Integrating Analytical Models with Finite-Element Models: An Application in Micromachining

References

• Arrazola, P. J. and Ozel, T. (2008). “Numerical Modeling of 3D Hard Turning Using Arbitrary Lagrangian Eulerian Finite Element Method”. International Journal of Machining and Machinability of Materials 4 (1), pp. 14–25.
   Google Scholar
• Ba, S. and Joseph, V. R. (2011). “Multi-Layer Designs for Computer Experiments”. Journal of the American Statistical Association 106, pp. 1139–1149.
   Web of Science ®Google Scholar
• Bingham, D.; Sitter, R. R.; and Tang, B. (2009). “Orthogonal and Nearly Orthogonal Designs for Computer Experiments”. Biometrika 96, pp. 51–65.
   Web of Science ®Google Scholar
• Challen, J. M. and Oxley, P. L. B. (1984). “Slip-Line Fields for Explaining the Mechanics of Polishing and Related Processes”. International Journal of Mechanical Sciences 26, pp. 403–418.
   Google Scholar
• Fang, K. T.; Li, R.; and Sudjianto, A. (2006). Design and Modeling for Computer Experiments. London, UK: Chapman and Hall.
   Google Scholar
• Fang, K. T. and Mukerjee, R. (2000). “A Connection Between Uniformity and Aberration in Regular Fractions of Two-Level Factorials”. Biometrika 87, pp. 193–198.
   Web of Science ®Google Scholar
• Hedayat, A.; Sloane, N.; and Stufken, J. (1999). Orthogonal Arrays. New York, NY: Springer Verlag.
   Google Scholar
• Johnson, M. E.; Moore, L. M.; and Ylvisaker, D. (1990). “Minimax and Maximin Distance Designs”. Journal of Statistical Planning and Inference 26, pp. 131–148.
   Web of Science ®Google Scholar
• Joseph, V. R. (2006). “Limit Kriging”. Technometrics 48, pp. 458–466.
   Web of Science ®Google Scholar
• Joseph, V. R. and Hung, Y. (2008). “Orthogonal-Maximin Latin Hypercube Designs”. Statistica Sinica 18, pp. 171–186.
   Web of Science ®Google Scholar
• Kennedy, M. C. and O'Hagan, A. (2000). “Predicting the Output from a Complex Computer Code When Fast Approximation Are Available”. Biometrika 87, pp. 1–13.
   Web of Science ®Google Scholar
• Kerr, M. K. (2001). “Bayesian Optimal Fractional Factorials”. Statistica Sinica 11, pp. 605–630.
   Web of Science ®Google Scholar
• Manjunathaiah, J. and Endres, W. J. (2000). “A New Model and Analysis of Orthogonal Machining with an Edge-Radiused Tool”. ASME Journal of Manufacturing Science and Engineering 122, pp. 384–390.
   Web of Science ®Google Scholar
• McKay, M. D.; Beckman, R. J.; and Conover, W. J. (1979). “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”. Technometrics 21, pp. 239–245.
   Web of Science ®Google Scholar
• Merchant, M. E. (1944). “Basic Mechanics of the Metal-Cutting Process”. Journal of Applied Mechanics 11, pp. A168–A175
   Google Scholar
• Morris, M. D. and Mitchell, T. J. (1995). “Exploratory Designs for Computational Experiments”. Journal of Statistical Planning and Inference 43, pp. 381–402.
   Web of Science ®Google Scholar
• Outeiro, J. C.; Umbrello, D.; and M'Saoubi, R. (2006) “Experimental and Numerical Modeling of the Residual Stresses Induced in Orthogonal Cutting of AISI 316L Steel”. International Journal of Machine Tools and Manufacture 46, pp. 1786–1794.
   Web of Science ®Google Scholar
• Qian, P. Z. G. (2009). “Nested Latin Hypercube Designs”. Biometrika 96, pp. 957–970.
   Web of Science ®Google Scholar
• Qian, P. Z. G.; Ai, M.; and Wu, C. F. J. (2009a). “Construction of Nested Space-Filling Designs”. Annals of Statistics 37, pp. 3616–3643.
   Web of Science ®Google Scholar
• Qian, P. Z. G.; Seepersad, C.; Joseph, V. R.; Allen, J.; and Wu, C. F. J. (2006). “Building Surrogate Models with Detailed and Approximate Simulations”. ASME Journal of Mechanical Design 128, pp. 668–677.
   Web of Science ®Google Scholar
• Qian, P. Z. G. and Wu, C. F. J. (2008). “Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments”. Technometrics 50, pp. 192–204.
   Web of Science ®Google Scholar
• Qian, P. Z. G.; Tang, B.; and Wu, C. F. J. (2009b). “Nested Space-Filling Designs for Experiments with Two Levels of Accuracy”. Statistica Sinica 19, pp. 287–300.
   Web of Science ®Google Scholar
• Sacks, J.; Welch, W. J.; Mitchell, T. J.; and Wynn, H. P. (1989). “Design and Analysis of Computer Experiments”. Statistical Science 4, pp. 409–423.
   Google Scholar
• Santner, T. J.; Williams, B. J.; and Notz, W. (2003). The Design and Analysis of Computer Experiments. New York, NY: Springer.
   Google Scholar
• Saltelli, A.; Chan, K.; and Scott, E. (2000). Sensitivity Analysis. Chichester, UK: John Wiley # Sons.
   Google Scholar
• Singh, R. and Melkote, S. N. (2009). “Force Modeling in Laser Assisted Micromachining Including the Effect of Machine Deflection”. ASME Journal of Manufacturing Science and Engineering 131, pp. 011013–011022.
   Google Scholar
• Tuo, R.; Wu, C. F. J.; and Yu, D. (2013). “Modeling of Computer Experiments with Different Mesh Densities”. Technometrics, under revision.
   Google Scholar
• Waldorf, D. J.; DeVor, R. E.; and Kapoor, S. G. (1998). “A Slip-Line Field for Ploughing During Orthogonal Cutting”. ASME Journal of Manufacturing Science and Engineering 120, pp. 693–699.
   Web of Science ®Google Scholar
• Wu, C. F. J. and Hamada, M. S. (2009). Experiments: Planning, Analysis, and Optimization, 2nd edition. New York, NY: Wiley.
   Google Scholar
• Xia, H.; Ding, Y.; and Mallick, B. (2011). “Bayesian Hierarchical Models for Combining Misaligned Two-Resolution Metrology Data”. IIE Transactions 43, pp. 242–258.
   Web of Science ®Google Scholar
• Yan, H.; Hua, J.; and Shivpuri, R. (2007). “Flow Stress of AISI H13 Die Steel in Hard Machining”. Materials and Design 28, pp. 272–277.
   Web of Science ®Google Scholar