Reliability Evaluation of NC Machine Tools considering Working Conditions

Li, Hongzhou; Yang, Zhaojun; Xu, Binbin; Chen, Chuanhai; Kan, Yingnan; Liu, Guofei

doi:https://doi.org/10.1155/2016/9842607

Mathematical Problems in Engineering

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Abstract Introduction Conclusions Appendix Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2016 | Article ID 9842607 | https://doi.org/10.1155/2016/9842607

Reliability Evaluation of NC Machine Tools considering Working Conditions

Hongzhou Li,^1,2Zhaojun Yang,¹Binbin Xu,¹Chuanhai Chen,¹Yingnan Kan,¹and Guofei Liu¹

Academic Editor: Yuming Qin

Received16 Nov 2015

Accepted17 Feb 2016

Published04 May 2016

Abstract

Reliability evaluation is the basis for reliability design of NC machine tools. Since traditional reliability evaluation methods do not consider the working conditions’ effects on reliability, there is a great error of a result of a traditional method compared with an actual value. A new reliability evaluation model of NC machine tools is proposed based on the Cox proportional hazards model, which describes the mathematical relation between the working condition covariates and the reliability level of NC machine tools. Firstly, the coefficients of working condition covariates in the new reliability evaluation model are estimated by the partial likelihood estimation method; secondly, the working condition covariates which have no effects on the reliability of NC machine tools are eliminated by the likelihood ratio test; then parameters of the baseline failure rate function are estimated by the maximum likelihood estimation method. Thus, the reliability evaluation model of NC machine tool is obtained under different working conditions and the reliability level of NC machine tools is obtained. Case study shows that the proposed method could establish the relation between the working condition covariates and the reliability level of NC machine tools, and it would provide a new way for the reliability evaluation of NC machine tools.

1. Introduction

With the rapid development of high-speed and high-precision technologies, NC machine tools are becoming the main equipment for advanced manufacturing technology [1], and so they have been applied widely in many industries and regions. Kinds of machining workpieces are also very different from user to user. Meanwhile, the atmosphere pressure and temperature vary greatly from one region to another. Therefore, NC machine tools are usually in different working conditions [2]. Generally, different working conditions have different impacts on reliability of products [3], which have been confirmed by many researches [4–10]. Hu studied the influence of various road conditions on reliability of cars, and the strengthening coefficients based on the strengthening road conditions were obtained [4]. The reliability model of power system considering weather conditions was established and the weather conditions were divided into 3-state or 2-state weather model [5, 6]. In order to evaluate the reliability of electronic system, hybrid stochastic Petri net was used to establish the reliability model. And the reliability levels of the system under different temperature and voltage were obtained [7]. Chen et al. studied the failure physics equation, which is generalized Eyring model, of aerospace electrical connectors; the multiple-stress accelerated test scheme is adopted; then the reliability levels of aerospace electrical connectors under different temperature and vibration stresses were obtained [8]. Besides, some scholars established relations between the environmental stresses and reliability levels of some products by carrying out accelerated life tests [11, 12]. Li et al. established the relationship between working conditions (the speed and load) and reliability of harmonic driver by accelerated life test [11]. Nogueira et al. studied temperature, humidity, and current impacts on reliability of high luminosity AlGaInP LEDs by accelerated life test [12]. Thus, it can be deduced from the above studies that working conditions would also affect the reliability of NC machine tools. Different working conditions cause different reliability levels, and the more different the working conditions are, the more obvious the disparity of the reliability levels is.

Reliability is one of the most important indicators of measuring the performance of NC machine tools, which has always been the researching focus by scholars [13–17]. Given that the result of reliability evaluation is influenced by many factors, methods including the Markov model [18], Petri net [19], Monto Carlo method [20], and Bayesian method [21] are applied to reliability evaluation of NC machine tools. However, for the present, the relation between reliability of the NC machine tools and the working conditions has not been established, which causes error in the result of reliability evaluation of NC machine tools compared with the actual value. Therefore, to decrease the evaluation error, it is of great engineering significance to establish the relation between the reliability of NC machine tools and the working conditions, which is also a hard problem in reliability research on NC machine tools.

In different research areas, there are many models to describe the relations between the reliability and the working conditions. Commonly seen models, besides [4–12], include Arrhenius Model [22], Inverse Power Law Model [23], and Cumulative Exposure Model [24], as well as other models [25]. However, each of them is usually aimed at a specific type of product. For the NC machine tool is a typical mechanic-electric-hydraulic system, the above methods are difficultly used to establish the relationship between working conditions and reliability.

The proportional hazards model is a life statistical model, which is usually used for survival analysis in medicine field [26, 27], and can describe relation between patients and influencing factor. Thus, a new reliability evaluation method for NC machine tools based on the Cox proportional hazards model is proposed in this paper. Working conditions of NC machine tools are taken as covariates, and then the relation between the reliability level of NC machine tools and the covariates is established based on the proposed model. The coefficients of working condition covariates of the proposed model are estimated by the partial likelihood method; the covariates which have no effects on the reliability of NC machine tools are eliminated by the likelihood ratio test; the parameters of baseline failure rate function are estimated by the maximum likelihood estimation. Then, the reliability model of NC machine tools is developed under different working conditions. A batch of NC machine tools is taken as the research object for case study, where the impact laws of environment temperature, cutting fluid, number of tool changes, and cutting force on the reliability of NC machine tools are researched, respectively. Finally, the feasibility of the proposed method is validated in the case study.

2. Reliability Model of NC Machine Tools considering Working Conditions

2.1. Proportional Hazards Model

The proportional hazards model was developed in 1972 by Cox, a British statistician. This model considers the relation between failure rate and covariates, which is defined as [28]where is the time between failures (TBF, a random variable) of NC machine tools, is the vector of working condition covariates, which affects the failure rate of NC machine tools, is the th covariate, such as cutting force, environment temperature, number of tool changes, or vibration, and is function of working condition covariates.

In general, can be expressed aswhere is the vector of ’s coefficients, which reflect the covariates’ influences on the failure rate function, and is the coefficient of . When , it indicates that catalyzes the machine tools to fail; when , has no effects on the failure rate of machine tools; when , depresses the machine tools to fail. is the baseline failure rate of NC machine tools, that is, the failure rate function when ; represents the failure rate function of NC machine tools under covariate .

Substituting (2) in (1) gets

Equation (3) is equivalent to the following equation:

The failure rate function of NC machine tools under the covariate is ; thus,

According to (4) and (5), then

Therefore,

Assume that the probability density function (PDF) of NC machine tools’ TBF under covariate vector is , and corresponding reliability function is . According to (7), of NC machine tools iswhere is the reliability function of NC machine tools under covariate vector .

can be expressed by

TBF of NC machine tools is generally considered to follow two-parameter Weibull distribution [2, 13, 21, 29, 30]. Suppose that, under working condition covariate , the failure rate function can be expressed by

Therefore, (7) is equivalent towhere is the shape parameter, ; is the scale parameter, .

So

Therefore, the PDF of NC machine tools’ TBF considering the working conditions can be expressed as

Then, MTBF of NC machine tools under covariate vector can be obtained bywhere is Gamma function.

2.2. Parameter Estimation

There are several parameters and coefficients in (11), which are and and ). Thus, a two-step estimation method to estimate these parameters and coefficients is employed. Firstly, is estimated by the partial likelihood estimation method [31]; then, and are estimated by the maximum likelihood estimation method.

2.2.1. Covariate Coefficients Estimation

From (7), we can get

Assuming that there are failures, the th failure of NC machine tools can be expressed as (, , ), where is time between the th failure and the th failure; is an indicator variable of datum ; when , is noncensoring time and when , is censoring time.

Equation (16) is dimensional column vector, which indicates that there are covariates in the th failure of NC machine tools:

Therefore, the partial likelihood function is given bywhere is the number of failures whose TBF is equal to . If is the set of failures of NC machine tools at time , then . is the sum of working condition covariates of failures, and so when ; is the set of data that NC machine tools do not fail and there is no censoring at .

Now take the logarithm of both sides in (17); then

Take the first derivative with respect to and let it be equal to zero, sowhere is the th element in .

There is no analytical solution to (19). So Newton-Raphson iterative method is applied to estimating the parameters [32].

The second partial derivatives of consist of the order matrix , of which the elements arewhere .

So can be estimated by Newton-Raphson numerical algorithm.

2.2.2. Elimination of No Impacting Covariate

In order to eliminate the covariates which have little or no impact on the reliability of NC machine tools, the likelihood ratio test is used and the procedure is shown in Figure 1 [33].

The likelihood ratio statistics are constructed according to (13) and (17). Suppose that there are covariates in the model and is approximate Chi-square distribution whose degree of freedom is . is the partial likelihood function of the model which does not contain covariate coefficient , and is partial likelihood function of the model which includes covariate coefficient . So

For a given confidence level , if , then has obvious effects on the model and it should be considered in the model; if , then has no effects on the model and it should not be considered.

2.2.3. Parameters Estimation of Baseline Failure Function

The maximum likelihood estimation method is adopted to estimate the parameters and in (13), and the likelihood function is given as

Take the logarithm of both sides in (22); then

Take the partial derivative of the parameters and in (23), respectively, and then

Since (24) have no analytical solutions, Newton-Raphson numerical algorithm is used to estimate parameters and .

3. Case Study

3.1. Field Test Data Collection

The failure data and the corresponding load data analyzed in this study were collected from 19 machining centers which were used in two user enterprises located in southwest of China. These machining centers were traced from February 10, 2014, to June 20, 2014. The failure data includes failure happening time and ending time, failure cause, failure position, failure phenomenon, and the repair information, all of which are recorded in Appendix A by testers. The load data, including manufacturing procedure, cutting parameters, material, cutting fluid, and environment temperature, are all recorded in Appendix B by testers.

According to the failure data and the corresponding load data recorded in Appendix A and B, TBFs and corresponding cutting force, environment temperature, cutting fluid condition, number of tool changes, and other working conditions of the traced machining centers are elicited and arranged. Cutting force, number of tool changes, environmental temperature, and cutting fluid are chosen as the working condition covariates considering the limitation of the testing conditions. The environmental temperature, cutting fluid, and the number of tool changes can be found in Appendix B directly. As for the field reliability test, it is impossible to install force sensors on machine tools, and thus cutting force cannot be measured by the force sensor directly. Therefore, the average cutting force is calculated by empirical formula (25) [34] in combination with the data recorded in Appendix B:where , , , , , and are coefficients which can be found in [34]; is the depth of cuts; is the feed of a tooth; is the cutting breadth; is the number of milling cutter teeth; is the diameter of milling cutter; is the rotation speed of milling cutter.

Machining centers used in the first enterprise are mainly for machining cylinder block, cylinder head, connecting plate, and the corresponding mould workpieces. The cutting times of cylinder block, cylinder head, and connecting plate account for 89 percent of the total cutting time. Generally, the machining process of workpiece consists of several operations or more; we calculate cutting forces of the above workpieces, respectively, by the parameters of the operation whose cutting time is the longest. And machining centers in the second enterprise are mainly for machining flywheel, flywheel housing, and cylinder. While the cutting time of these workpieces accounts for about 92 percent of the total cutting time, we also calculate cutting forces of the three workpieces, respectively, by the parameters of the operation whose cutting time is the longest. The parameters of the longest cutting time of the above workpieces are shown in Table 1.

The average cutting force for the seven workpieces is calculated according to Table 1 and (25). The covariates, TBFs, and other data are shown in Table 2.

3.2. Model Establishment and Comparison between the Traditional Method and the New Method

The coefficient of the covariate in the proportional hazards model is calculated by the partial likelihood estimation method and the likelihood ratio test, where . The results are shown in Table 3.

It is seen from Table 3 that is greater than in the third step and there are only two covariates in the model, cutting force and number of tool changes, in the third step. Thus, the working condition covariates of cutting fluid and temperature (19°C–22°C) have no effects on the reliability of NC machine tools.

The failure rate function with the following working condition covariates, cutting force KN and number of tool changes , is chosen as the baseline failure rate function in (15). Then and in (15) are estimated by the maximum likelihood estimation method. The reliability models under different covariates are obtained and shown in Table 4.

The proportional hazard models under different working condition covariates shown in Table 4 can be transformed into Weibull failure rate models, and then MTBF can be obtained by (14), as shown in Table 5.

For comparison, reliability model parameters and MTBF of NC machine tools obtained by the traditional method [35], which does not consider the working condition covariates and is based on two-parameter Weibull distribution, are also shown in Table 5. The traditional method uses only two columns (TBF and data type) of data in Table 2 of the same batch of NC machine tools. The detailed procedure of calculation of the traditional method is given in [35], and the corresponding evaluation result is shown in the last row of Table 5.

For an obvious comparison, the last column of Table 5 gives the MTBF comparison (MC), which is obtained bywhere is the MTBF obtained by the traditional method; is the MTBF under different working condition covariates obtained by the new method.

From Table 5, it is seen that the traditional method gives h, and the given by the new method varies with different working conditions of NC machine tools. For example, firstly, when the cutting force KN and the number of tool changes , is 351.8384 h, which is only 0.6014 times of . Secondly, the reliability level could reach 1084.6 h when the cutting force and the number of tool changes are getting smaller ( KN and ), which is 1.8540 times of . Thirdly, when , and changes from 0.54 KN to 0.81 KN, the values of change from to . And when the cutting force KN and the number of tool changes , is 581.4844 h, which is 0.994 times of . From the above analysis it can be seen that obtained by the new method increases as the cutting force or the number of tool changes increases. Only under some particular working conditions does the obtained by the traditional method equal .

From Table 5, the PDFs and cumulative distribution functions (CDFs) of NC machine tools’ TBF under different working condition covariates obtained by the new method are shown in Figures 2 and 3, respectively. Meanwhile, the PDF and CDF obtained by the traditional method are also shown in Figures 2 and 3.

It is seen from Figures 2 and 3 that there are obvious distinctions between the PDF curves and CDF curves obtained by the traditional method and the new methods. The PDF curve and the CDF curve obtained by the traditional method are only one single curve, and only under some particular working conditions are they close to the PDF curves and CDF curves obtained by the new method. When the values of cutting force or the number of tool changes are comparatively large (e.g., the cutting force = 1.03 KN and the number of tool changes = 14), the PDF curves and the CDF curves are steep. And when the values of cutting force or the number of tool changes are comparatively small (e.g., = 0.35 KN and = 2), the PDF curves and the CDF curves are even. It can be concluded from the above facts that, under different working conditions, the failure rate of NC machine tools is different, and the traditional method does not consider the differences in the results of reliability evaluation caused by different working conditions.

Therefore, the working conditions must be considered in evaluating the reliability level of NC machine tools.

4. Conclusions

The same NC machine tools under different working conditions have different reliability level. However, traditional reliability evaluation methods do not consider the impacts of working conditions on the reliability of NC machine tools. Considering this problem, the authors propose a new method for NC machine tools’ reliability evaluating. The Cox proportional hazards model is used to establish the mathematical relation model between the NC machine tools’ reliability and the corresponding working conditions. The likelihood ratio test is used to eliminate the working condition covariates which have no effects on the reliability of NC machine tools. In the parameter estimating process, a two-step estimation method is applied. That is, the coefficients of working condition covariates are firstly estimated by the partial likelihood estimation method, and then the maximum likelihood estimation method is used to estimate the parameters of the baseline failure rate function. Finally, a practical application case is presented. The new method is used for evaluating reliability of NC machine tools under different working conditions, which have 4 covariates (the cutting force, the number of tool changes, the cutting fluid, and the environment temperature). By the likelihood ratio test, the cutting fluid and the environment temperature (within the range of 19~22 degrees Centigrade) are eliminated, which have no effects on the reliability, and the mathematical relation between the cutting force, the number of tool changes, and the reliability is finally established. When the cutting force and the number of tool changes increase, the reliability of NC machine tools increases. For further comparison, the authors used a traditional method to evaluate the reliability of this batch of NC machine tools. The result of the case study indicates that, compared with the traditional method, the new method can establish the mathematical relation between the working conditions and the reliability, which makes up the defects of traditional methods and provides a more accurate basis for further reliability improvement design and reliability growth.

Appendix

A. Failure Records

Firm:………… Failure time:………… Serial Number:………… Machine Tool Type:………… Machine Tool Name:………… Failure Time Failure Start Time (hour):………… Failure End Time (hour):………… Repair Time Repair Start Time (hour):………… Repair End Time (hour):………… Staff Waiting Time (hour):………… Spare Workpieces Waiting Time (hour):………… Pictures of failure Failure phenomenon:………… Failure position:………… Failure cause:………… Pictures of Repair Failure Tracing:………… Failure Handling:………… Replaced Part Name:………… Specifications and Models:………… Quantity:………… Whether it is an coherent failure:…………

B. Load Records

Machine Tool Type:………… Manufacture Date:………… Workpieces Name:………… Quantity:………… Workpieces Material:………… Serial Number Contents of manufacturing Procedures Tool Name Cutting Tool Material Tool Type Workpieces Material Workpieces Hardness (HRC) Cutting Edge Angle Kr (degree) Spindle Speed (r/min) Feed Rate (mm/min) ±20% Cutting Depth (mm) Allowance (mm) Feed Rate (mm/r) Feed Rate (mm/min) Feed Rate per Tooth (mm/z) Cutting Breadth (mm) Tool Diameter (mm) Number of Cutter Teeth (pieces) Tool Life (min) Number of Tool Changes Processing Time (min) Type of Cutting Fluid Environmental Temperature Special Instructions Workpieces Clamping Method.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

Research in this paper was supported by National Science and Technology Major Project of China (Grant no. 2013ZX04011-012) of reliability promotion of thousands of Chinese CNC machining centers and the National Natural Science Foundation of China (Grant no. 51505186).

References

H. Xiao-cong, “Recent development in reliability analysis of NC machine tools,” The International Journal of Advanced Manufacturing Technology, vol. 9, pp. 1–17, 2015.
View at: Google Scholar
W. Wang and D. B. Kececioglu, “Fitting the Weibull log-linear model to accelerated life-test data,” IEEE Transactions on Reliability, vol. 49, no. 2, pp. 217–223, 2000.
View at: Publisher Site | Google Scholar
D. Dimitrov, V. Karachorova, and T. Szecsi, “Accuracy and reliability control of machining operations on machining centres,” Key Engineering Materials, vol. 615, pp. 32–38, 2014.
View at: Publisher Site | Google Scholar
G. Hu, Enhancement Coefficient of Motor Vehicle Reliability Enhancement Test, Zhejiang University, 2003.
Y.-J. Chen, R. Zhen, and W.-Y. Huang, “Model and analysis of power system reliability evaluation considering weather change,” Automation of Electric Power Systems, vol. 28, no. 21, pp. 17–21, 2004.
View at: Google Scholar
L. Wang, S. Zhao, and M. Zhang, “Reliability evaluation of transmission system considering weather change,” Power System Technology, vol. 35, no. 7, pp. 66–70, 2011.
View at: Google Scholar
Q.-N. Qin, X.-Y. Wei, Z.-W. Huang, and Y. Meng, “Reliability evaluation method of electronic system under variable environments and working conditions,” Journal of Central South University: Science and Technology, vol. 44, no. 8, pp. 3254–3260, 2013.
View at: Google Scholar
W.-H. Chen, H.-S. Li, W.-Z. Lian, J. Pan, and X.-B. Lu, “Accelerated life test and statistical analysis of aerospace electrical connectors under multiple environmental stresses,” Journal of Zhejiang University, vol. 40, no. 2, pp. 348–351, 2006.
View at: Google Scholar
L.-N. Ren, Z.-Y. Rui, J. Liu, and C.-L. Lei, “Whole lifecycle reliability assessment of multiple NC machine tools with minimal repair,” Journal of Shanghai Jiao Tong University, vol. 49, no. 1, pp. 19–23, 2015.
View at: Publisher Site | Google Scholar
X.-Z. Zhou, Research on reliability assessment based on Bayesian methods [M.S. thesis], Huazhong University of Science and Technology, Wuhan, China, 2007.
J.-Y. Li, J.-X. Wang, G.-W. Zhou, W. Pu, and Z.-H. Wang, “Accelerated life testing of harmonic driver in space lubrication,” Proceedings of the Institution of Mechanical Engineers J: Journal of Engineering Tribology, vol. 229, no. 12, pp. 1491–1502, 2015.
View at: Publisher Site | Google Scholar
E. Nogueira, M. Vazquez, and J. Mateos, “Accelerated life test of high luminosity AlGaInP LEDs,” Microelectronics Reliability, vol. 52, no. 9-10, pp. 1853–1858, 2012.
View at: Publisher Site | Google Scholar
Z. Yang, C. Chen, F. Chen, and G. Li, “Progress in the research of reliability technology of machine tools,” Journal of Mechanical Engineering, vol. 49, no. 20, pp. 130–139, 2013.
View at: Publisher Site | Google Scholar
B. K. Lad and M. S. Kulkarn, “Reliability and maintenance based design of machine tools,” International Journal of Performability Engineering, vol. 9, no. 3, pp. 321–332, 2013.
View at: Google Scholar
Y. Zhang, R. Zheng, G. Shen, and B. Chen, “Reliability analysis for CNC machine tool based on failure interaction,” Communications in Computer and Information Science, vol. 134, no. 1, pp. 489–496, 2011.
View at: Publisher Site | Google Scholar
M. K. Loganathan and O. P. Gandhi, “Reliability evaluation and analysis of CNC cam shaft grinding machine,” Journal of Engineering, Design and Technology, vol. 13, no. 1, pp. 37–73, 2015.
View at: Publisher Site | Google Scholar
J. R. W. Merrick, R. Soyer, and T. A. Mazzuchi, “A Bayesian semiparametric analysis of the reliability and maintenance of machine tools,” Technometrics, vol. 45, no. 1, pp. 58–69, 2003.
View at: Publisher Site | Google Scholar | MathSciNet
H.-T. Guo and X.-H. Yang, “Automatic creation of Markov models for reliability assessment of safety instrumented systems,” Reliability Engineering and System Safety, vol. 93, no. 6, pp. 829–837, 2008.
View at: Publisher Site | Google Scholar
V. Kumar and K. K. Aggarwal, “Petri Net modelling and reliability evaluation of distributed processing systems,” Reliability Engineering and System Safety, vol. 41, no. 2, pp. 167–176, 1993.
View at: Publisher Site | Google Scholar
M. Marseguerra and E. Zio, Basics of the Monte Carlo Method with Application to System Reliability, LiLoLe Press, Hagen, Germany, 2002.
Z.-J. Yang, Y.-N. Kan, F. Chen, B. Xu, C. Chen, and C. Yang, “Bayesian reliability modeling and assessment solution for NC machine tools under small-sample data,” Chinese Journal of Mechanical Engineering, vol. 28, no. 6, pp. 1229–1239, 2015.
View at: Publisher Site | Google Scholar
O. Haillant, D. Dumbleton, and A. Zielnik, “An Arrhenius approach to estimating organic photovoltaic module weathering acceleration factors,” Solar Energy Materials and Solar Cells, vol. 95, no. 7, pp. 1889–1895, 2011.
View at: Publisher Site | Google Scholar
A. Azrulhisham Engku, W. Mohamad, and W. Mansor, “Inverse power law model for operative life estimation of carbon steel stub axle,” SAE Technical Papers 2013-01-0328, 2013.
View at: Publisher Site | Google Scholar
W. B. Nelson, “Accelerated life testing-step-stress models and data analysis,” IEEE Transactions on Reliability, vol. 29, no. 2, pp. 103–108, 1980.
View at: Publisher Site | Google Scholar
H. Ma and W. Q. Meeker, “Strategy for planning accelerated life tests with small sample sizes,” IEEE Transactions on Reliability, vol. 59, no. 4, pp. 610–619, 2010.
View at: Publisher Site | Google Scholar
N. Kazumitsu, L. Masako, and I. Aya, “An analysis of the length of hospital stay for cataract patients in Japan using discrete-type proportional hazard,” Mathematics and Computers in Simulation, vol. 79, no. 9, pp. 2889–2896, 2009.
View at: Publisher Site | Google Scholar
P. J. G. Lisboa, H. Wong, A. Vellido, S. P. J. Kirby, P. Harris, and R. Swindeil, “Survival of breast cancer patients following surgery: a detailed assessment of the multi-layer perceptron and Cox's proportional hazard model,” in Proceedings of the IEEE World Congress on Computational Intelligence. The IEEE International Joint Conference on Neural Networks, vol. 1, pp. 112–116, Anchorage, Alaska, USA, May 1998.
View at: Publisher Site | Google Scholar
D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 34, no. 2, pp. 187–220, 1972.
View at: Google Scholar | MathSciNet
J. Yazhou, S. Guixiang, J. Zhixin, and W. Yiqiang, “Equivalent fatigue load in machine-tool probabilistic reliability. Part II: calculation methodology and practical applications,” International Journal of Fatigue, vol. 15, no. 6, pp. 478–487, 1993.
View at: Publisher Site | Google Scholar
B. K. Lad and M. S. Kulkarni, “A mechanism for linking user's operational requirements with reliability and maintenance schedule for machine tool,” International Journal of Reliability and Safety, vol. 4, no. 4, pp. 343–358, 2010.
View at: Publisher Site | Google Scholar
D. R. Cox, “Partial likelihood,” Biometrika, vol. 62, no. 2, pp. 269–276, 1975.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
S. Janković and B. Ivanović, “Application of combined Newton–Raphson method to large load flow models,” Electric Power Systems Research, vol. 127, pp. 134–140, 2015.
View at: Publisher Site | Google Scholar
M. Anisimova and O. Gascuel, “Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative,” Systematic Biology, vol. 55, no. 4, pp. 539–552, 2006.
View at: Publisher Site | Google Scholar
A. Xing and X. Shi-gang, Concise Manual of Cutting Parameters, China Machine Press, 2011.
Y. Dai, Y.-F. Zhou, and Y.-Z. Jia, “Distribution of time between failures of machining center based on type I censored data,” Reliability Engineering and System Safety, vol. 79, no. 3, pp. 377–379, 2003.
View at: Publisher Site | Google Scholar

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Copyright © 2016 Hongzhou Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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