, according to the ASM International standard, the round bars have been heated at about 850 °C for 75 min, and then quickly quenched in the water (Dossett and Boyer 2006). Subsequent aging was performed for approximately 3 h at a temperature of 500 °C to produce a fine dispersion of Ni3(X,Y) intermetallic phases along the dislocations left by martensitic transformation, where X and Y are solute elements added for such precipitation. A pre-cut with 0.5 mm cutting depth was performed before hardening the steel to remove the rusts and oxide layers from the surface of the workpiece. Finally, the hardness level of 55 HRC was obtained by performing the heat treatment method for the round bars of MDN250 steel. Average surface roughness (Ra) values were recorded with a Surftest 301 Mitutoyo® roughness meter set to a cut-off of 0.8 mm. The surface roughness values were measured at five equally spaced locations along the cutting length. The average of this five roughness values was taken as the arithmetic surface roughness value (Ra). This measuring process was repeated three times for each test and their average was considered as the final surface roughness. A piezoelectric type vibration meter was used to measure and recorded the acceleration of the radial holder vibration. Three forces component, i.e., feed force, cutting force, and radial force, was measured precisely by using a Kistler piezoelectric turning dynamometer and a signal amplifier connected to a control unit, data acquisition board and microcomputer. The maximum flank wear (VBmax) was measured using a TM505 Mitutoyo® toolmaker’s microscope with 1 μm resolution. The dimensional accuracy, which is defined as the difference between the desired cutting depth and the obtained depth of cut, was determined by measuring the final dimensional deviation. The diameter deviation was measured using a precision dial gauge with an accuracy of ± 0.01 mm. Diameter deviation was measured at five equally spaced points along the cutting length and the average of these five values considered as the final dimensional deviation value. In this paper, insert nose radius, feed rate, cutting depth, and speed were considered as the processing parameters. The values of the variables were chosen according to the recommendation of the cutting tool catalogs. In order to conduct a comprehensive study, the cutting depth and feed speed with a wider range were selected compared with the ranges chosen by the previous researchers in the hard turning operation. Furthermore, to increase the confidence level of the results, all the measurements were repeated three times for each test.
According to the experimental results, it was observed that the feed rate with a 60% contribution is the most important factor affecting the surface roughness. The next important factor influencing the surface roughness is the insert nose radius with a 28% contribution. Compared to the feed and nose radius effect, the cutting depth and the spindle speed effect is insignificant. On the other hand, the cutting depth with 50% contribution has a critical effect on the dimensional accuracy, while the nose radius has insignificant influence. The spindle speed is the next important factor affecting the final dimensional accuracy.
The effect of insert nose radius
The analysis of the experimental results showed that the nose radius effect on the dimensional accuracy is neglect. As shown in Fig. 1b, the lowest dimensional deviation is obtained at 0.4 mm and 0.8 mm nose radii respectively. The highest dimensional accuracy is produced at the lowest level of the nose radius (i.e., 0.4 mm). As shown in Fig. 2b, by increasing the nose radius, the vibration is increased. By increasing the nose radius from 1.2 to 0.4 mm, the tool wear is exacerbated, but the dimensional accuracy is improved due to the lower generated vibration. Therefore, it can be said that at the dry hard turning operation, the insert flank wear increases by decreasing the nose radius, but the dimensional accuracy improves significantly. The lower dimensional accuracy at the larger nose radius can be related to the higher radial force and vibration, which enhance because of the larger contact area between the cutting tool and the workpiece surface.
The effect of spindle speed
The effect of feed rate
It is also interesting to note that at 0.4 mm nose radius, when the feed rate increases from 0.16 to 0.32 mm/rev, the average of the surface roughness is decreased from 8.32 to 6.93 μm, while at other nose radii, there is no such complicated behavior. As a result, it can be said that the effect of the feed rate on the variations of the surface roughness, especially at high-level feed rates, strongly depends on the insert nose radius.
Most of the researchers reported that by increasing the feed rate within the range of 0.04 mm/rev to 0.4 mm/rev, the surface finish is decreased significantly.
However, in the this research work, the obtained results showed that increasing the feed rate from a particular value not only leads to no significant changes in the surface roughness value but in some cases improves the surface roughness. This subject is important, especially in situations, where the total operation time or the total cost is more vital than the surface finish.
It is well known that strain hardening and thermal softening are two important factors affecting the surface quality. These two conflicting factors act completely opposite to each other. Strain hardening increases the hardness of the workpiece and the thermal softening reduces the workpiece hardness. At the feed rate relatively higher than 0.16 mm/rev, due to higher forces (see Fig. 6a), extra heat is produced in the cutting zone, compared with the lower feed rates. This extra heat leads to thermal softening of the workpiece material. On the other hand, CBN tools have an excellent hot hardness and the produced heat overcomes the strain hardening effects of the workpiece material and decreases its hardness and shear strength, especially at the interface of the workpiece and tool. Thus, the thermal softening of the workpiece material reduces the tool wear rate and therefore, the surface roughness is not increased.
The effect of cutting depth (d)
As explained earlier, the impact of cutting depth on the surface roughness is insignificant. Some other researchers reported similar result by using carbide and ceramic tools (Asiltürk and Akkuş 2011; Das et al. 2015; Aslan et al. 2007). On the other hand, M. Elbah et al. (2011) claimed that for the wiper ceramic tools, the influence of the cutting depth on the surface roughness is considerable. The obtained results show that the surface roughness is decreased about 9% by increasing the cutting depth to 2 mm. Further increasing the cutting depth to about 3 mm leads to the surface roughness deterioration. This surface roughness deterioration is mainly due to the chatter phenomenon, which occurs at the high level of the cutting depths. According to the results, 0.312 μm is the best-obtained surface roughness that has been achieved with 0.5 mm cutting depth, 2000 rpm speed, 0.08 mm/rev feed rate, and 1.2 mm nose radius. This low surface roughness is comparable with the surface finish that is obtained under the conventional grinding operation. The cutting depth in the range of 0.05 to 0.3 mm is proposed by most researchers for the finish hard turning operation (Revel et al. 2016; Bartarya and Choudhury 2012). However, the obtained results showed that by using the CBN tools, a better surface finish can be achieved at higher cutting depths compared to the ceramic and carbide tools. Therefore, by using CBN inserts during the finish hard turning, the total cost and the operational time both will improve simultaneously.
According to Fig. 9, by applying the medium level of the cutting depth, a desirable surface roughness is achieved. The analysis reveals that a proper surface finish is achieved when the spindle speed and nose radius are higher than their moderate level. From the above discussion, it can be concluded that the highest level of the spindle speed and nose radius, the moderate level of the cutting depth, and the lowest level of the feed rate is an ideal combination to achieve an acceptable surface roughness in the finish hard turning operation.
Figure 10 shows the dimensional accuracy variations under the various combinations of the processing parameters. The best dimensional accuracy is achieved when the dimensional deviation is equal to zero (i.e., Δd = 0). As depicted in Fig. 10a b, a high dimensional accuracy is obtained at the lowest level of the spindle speed and nose radius. In addition, as shown in Fig. 10c, d, the spindle speed between 700 and 1200 rpm along with the feed rate lower than 0.15 mm/rev is an ideal combination to achieve a high dimensional accuracy. It can be seen from Fig. 10g–j that for any level of the feed rate and spindle speed, the best dimensional accuracy is achieved at the cutting depth lower than 1.5 mm. According to the experimental results, 0.5 mm cutting depth, 0.8 mm nose radius, 0.16 mm/rev feed rate, and the speed lower than 1100 rpm provide the best dimensional accuracy. Therefore, it can be concluded that the lowest cutting depth level, the moderate feed rate, and nose radius level and the lowest level of the spindle speed always produce the best dimensional accuracy during the hard turning operation.
The impact of the cutting parameters on the surface roughness and dimensional accuracy of hardened steel with CBN cutting tools was studied experimentally. In order to perform a comprehensive study on the dimensional accuracy and surface roughness, the variation of vibration, cutting forces, and tool wear under various cutting condition was investigated. The obtained results showed that feed rate is the most important factor affecting the surface roughness, while cutting depth and spindle speed has no considerable effect on the surface roughness. On the other hand, the effect of cutting depth and spindle speed on the dimensional accuracy is significant, whereas nose radius has no considerable effect on the dimensional accuracy. The tool wear and vibration analysis showed that the effect of vibration on the dimensional accuracy is considerable compared with the tool wear effect on the dimensional accuracy. It was also observed that by increasing the feed rate from a particular value, the surface roughness not only has no significant changes, but in some cases, the surface roughness decreases significantly. The best surface roughness of 0.312 μm was obtained at the nose radius of 1.2 mm, the spindle speed of 2000 rpm, the feed rate of 0.08 mm/rev, and 0.5 mm cutting depth, which is comparable with that obtained by the grinding operation. According to the experimental results, 0.5 mm cutting depth, 0.16 mm/rev feed rate, and the speed lower than 1100 rpm produced the best dimensional accuracy. The highest level of the nose radius and spindle speed, the moderate level of the cutting depth, along with the lowest level of the feed rate is an ideal combination, which yields the best surface roughness for the finish hard turning operation. It was also concluded that by using the CBN cutting tools, a better surface roughness at both higher cutting speed and cutting depth can be achieved compared with the ceramic and carbide tools. Furthermore, by using CBN cutting tools in the finish hard turning, higher cutting depths can be used compared with the proposed range of 0.1–0.3 mm for other cutting tools. All of these obtained results can be used for developing aknowledged base expert system.
No funding received.
Availability of data and materials
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
SY and MZ conceived of the presented idea, developed the theory, and performed the computations. They also verified the analytical methods. All authors discussed the results and contributed to the final manuscript. Both authors read and approved the final manuscript.
The newness/scientific contributions of the work can be summarized as follows:
1- The effect of processing parameters on the variations of dimensional accuracy in dry hard turning operation (especially for hardened MDN250) has been studied for the first time.
2- The relationship between tool wear, cutting forces, and vibration and also their effects on the dimensional accuracy have been studied for the first time in dry hard turning operation.
3- Reported obtained surface roughness of 0.312 μμm, as super-finishing, has not been reported until now during hard turning operation. This research work has industrial application for machinist and machining researchers. In addition, due to the importance of hardened MDN250 μ machining in the automotive, and aerospace industries, the obtained results are completely valuable for engineers and process planers.
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- Asiltürk, I., & Akkuş, H. (2011). Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement, 44, 1697–1704.Google Scholar
- Aslan, E., Camuşcu, N., & Birgören, B. (2007). Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al 2 O 3+ TiCN mixed ceramic tool. Materials & Design, 28, 1618–1622.View ArticleGoogle Scholar
- Bartarya, G., & Choudhury, S. K. (2012). State of the art in hard turning. International Journal of Machine Tools and Manufacture, 53, 1–14.View ArticleGoogle Scholar
- Chavoshi, S. Z., & Tajdari, M. (2010). Surface roughness modelling in the hard turning operation of AISI 4140 using CBN cutting tool. International Journal of Material Forming, 3, 233–239.View ArticleGoogle Scholar
- Das, S. R., Dhupal, D., & Kumar, A. (2015). Experimental investigation into machinability of hardened AISI 4140 steel using TiN coated ceramic tool. Measurement, 62, 108–126.View ArticleGoogle Scholar
- Dhar, N. R., Islam, M. W., Islam, S., & Mithu, M. A. H. (2006). The influence of minimum quantity of lubrication (MQL) on cutting temperature, chip and dimensional accuracy in turning AISI-1040 steel. Journal of Materials Processing Technology, 171(1), 93–99.View ArticleGoogle Scholar
- Dhar, N. R., Islam, S., & Kamruzzaman, M. (2007). Effect of minimum quantity lubrication (MQL) on tool wear, surface roughness and dimensional deviation in turning AISI-4340 steel. Gazi University Journal of Science, 20(2), 23–32.Google Scholar
- Dhar, N. R., & Kamruzzaman, M. (2007). Cutting temperature, tool wear, surface roughness and dimensional deviation in turning AISI-4037 steel under cryogenic condition. International Journal of Machine Tools and Manufacture, 47(5), 754–759.View ArticleGoogle Scholar
- Dhar, N. R., Paul, S., & Chattopadhyay, A. B. (2002). Machining of AISI 4140 steel under cryogenic cooling—tool wear, surface roughness and dimensional deviation. Journal of Materials Processing Technology, 123(3), 483–489.View ArticleGoogle Scholar
- Dossett, J. L., & Boyer, H. E. (2006). Practical heat-treating. ASM International.Google Scholar
- Elbah, M., Yallese, M. A., Aouici, H., Mabrouki, T., & Rigal, J. F. (2011). Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel. Measurement, 46, 3041–3056.View ArticleGoogle Scholar
- Khrais, S. K., & Lin, Y. J. (2007). Wear mechanisms and tool performance of TiAlN PVD coated inserts during machining of AISI 4140 steel. Wear, 262, 64–69.View ArticleGoogle Scholar
- Lalwani, D. I., Mehta, N. K., & Jain, P. K. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of Materials Processing Technology, 206, 167–179.View ArticleGoogle Scholar
- Özel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4–5), 467–479.View ArticleGoogle Scholar
- Revel, P., Jouini, N., Thoquenne, G., & Lefebvre, F. (2016). High precision hard turning of AISI 52100 bearing steel. Precision Engineering, 43, 24–33.View ArticleGoogle Scholar
- Risbood, K. A., Dixit, U. S., & Sahasrabudhe, A. D. (2003). Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. Journal of Materials Processing Technology, 132(1), 203–214.View ArticleGoogle Scholar
- Saini, S., Inderpreet, A., & Vishal, S. (2012). Residual stresses, surface roughness, and tool wear in hard turning: a comprehensive review. Materials and Manufacturing Processes, 27, 583–598.View ArticleGoogle Scholar
- Sandvik Coromant. (2010). Metal working products – General turning. Viernheim: AB Sandvik Coromant, Sandvik Automation GmbH.Google Scholar
- Shahabi, H. H., & Ratnam, M. M. (2010). Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach. The International Journal of Advanced Manufacturing Technology, 48(1–4), 213–226.View ArticleGoogle Scholar
- Thiele, J. D., & Melkote, S. N. (1999). Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel. Journal of Materials Processing Technology, 94(2–3), 216–226.View ArticleGoogle Scholar
- Yousefi, S., & Zohoor, M. (2018). Experimental studying of the variations of surface roughness and dimensional accuracy in dry hard turning operation. The Open Mechanical Engineering Journal, 12, 175–191.View ArticleGoogle Scholar
- Yousefi, S., Zohoor, M., & Faraji, M. (2017). The variations of dimensional accuracy in dry hard turning operation. In 25th annual international conference on mechanical engineering ISME2017.Google Scholar
- Zohoor, M., & Yousefi, S. (2018). Experimental investigation of the effect of processing parameters on the surface roughness operation for using as expert system database. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(5), 273.View ArticleGoogle Scholar