Academia.eduAcademia.edu

Figure 2 – uploaded by Karam Maalawi

See full PDFdownloadDownload figure
TABLE |.—Chemical composition of HSS tools of 66 HRC hardness. FiGurE 1|.—Experimental setup. TABLE 2.—The mechanical properties of St. 44.

Figure 1 |.—Chemical composition of HSS tools of 66 HRC hardness. FiGurE 1|.—Experimental setup. TABLE 2.—The mechanical properties of St. 44.

Related Figures (48)

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.
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.
Related topics:
Materials EngineeringManufacturing EngineeringTool wearTool lifeParameter Optimization
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved