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Title
Abstract
Figures
Introduction
Experimental Work
Tool Wear Results
Force Results
Surface Roughness Results
Conclusion
References
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Engineering
Mechanical Engineering

Cutting Parameter Optimization when Machining Different Materials

Profile image of Karam MaalawiKaram Maalawi

2010, Materials and Manufacturing Processes

https://doi.org/10.1080/10426914.2010.480998
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Abstract
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This research focuses on optimizing cutting parameters in the machining of different materials to improve tool life and reduce production costs and times. The study reviews previous literature on cutting forces, tool wear, and surface roughness, highlighting the importance of selecting appropriate parameters for economic machining. Through a series of optimization models conducted on diverse materials with varying hardness levels, the findings present optimized cutting conditions that enhance tool efficiency and machining effectiveness.

Figures (49)
FiGuRE 2.—Data acquisition card.
FiGuRE 2.—Data acquisition card.
ranged between 0° to 15° and approach angle values (A) ranged between 30° to 90° were used in these experiments. Experiments were conducted on workpiece materials St. 44. The mechanical properties of workpiece materials were tested in the Iron & Steel Company In Helwan, Cairo, Egypt, while the hardness was measured using Hardness Tester for Rockwell B and presented in Table 2.
ranged between 0° to 15° and approach angle values (A) ranged between 30° to 90° were used in these experiments. Experiments were conducted on workpiece materials St. 44. The mechanical properties of workpiece materials were tested in the Iron & Steel Company In Helwan, Cairo, Egypt, while the hardness was measured using Hardness Tester for Rockwell B and presented in Table 2.
TABLE |.—Chemical composition of HSS tools of 66 HRC hardness. FiGurE 1|.—Experimental setup. TABLE 2.—The mechanical properties of St. 44.
TABLE |.—Chemical composition of HSS tools of 66 HRC hardness. FiGurE 1|.—Experimental setup. TABLE 2.—The mechanical properties of St. 44.
FiGurE 6.—Tool wear for different approach angle values: V = 54 m/min. f =0.12 mm/rev, d = 0.8 mm, and a = 9°.
FiGurE 6.—Tool wear for different approach angle values: V = 54 m/min. f =0.12 mm/rev, d = 0.8 mm, and a = 9°.
FiGuRE 7.—Tool wear for different rake angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°.
FiGuRE 7.—Tool wear for different rake angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°.
FiGureE 5.—Tool wear for different depth of cuts: V = 54m/min, f = 0.12 mm/rev, A = 45°, and a = 9°. Experiments were conducted using different cutting conditions and tool life was measured based on wear criterion (W.C. = 0.6mm). Figures 3-7 show the
FiGureE 5.—Tool wear for different depth of cuts: V = 54m/min, f = 0.12 mm/rev, A = 45°, and a = 9°. Experiments were conducted using different cutting conditions and tool life was measured based on wear criterion (W.C. = 0.6mm). Figures 3-7 show the
FIGURE 3.—Tool wear for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FIGURE 3.—Tool wear for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FicurE 4.—Tool wear for different feed rates: V = 54 m/min, d = 0.8mm, A= 45°, anda = 9°.
FicurE 4.—Tool wear for different feed rates: V = 54 m/min, d = 0.8mm, A= 45°, anda = 9°.
FiGurE 13.—Relations between cutting force and depth of cut. Ficure 12.—Cutting force vs. time for different depths of cut: V = 54 m/min, f =0.12 mm/rev, A = 45°, and a = 9°.
FiGurE 13.—Relations between cutting force and depth of cut. Ficure 12.—Cutting force vs. time for different depths of cut: V = 54 m/min, f =0.12 mm/rev, A = 45°, and a = 9°.
Ficure 10.—Cutting force vs. time for different feed rates: V = 54 m/min. d=0.8mm, A = 45°, anda = 9°. FiGurE 9.—Relations between cutting force and cutting speed.
Ficure 10.—Cutting force vs. time for different feed rates: V = 54 m/min. d=0.8mm, A = 45°, anda = 9°. FiGurE 9.—Relations between cutting force and cutting speed.
*IGURE 1|1.—Relations between cutting force and feed rate.
*IGURE 1|1.—Relations between cutting force and feed rate.
FiGuRE 8.—Cutting force vs. time for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FiGuRE 8.—Cutting force vs. time for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FiGuRE 19.—Relation between surface roughness and cutting speed. Ficure 18.—Surface roughness vs. time for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FiGuRE 19.—Relation between surface roughness and cutting speed. Ficure 18.—Surface roughness vs. time for different cutting speeds: f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
Ficure 16.—Cutting force vs. time for different rake angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°. FiGurE 15.—Relations between cutting force and approach angle.
Ficure 16.—Cutting force vs. time for different rake angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°. FiGurE 15.—Relations between cutting force and approach angle.
FicurE 14.—Cutting force vs. time for different approach angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and a = 9°.
FicurE 14.—Cutting force vs. time for different approach angle values: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, and a = 9°.
FiGuRE 17.—Relations between cutting force and rake angle.
FiGuRE 17.—Relations between cutting force and rake angle.
FiGurE 25.—Relation between surface roughness and approach angle. Ficure 24.—Surface roughness vs. time for different approach angle values: V = 54m/min, f = 0.12 mm/rev, d = 0.8mm, and a = 9°.
FiGurE 25.—Relation between surface roughness and approach angle. Ficure 24.—Surface roughness vs. time for different approach angle values: V = 54m/min, f = 0.12 mm/rev, d = 0.8mm, and a = 9°.
FiGuRE 23.—Relation between surface roughness and depth of cut.
FiGuRE 23.—Relation between surface roughness and depth of cut.
Ficure 20.—Surface roughness vs. time for different feeds: V = 54 m/min, d=0.8mm, A = 45°, anda = 9°.
Ficure 20.—Surface roughness vs. time for different feeds: V = 54 m/min, d=0.8mm, A = 45°, anda = 9°.
FiGurE 21.—Relation between surface roughness and feed rate.
FiGurE 21.—Relation between surface roughness and feed rate.
FiGure 22.—Surface roughness vs. time for different depth of cuts: V = 54 m/min, f = 0.12 mm/rev, A = 45°, and a = 9°.
FiGure 22.—Surface roughness vs. time for different depth of cuts: V = 54 m/min, f = 0.12 mm/rev, A = 45°, and a = 9°.
TABLE 3c.—T-test of coefficients for Fw model.
TABLE 3c.—T-test of coefficients for Fw model.
Predictors: log C, log t, log a, log d, log f, log A, log V. TABLE 3b.—Model summary for Fw model.
Predictors: log C, log t, log a, log d, log f, log A, log V. TABLE 3b.—Model summary for Fw model.
TABLE 3a.—ANOVA for Fw model. Predictors: log C, log t, log a, log d, log f, log A, log V. Dependent variable: log Fw.
TABLE 3a.—ANOVA for Fw model. Predictors: log C, log t, log a, log d, log f, log A, log V. Dependent variable: log Fw.
FiGureE 27.—Relation between surface roughness and rake angle.
FiGureE 27.—Relation between surface roughness and rake angle.
Dependent variable: log Fw. FiGuRE 26.—Surface roughness vs. time for different rake angle values: St. 44, V = 54m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°.
Dependent variable: log Fw. FiGuRE 26.—Surface roughness vs. time for different rake angle values: St. 44, V = 54m/min, f = 0.12 mm/rev, d = 0.8mm, and A = 45°.
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed). TABLE 6.—Correlation between input variables and Fw, Fc, and Ra responses.
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed). TABLE 6.—Correlation between input variables and Fw, Fc, and Ra responses.
TABLE 5.—The surface roughness (Ra) model coefficients.
TABLE 5.—The surface roughness (Ra) model coefficients.
TABLE 4.—The cutting force (Fc) model coefficients.
TABLE 4.—The cutting force (Fc) model coefficients.
FIGURE 28.—The residual plots for the flank wear response.
FIGURE 28.—The residual plots for the flank wear response.
FiGurE 29.—Comparison between time-dependent Fw and Fe responses: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FiGurE 29.—Comparison between time-dependent Fw and Fe responses: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
TABLE 8a.—ANOVA for T model. Predictors: log C, log h, log d, log a, log A, log f, log V. Dependent variable: log T.
TABLE 8a.—ANOVA for T model. Predictors: log C, log h, log d, log a, log A, log f, log V. Dependent variable: log T.
Predictors: log C, log h, log d, log a, log A, log f, log V. TABLE 8b.—Model summary for T model.
Predictors: log C, log h, log d, log a, log A, log f, log V. TABLE 8b.—Model summary for T model.
TABLE 9a.—ANOVA for Fe model. Predictors: log C, log h, log d, log a, log 4, log f, log V. Dependent variable: log Fc.
TABLE 9a.—ANOVA for Fe model. Predictors: log C, log h, log d, log a, log 4, log f, log V. Dependent variable: log Fc.
TABLE 7.—Time dependency of Fc and Ra responses. it is better to have a new criterion for regrinding the tool. As the cutting force and surface roughness can represent the tool wear as a criterion for tool change, i.e., from the product quality view point in this case, choosing surface roughness as a criterion for regrinding the tool is more beneficial from measuring the tool flank wear. Also, it is easier to measure the surface roughness than to measure the flank wear. In the example shown in Table 7, it is better to have surface roughness criterion of 8.2 4m than to take flank wear criterion of 0.42 mm.
TABLE 7.—Time dependency of Fc and Ra responses. it is better to have a new criterion for regrinding the tool. As the cutting force and surface roughness can represent the tool wear as a criterion for tool change, i.e., from the product quality view point in this case, choosing surface roughness as a criterion for regrinding the tool is more beneficial from measuring the tool flank wear. Also, it is easier to measure the surface roughness than to measure the flank wear. In the example shown in Table 7, it is better to have surface roughness criterion of 8.2 4m than to take flank wear criterion of 0.42 mm.
FIGURE 30.—Comparison between time-dependent Fw and Ra responses: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
FIGURE 30.—Comparison between time-dependent Fw and Ra responses: V = 54 m/min, f = 0.12 mm/rev, d = 0.8mm, A = 45°, and a = 9°.
Predictors: log C, log h, log d, log a, log A, log f, log V. TABLE 9b.—Model summary for Fe model.
Predictors: log C, log h, log d, log a, log A, log f, log V. TABLE 9b.—Model summary for Fe model.
Predictors: log C, log h, log d, log a, log f, log A, log V. TABLE 10b.—Model summary for Ra model.
Predictors: log C, log h, log d, log a, log f, log A, log V. TABLE 10b.—Model summary for Ra model.
“Correlation is significant at the 0.01 level (2-tailed). TABLE 1|1.—Correlation between input variables and Fc, Ra, and T responses.
“Correlation is significant at the 0.01 level (2-tailed). TABLE 1|1.—Correlation between input variables and Fc, Ra, and T responses.
TABLE 10a.—ANOVA for Ra model. Predictors: log C, log h, log d, log a, log f, log A, log V. Dependent variable: log Ra.
TABLE 10a.—ANOVA for Ra model. Predictors: log C, log h, log d, log a, log f, log A, log V. Dependent variable: log Ra.

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INVESTIGATION TO EFFECTS OF CUTTING PARAMETER ON TOOL WEAR, TOOL LIFE AND MACHINABILITY IN MACHINING OPERATION
IJASEM Internatioanl, Yunus Kartal

In this study, it is aimed to investigate the surface roughness of cutting tools during the machining of AISI 304 stainless steels, the life of cutting tools and the cutting forces that occur during cutting and machining. The main features of stainless steels are: high corrosion resistance, ductility and high tensile strength. Stainless steels contain elements such as chromium, nickel and molybdenum which affect the machinability in the negative direction; therefore it is very difficult to process stainless steels. This work was carried out using covered cutting tools. The work was carried out at different cutting depths and different feed rates without the use of cooling liquid. The life and wear mechanisms of cutting tools, cutting forces and surface roughness were investigated in relation to cutting parameters.

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Optimization of Tool Wear: A Review
Rohit Saini

2014

I. INTRODUCTION Research in metal cutting was started with Cocquilhat in 1851 which measured the work required to remove a given volume of material in drilling. The attempt made by Time led to the explanation of formation of chips in 1870 and further research was made by Tresca in 1873. Later in 1881, Mallock suggested that the cutting process was the shearing of workpiece to form the chip and emphasized the importance of the effect of friction occurring on the cutting tool face as the chip was removed. Further, Taylor investigated the effect of tool material and cutting conditions on tool life during rough operations. Latest fundamental work has been carried out by Ernst and Merchant in 1941 dealing with the mechanics of metal cutting process. The simplest and most widely used model for cutting was first by Ernest and Merchant (1941) and further contribution to study of Ernest and Merchant theory was done by Lee and Shaffer (1951), Kobayashi and Thomsen (1962). Large number of literature is available on the determination of chip-tool interface temperature, factors affecting the interface temperature and techniques of optimization of machining parameters including cutting speed, feed rate, cutting zone temperature, etc. Armerago (1969), Boothroyd (1981), Shaw (1984) and Trent (2000) wrote the most widely used text books. Kalpakjian, et al. (2006), and DeGarmo, et al. (1997) wrote books on more general introductory knowledge. The study machining process by experimental approach is expensive and time consuming peculiarly when a wide range of parameters is included like tool geometry, cutting conditions, and materials. II. LITERATURE REVIEW Jensen M.R. et al. [1] stated that an important problem in the machining of drawn parts is tool wear. They said that tool wear can be reduced by increasing the tool lifetime and by making more continuous production flow, by reducing the number of breakdowns when the tools have to be re-polished. In their paper, an optimization of the shape of the draw-die profile with regard to wear was carried out using a conventional optimization method and explicit finite element. The optimized draw-die profile had almost twice the tool life compared to that of the initial circular draw-die, if the peak value of wear was used as the wear criteria. The relatively small resources used by them with their optimization approach made the tool wear design relevant for industrial use at the tool-design state. Sullivan D.O' et al. [2] determined the temperature in a single point turning process. The total work done by a cutting tool in removing metal was determined from the force components on the cutting tool. Approximately, all of this work or energy is converted into heat which is dissipated into the chip, tool and workpiece material. The wear of the tool is related to the cutting forces. Initial experiments conducted involved the simultaneous measurement of forces and temperatures. These experiments focused on the use of embedded thermocouple (in the work piece) and using the infrared thermal camera to monitor the process. They concluded that in machining of aluminium Al 6082-T6 the decreased cutting tool forces and machined surface temperatures was resulted by increased cutting speed (V C) and increased cutting tool forces and machined

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Effect of machining variables on tool life
Sunday Ojolo

This work involves the investigation carried out to study the effects of machining parameters on tool life under dry machining environment. Three cutting tool materials (HSS blank tool -M2 C66, tungsten carbide insert tool grade P-10, DMNG carbide insert tool 150412-SA) and work materials (medium carbon steel 0.4 wt% C, mild steel 0.29 wt% C, brass C330) were examined. The experiments were conducted under three different spindle speeds (900, 1120, 1400rev/min); feed rates (0.1, 0.2, 0.3mm/rev) and depths of cut (0.5, 1.0, 1.5mm). The settings of machining parameters were determined by using the Taguchi experimental design method. The level of importance of the machining parameters on tool life was determined by using analysis of variance (ANOVA). The optimum machining parameters combination was obtained by using the analysis of signal-to-noise (S/N) ratio. The relationship between cutting parameters and tool life was obtained. From the results, the spindle speed had the most significant effects on tool life followed by feed rate and the depth of cut. The life of the HSS when cutting the three work pieces (medium carbon steel, mild steel and brass) was 161s, 321s and 386s respectively. The life of tungsten carbide when cutting the three work materials was 480s, 726s and 1028s respectively. The life of DMNG carbide were 782s using medium carbon steel, 864s using mild steel, and 1183s using brass. The shortest life of the three cutting tool materials (HSS, tungsten carbide and DMNG carbide) on the three work material (medium carbon steel, mild steel and brass) occurred at cutting speed (1400 rev/min), feed rate (0.3 mm/rev) and depth of cut (1.5 mm), where the life of the HSS were (15s using medium carbon steel, 58s using mild steel, 94s using brass). The life of tungsten carbide were (135s using medium carbon steel, 180s using mild steel, 274s using brass) and the life of DMNG carbide were (219s using medium carbon steel, 215s using mild steel, 311s using brass). The increment of spindle speed, feed rate and depth of cut value mostly will affect the tool life.

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The assessment of cutting tool wear
Viktor Astakhov

International Journal of Machine Tools and Manufacture, 2004

Flank wear of cutting tools is often selected as the tool life criterion because it determines the diametric accuracy of machining, its stability and reliability. This paper argues that the existing criteria of flank wear are insufficient for its proper characterization. Their existence is due to the lack of knowledge on the contact conditions at the tool flank-workpiece interface. Known attempts to evaluate the physical processes at this interface do not help to resolve this issue. This paper compares different characteristics of the evaluation of flank wear. The contact process at the mentioned interface is analyzed through the experimental assessment of the contact stresses, and the full validity of Makarow's law is confirmed, i.e. minimum tool wear occurs at the optimum cutting speed. A new concept of tool resources is proposed and discussed. This resource is defined as the limiting amount of energy that can be transmitted through the cutting wedge until it fails.

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A study of effects of machining parameters on tool life
Roy Haris Marantika

This work involves the investigation carried out to study the effects of machining parameters on tool life under dry machining environment. Three cutting tool materials (HSS blank tool-M2 C66, tungsten carbide insert tool grade P-10, DMNG carbide insert tool 150412-SA) and work materials (medium carbon steel 0.4 wt% C, mild steel 0.29 wt% C, brass C330) were examined. The experiments were conducted under three different spindle speeds (900, 1120, 1400rev/min); feed rates (0.1, 0.2, 0.3mm/rev) and depths of cut (0.5, 1.0, 1.5mm). The settings of machining parameters were determined by using the Taguchi experimental design method. The level of importance of the machining parameters on tool life was determined by using analysis of variance (ANOVA). The optimum machining parameters combination was obtained by using the analysis of signal-to-noise (S/N) ratio. The relationship between cutting parameters and tool life was obtained. From the results, the spindle speed had the most significant effects on tool life followed by feed rate and the depth of cut. The life of the HSS when cutting the three work pieces (medium carbon steel, mild steel and brass) was 161s, 321s and 386s respectively. The life of tungsten carbide when cutting the three work materials was 480s, 726s and 1028s respectively. The life of DMNG carbide were 782s using medium carbon steel, 864s using mild steel, and 1183s using brass. The shortest life of the three cutting tool materials (HSS, tungsten carbide and DMNG carbide) on the three work material (medium carbon steel, mild steel and brass) occurred at cutting speed (1400 rev/min), feed rate (0.3 mm/rev) and depth of cut (1.5 mm), where the life of the HSS were (15s using medium carbon steel, 58s using mild steel, 94s using brass). The life of tungsten carbide were (135s using medium carbon steel, 180s using mild steel, 274s using brass) and the life of DMNG carbide were (219s using medium carbon steel, 215s using mild steel, 311s using brass). The increment of spindle speed, feed rate and depth of cut value mostly will affect the tool life.

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Related topics

  • Materials Engineering
  • Manufacturing Engineering
  • Tool wear
  • Tool life
  • Parameter Optimization

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