Open Access
Issue |
MATEC Web Conf.
Volume 95, 2017
2016 the 3rd International Conference on Mechatronics and Mechanical Engineering (ICMME 2016)
|
|
---|---|---|
Article Number | 07018 | |
Number of page(s) | 5 | |
Section | Mechanical Design-Manufacture and Automation | |
DOI | https://doi.org/10.1051/matecconf/20179507018 | |
Published online | 09 February 2017 |
- W. Cai, S. J. Hu, J. X. Yuan, Deformable Sheet Metal Fixturing: Principles, Algorithms, and Simulations. ASME J. Manuf. Sci. Eng., 118, 3:318–324 (1996) [Google Scholar]
- K. Krishnakumar, S. N. Melkote, Machining fixture layout optimization using the genetic algorithm. Int. J. Mach. Tools Manuf., 40, 4:579–598 (2000) [CrossRef] [Google Scholar]
- S. G. Liu, L. Zheng, Z. H. Zhang, Z. Z. Li, D. C. Liu, Optimization of the number and positions of fixture locators in the peripheral milling of a low-rigidity workpiece. Int. J. Adv. Manuf. Technol., 33, 7–8: 668–676 (2007) [CrossRef] [Google Scholar]
- G. Prabhaharan, K. P. Padmanaban, R. Krishnakumar, Machining fixture layout optimization using FEM and evolutionary techniques. Int. J. Adv. Manuf. Technol., 32, 11–12:1090–1103 (2007) [CrossRef] [Google Scholar]
- K. P. Padmanaban, K. P. Arulshri, G. Prabhakaran, Machining fixture layout design using ant colony algorithm based continuous optimization method. Int. J. Adv. Manuf. Technol., 45, 9:922–934 (2009) [CrossRef] [Google Scholar]
- J. P. Dou, X. S. Wang, L. Wang, Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques. Int. J. Prod. Res., 50, 15:4294–4315 (2012) [CrossRef] [Google Scholar]
- Y. F. Xing, M. Hu, H. Zeng, Y. S. Wang, Fixture layout optimisation based on a non-domination sorting social radiation algorithm for auto-body parts. Int. J. Prod. Res., 53, 11:3475–3490 (2015) [CrossRef] [Google Scholar]
- T. W. Simpson, A. J. Booker, D. Ghosh, A. A. Giunta, P. N. Koch, R. J. Yang, Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Struct. Multidiscip. O., 27, 5:302–313 (2004) [CrossRef] [Google Scholar]
- M. Hamedi, Intelligent fixture design through a hybrid system of artificial neural network and genetic algorithm. Artif. Intell. Rev., 23, 3: 295–311 (2005) [CrossRef] [Google Scholar]
- C. Lu, H. W. Zhao, Fixture layout optimization for deformable sheet metal workpiece. Int. J. Adv. Manuf. Technol., 78, 1–4: 85–98 (2015) [CrossRef] [Google Scholar]
- Z. Q. Wang, B. Yang, Y. G. Kang, Y. Yang, Development of a prediction model based on RBF neural network for sheet metal fixture locating layout design and optimization, Computational Intelligence and Neuroscience, 2016: 1–6 (2016) [Google Scholar]
- J. L. Deng, Introduction to grey system theory. J. Grey Syst., 1, 1: 1–24 (1989) [Google Scholar]
- J. L. Deng, Grey system theory, Huazhong University of Science and Technology Press, Wuhan, (2002) (in Chinese) [Google Scholar]
- Y. Wang, G. Zhang, K. S. Moon, J. W. Sutherland, Compensation for the thermal error of a multi-axis machining center. J. Mater. Process. Tech, 75, 1: 45–53 (1998) [CrossRef] [Google Scholar]
- Y. X. Li, J. G. Yang, T. Gelvis, Y. Y. Li, Optimization of measuring points for machine tool thermal error based on grey system theory. Int. J. Adv. Manuf. Technol., 35, 7–8: 745–750 (2008) [CrossRef] [Google Scholar]
- M. D. Mckay, R. J. Beckman, W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output form a computer code. Technometrics, 42, 1: 55–61 (2000) [CrossRef] [Google Scholar]
- D. S. Simulia, Abaqus 6.12 documentation, Providence, Rhode Island, US, (2012) [Google Scholar]
- J. W. Zhuo, Application of MATLAB Software in Mathematical Modeling, Beihang University PRESS, Beijing, (2011) (in Chinese) [Google Scholar]
- Matlab 8.0, http://www.mathworks.com/, (2012) [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.