MATEC Web Conf.
Volume 192, 2018The 4th International Conference on Engineering, Applied Sciences and Technology (ICEAST 2018) “Exploring Innovative Solutions for Smart Society”
|Number of page(s)||4|
|Section||Track 1: Industrial Engineering, Materials and Manufacturing|
|Published online||14 August 2018|
- G. N. Sakharov, V. Ilinykh, V. Yu. Konyukhov, Soviet engineering research, Improvement of fastening elements in an assembled cutting tool, 10(11), 102-103 (1990) [Google Scholar]
- H. Wiklund, Quality and Reliability Engineering International, Bayesian and regression approaches to on-line prediction of residual tool life, 14(5), 303-309 (1998) [Google Scholar]
- R. E. DeVor, D. L. Anderson, W. J. Zdeblick, Journal of Engineering for Industry Transactions of the ASME, Tool Life Variation and its Influence on the Development of Tool Life Models, 99, 578–584 (Sec B) (1977) [Google Scholar]
- K. Hitomi, N. Nakamura, S. Inoue, Manufacturing Science and Engineering, Reliability analysis of cutting tools, 101(2), 185–190 (1979) [Google Scholar]
- H. Negishi, K. Aoki, Precis Machining Investigations on reliability of carbide cutting tools, 42(6), 578–589 (1976) [Google Scholar]
- C. E. P. Rodriguez, Souza de, Reliability Engineering & System Safety, Reliability concepts applied to cutting tool change time, 95(8), 866–873 (2010) [Google Scholar]
- X. ShengSi, W. Wang, C. HuaHu, D. HuaZhou, European Journal of Operational Research, Remaining useful life estimation – A review on the statistical data driven approaches, 213, 1-14 (2011) [Google Scholar]
- T. Benkedjouh, K. Medjaher, N. Zerhouni, and S. Rechak, Journal of Intelligent Manufacturing, Health assessment and life prediction of cutting tools based on support vector regression, 26(2), 1-11 (2013) [Google Scholar]
- N. Gebraeel, M. Lawley, R. Liu, V. Prameswaran, IEEE Transactions on Industrial Electronics, Residual life predictions from vibration based degradation signals: a neural network approach, 51(3), 694–700 (2004) [Google Scholar]
- M. Aramesh, M. H. Attia, H. A. Kishawy, M. Balazinski, CIRP Journal of Manufacturing Science and Technology, Estimating the remaining useful tool life of worn tools under different cutting parameters: A survival life analysis during turning of titanium metal matrix composites (Ti-MMCs), 12, 35–43 (2016) [Google Scholar]
- H. A. Kishawy, S. Kannan, M. Balazinski, CIRP Annals – Manufacturing Technology, Analytical modeling of tool wear progression during turning particulate reinforced metal matrix composites, 54(1), 55–58, (2005) [Google Scholar]
- Y. Shaban, M. Aramesh, S. Yacout, M. Balazinski, H. Attia, H. Kishawy, Journal on Engineering Manufacture, Optimal replacement times for machining tool during turning titanium metal matrix composites under variable machining conditions, 231(6), 924-932 (2017) [Google Scholar]
- F. Ding, Z. He, International Journal of Advanced Manufacturing Technology, Cutting tool wear monitoring for reliability analysis using proportional hazards model, 57, 565-574 (2011) [Google Scholar]
- R. J. Cook, J. F. Lawless, Springer, The statistical analysis of recurrent events, (2007) [Google Scholar]
- D. Banjevic, A. Jardine, V. Makis, M. Ennis, INFOR-OTTAWA, A control-limit policy and software for condition based maintenance optimization, 39, 32-50 (2001) [Google Scholar]
- https://www.sandvik.coromant.com/engb/products/t-max_p/Pages/default.aspx, (2018) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.