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Online since: April 2008
Authors: Angelina Stoyanova, Reni Iordanova, Yanko B. Dimitriev, Lyubomir Aleksandrov
The spectrum of 90MoO3.10Nd2O3 glass is characterized by a strong band at 880 cm-1 and a not well resolved band near 720 cm-1.
The spectrum of 75MoO3.25Bi2O3 glass possesses a strong absorption band at 760 cm-1, shoulders at 920 cm-1 and 820 cm-1 and weak bands below 600 cm-1.
Fig. 1.
The main band at 760 cm-1 with the shoulders at 820 and 920 cm-1 are related to the vibration of distorted MoO4 units [3].
Ghem., Vol. 479 (1981), p. 229 [14] Y.
The spectrum of 75MoO3.25Bi2O3 glass possesses a strong absorption band at 760 cm-1, shoulders at 920 cm-1 and 820 cm-1 and weak bands below 600 cm-1.
Fig. 1.
The main band at 760 cm-1 with the shoulders at 820 and 920 cm-1 are related to the vibration of distorted MoO4 units [3].
Ghem., Vol. 479 (1981), p. 229 [14] Y.
Online since: January 2016
Authors: Ming Jie Fu, Xiu Quan Han
.%) alloy sheet under the conditions of 940~1000℃ and 5.5×10-5s-1~1.7×10-3s-1 were studied.
Summary 1.
References [1] J.W.
Metals. 20(s.1)(2010)s336-341
Bampton, Evolution of microstructure and superplastic deformation mechanism in super α2 Ti3Al base alloys, Acta Mater. 46(2)(1998)465-479
Summary 1.
References [1] J.W.
Metals. 20(s.1)(2010)s336-341
Bampton, Evolution of microstructure and superplastic deformation mechanism in super α2 Ti3Al base alloys, Acta Mater. 46(2)(1998)465-479
Online since: July 2011
Authors: Chun Xiang Lin, Ming Hua Liu, Yi Hao
From a plot of 1/qe vs 1/Ce, the Q0 and b can be obtained from its slope and intercept.
The slope 1/n is a measure of the adsorption intensity.
Table 3 Kinetic parameters B2 (h-1) Di(cm-1/h) kf(cm/min) ks(cm·g/mL·min) kfav((h-1) H(mL/g) ksavH(h-1) Kf·av (h-1) Kf (cm/min) 1.276 3.231×10-4 16.6 1.78×10-4 29880 8729 2796.8 2557.4 1.42 Treatment of tannery wastewater.
References [1] Lee T., Lim H., Lee Y., Park J.
-W., Use of waste iron metal for removal of Cr(VI) from water, Chemosphere, 53 (2003) 479-485 [2] Alguacil F.
The slope 1/n is a measure of the adsorption intensity.
Table 3 Kinetic parameters B2 (h-1) Di(cm-1/h) kf(cm/min) ks(cm·g/mL·min) kfav((h-1) H(mL/g) ksavH(h-1) Kf·av (h-1) Kf (cm/min) 1.276 3.231×10-4 16.6 1.78×10-4 29880 8729 2796.8 2557.4 1.42 Treatment of tannery wastewater.
References [1] Lee T., Lim H., Lee Y., Park J.
-W., Use of waste iron metal for removal of Cr(VI) from water, Chemosphere, 53 (2003) 479-485 [2] Alguacil F.
Online since: July 2011
Authors: Li Hua
Figures of 0, 1, 2 and 3 respectively represent percentages of different elements, with the margin made up with copper and other ingredients unchanged. 4 groups of tests were carried out with the data shown below:
Group 1: Design of carcass recipe conducted with the orthogonal table (in Table 1)
Table 1 Test results of Group 1
1-Factors;2-Level;3-No.Range
Factors
Level
No.
483.4 476.6 409.4 499.4 523.6 545.2 464.2 440.4 467 512.2 527 495.2 411 473.6 499.8 532.8 524.6 Range 72.8 135.8 86.6 121.8 Group 2: (test results shown in Table 2) Table 2 Test results of Group 2 1-Metal;2-Content;3-Temperature Metal Content Temperature Cu (%) Sn (%) Ni (%) Cr (%) HRB σ (Mpa) 710 84 8 6 2 50 519.5 710 82 8 8 2 56.6 479 780 83 6 8 3 58.1 551 710 79 8 10 3 67.9 493.7 780 82 10 6 2 59.4 555.6 Group 3: (Test results shown in Table 3) Table 3 Test results of Group 3 1-Elements;2-Level;3-No.
Table 5 shows only No.1 and No.5 had coordinated hardness and strength.
In the recipe of Group 1, we added Fe content of 10%, 15%, 20% and 25% respectively.
We carried out 1 group with the iron content the same iron as that of Group 1, and adjusted Sn:Ni:Cr into 8:10:2.
483.4 476.6 409.4 499.4 523.6 545.2 464.2 440.4 467 512.2 527 495.2 411 473.6 499.8 532.8 524.6 Range 72.8 135.8 86.6 121.8 Group 2: (test results shown in Table 2) Table 2 Test results of Group 2 1-Metal;2-Content;3-Temperature Metal Content Temperature Cu (%) Sn (%) Ni (%) Cr (%) HRB σ (Mpa) 710 84 8 6 2 50 519.5 710 82 8 8 2 56.6 479 780 83 6 8 3 58.1 551 710 79 8 10 3 67.9 493.7 780 82 10 6 2 59.4 555.6 Group 3: (Test results shown in Table 3) Table 3 Test results of Group 3 1-Elements;2-Level;3-No.
Table 5 shows only No.1 and No.5 had coordinated hardness and strength.
In the recipe of Group 1, we added Fe content of 10%, 15%, 20% and 25% respectively.
We carried out 1 group with the iron content the same iron as that of Group 1, and adjusted Sn:Ni:Cr into 8:10:2.
Online since: January 2016
Authors: Rosniza Hussin, Nur Syahraain Zulkiflee
Fig. 1 shows the XRD spectra for TiO2 (as)/ZnO thin films.
The intensity of the thin films show TiO2 appear with anatase phase at typical peaks around 2θ = 25.3, which are attributed to the (1 0 1) plane matched with the JCPDS file (00-021-1272) and ZnO appear with zincite phase at 2θ = 31.8, 34.3, 36.3, 47.5, 56.6, 62.9, 66.4, 68, 69.1 which are attributed to the (1 0 0), (0 0 2), (1 0 1), (1 0 2), (1 1 0), (1 0 3), (2 0 0), (1 1 2) and (2 0 1) planes, respectively matched with the JCPDS file (00-036-1451).
Fig. 2(c) show the bilayer TiO2 (400oC)/ZnO calcined at 600 oC appear with anatase phase at 2θ = 25.3, 37.9, and 53.9 which are attributed to (1 0 1), (0 0 4), (1 0 5) planes.
References [1] J.
Sci. 351 (2015) 474-479 [5] L.J.
The intensity of the thin films show TiO2 appear with anatase phase at typical peaks around 2θ = 25.3, which are attributed to the (1 0 1) plane matched with the JCPDS file (00-021-1272) and ZnO appear with zincite phase at 2θ = 31.8, 34.3, 36.3, 47.5, 56.6, 62.9, 66.4, 68, 69.1 which are attributed to the (1 0 0), (0 0 2), (1 0 1), (1 0 2), (1 1 0), (1 0 3), (2 0 0), (1 1 2) and (2 0 1) planes, respectively matched with the JCPDS file (00-036-1451).
Fig. 2(c) show the bilayer TiO2 (400oC)/ZnO calcined at 600 oC appear with anatase phase at 2θ = 25.3, 37.9, and 53.9 which are attributed to (1 0 1), (0 0 4), (1 0 5) planes.
References [1] J.
Sci. 351 (2015) 474-479 [5] L.J.
Statistical Approach for the Development of Tangential Cutting Force Model in End Milling of Ti6Al4V
Online since: June 2011
Authors: A.K.M. Nurul Amin, Anayet Ullah Patwari, Waleed Fekry Faris
Fig. 1 Experimental set up for end milling
Kistler Rotating Cutting Force Dynamometer was used for measuring cutting forces.
Type Coding of Level Cutting Forces (N) x1 x2 x3 Cutting speed, m/min Axial Depth of cut, mm Feed, mm/tooth 1 Factorial 1 1 -1 410 2 Factorial 1 -1 1 605 3 Factorial -1 1 1 889 4 Factorial -1 -1 -1 342 5 Centre 0 0 0 500 6 Centre 0 0 0 503 7 Centre 0 0 0 503 8 Centre 0 0 0 502 9 Centre 0 0 0 503 10 Axial -1.414 0 0 515 11 Axial 1.414 0 0 450 12 Axial 0 -1.414 0 415 13 Axial 0 1.414 0 652 14 Axial 0 0 -1.414 325 15 Axial 0 0 1.414 905 In the experiment, small central composite design was used to develop the tangential cutting force model.
Table 4 ANOVA for second order CCD model Source Sum of Squares DF Mean Square F Value Prob > F Block 6.75E-05 1 6.75E-05 Model 1.196514 8 0.149564 15826.31 < 0.0001 Significant x1 0.019437 1 0.019437 2056.719 < 0.0001 x2 0.102046 1 0.102046 10798.14 < 0.0001 x3 0.5244 1 0.5244 55490.01 < 0.0001 x12 0.002589 1 0.002589 273.9307 < 0.0001 X22 0.003045 1 0.003045 322.1804 < 0.0001 x32 0.012612 1 0.012612 1334.583 < 0.0001 x1 x2 0.001351 1 0.001351 142.9317 < 0.0001 x2 x3 0.00066 1 0.00066 69.86719 0.0004 Residual 4.73E-05 5 9.45E-06 Lack of Fit 2.02E-05 1 2.02E-05 2.991322 0.1588 Non-significant Pure Error 2.7E-05 4 6.76E-06 Cor Total 1.196629 14 The quadratic CCD model shows that feed has the most significant effect on tangential cutting force, followed by axial depth of cut and cutting speed.
References [1] X.P.
Tools Manuf. 31 (4) (1991) 471–479
Type Coding of Level Cutting Forces (N) x1 x2 x3 Cutting speed, m/min Axial Depth of cut, mm Feed, mm/tooth 1 Factorial 1 1 -1 410 2 Factorial 1 -1 1 605 3 Factorial -1 1 1 889 4 Factorial -1 -1 -1 342 5 Centre 0 0 0 500 6 Centre 0 0 0 503 7 Centre 0 0 0 503 8 Centre 0 0 0 502 9 Centre 0 0 0 503 10 Axial -1.414 0 0 515 11 Axial 1.414 0 0 450 12 Axial 0 -1.414 0 415 13 Axial 0 1.414 0 652 14 Axial 0 0 -1.414 325 15 Axial 0 0 1.414 905 In the experiment, small central composite design was used to develop the tangential cutting force model.
Table 4 ANOVA for second order CCD model Source Sum of Squares DF Mean Square F Value Prob > F Block 6.75E-05 1 6.75E-05 Model 1.196514 8 0.149564 15826.31 < 0.0001 Significant x1 0.019437 1 0.019437 2056.719 < 0.0001 x2 0.102046 1 0.102046 10798.14 < 0.0001 x3 0.5244 1 0.5244 55490.01 < 0.0001 x12 0.002589 1 0.002589 273.9307 < 0.0001 X22 0.003045 1 0.003045 322.1804 < 0.0001 x32 0.012612 1 0.012612 1334.583 < 0.0001 x1 x2 0.001351 1 0.001351 142.9317 < 0.0001 x2 x3 0.00066 1 0.00066 69.86719 0.0004 Residual 4.73E-05 5 9.45E-06 Lack of Fit 2.02E-05 1 2.02E-05 2.991322 0.1588 Non-significant Pure Error 2.7E-05 4 6.76E-06 Cor Total 1.196629 14 The quadratic CCD model shows that feed has the most significant effect on tangential cutting force, followed by axial depth of cut and cutting speed.
References [1] X.P.
Tools Manuf. 31 (4) (1991) 471–479
Online since: September 2013
Authors: Hao Chen, Jie Huang, Kai Chai, Mei Jun Zhang
Total 400 group 9d (8d IMF component C1 ~ C8 and 1d surplus R) normalized energy as a sample characteristic vectors show in fig.1
Table 2 SVM binary classification and recognition results of bearing normal and fault kernel function category c g training sample test sample erroneous judgement classification accuracy linear kernel 1 1 200 group 9d 200 group 9d 0 group 100% polynomial kernel 1 1 200 group 9d 200 group 9d 0 group 100% RBF kernel 1 1 200 group 9d 200 group 9d 18 groups 91.0% Sigmoid kernel 1 1 200 group 9d 200 group 9d 50 groups 75.0% Compared high dimension large sample in table 2 with low dimensional small sample in table 1,SVM based on linear kernel function and polynomial kernel function is the same results.The classification accuracy using RBF kernel function and Sigmoid nuclear the function slightly less at low dimensional small sample.But there is no obvious difference at low dimensional small sample compared to high dimensional large sample.It shows that combination improved EEMD with SVM can realize the low dimensional small sample of correct fault diagnosis.
References [1] Hwi Li,Xiaofeng Liu and Lin Bo.
Advances in Adaptive Data Analysis,Vol.1(2009),p1-41 [5] Meijun Zhang,Sichen Han,Chuang Wang and Shuguang Li.
Journal of Advanced Materials Research.Vol. 479-481(2012) , p1180-1185 [6] Zhang Meijun ,Chen Hao , Cao Qin and Chuang Wang .
Table 2 SVM binary classification and recognition results of bearing normal and fault kernel function category c g training sample test sample erroneous judgement classification accuracy linear kernel 1 1 200 group 9d 200 group 9d 0 group 100% polynomial kernel 1 1 200 group 9d 200 group 9d 0 group 100% RBF kernel 1 1 200 group 9d 200 group 9d 18 groups 91.0% Sigmoid kernel 1 1 200 group 9d 200 group 9d 50 groups 75.0% Compared high dimension large sample in table 2 with low dimensional small sample in table 1,SVM based on linear kernel function and polynomial kernel function is the same results.The classification accuracy using RBF kernel function and Sigmoid nuclear the function slightly less at low dimensional small sample.But there is no obvious difference at low dimensional small sample compared to high dimensional large sample.It shows that combination improved EEMD with SVM can realize the low dimensional small sample of correct fault diagnosis.
References [1] Hwi Li,Xiaofeng Liu and Lin Bo.
Advances in Adaptive Data Analysis,Vol.1(2009),p1-41 [5] Meijun Zhang,Sichen Han,Chuang Wang and Shuguang Li.
Journal of Advanced Materials Research.Vol. 479-481(2012) , p1180-1185 [6] Zhang Meijun ,Chen Hao , Cao Qin and Chuang Wang .
Online since: December 2013
Authors: Zhi Yong Wu, Xiao Ning Zhang, Li Wan Shi, Shu Wen Zhang
For discussion, we assume that the pixel gray scale values have been normalized in the interval [0,1], r = 0 means black, r = 1 means white in the gray level coordinates.
Any one of the r-value within the interval [0,1] transforms by transformation function (5) T (r) meets two conditions: (1) single-valued monotonically increasing function; (2) 0 ≤ T (r) ≤ 1.The order of the gray level from black to white is kept in condition (1) and the condition (2) ensures that the mapping transformation pixel gray value within the allowable range.
References [1] Yue Z Q, Morin I.
Canadian Journal of civil Engineering, 1996, 23: 479~489
Cement& Concrete Research,1999, 29:1 403~1 401
Any one of the r-value within the interval [0,1] transforms by transformation function (5) T (r) meets two conditions: (1) single-valued monotonically increasing function; (2) 0 ≤ T (r) ≤ 1.The order of the gray level from black to white is kept in condition (1) and the condition (2) ensures that the mapping transformation pixel gray value within the allowable range.
References [1] Yue Z Q, Morin I.
Canadian Journal of civil Engineering, 1996, 23: 479~489
Cement& Concrete Research,1999, 29:1 403~1 401
Online since: July 2012
Authors: Guang Yu Zhang, Cui Ru Wu, Zhi Gang Chen
In Fig. 1.
References [1] N.
Contr., Vol. 107 (1985) No. 1, pp.1-24
IEEE/RSJ International Workshop on Intelligent Robots and Systems (Tsukuba, Japan, September 4-6, 1989). pp. 479-485
Vol.1, pp.186-191
References [1] N.
Contr., Vol. 107 (1985) No. 1, pp.1-24
IEEE/RSJ International Workshop on Intelligent Robots and Systems (Tsukuba, Japan, September 4-6, 1989). pp. 479-485
Vol.1, pp.186-191
Online since: June 2013
Authors: Yong Xiao
When m-NpSB was spread on the pure water surface, a phase transition was clearly observed at 12.9 mN·m-1 or 0.56 nm2·molecule-1.
On the other hand, the extrapolating area of Cu-m-NpSB isotherm was 0.30 nm2·molecule-1 while that of the ligand on the 1.0 mmol∙L-1 Cu(Ac)2 surface was 0.35 nm2·molecule-1.
Fig. 1.
References [1] G.L.
Liu: Thin Solid Films Vol. 479 (2005), p. 269
On the other hand, the extrapolating area of Cu-m-NpSB isotherm was 0.30 nm2·molecule-1 while that of the ligand on the 1.0 mmol∙L-1 Cu(Ac)2 surface was 0.35 nm2·molecule-1.
Fig. 1.
References [1] G.L.
Liu: Thin Solid Films Vol. 479 (2005), p. 269