Abstract
For the new generation of concrete which enables more stable constructions, we require more efficient tools. Since the preferred tool for machining concrete is a diamond impregnated drill with substantial initial investment costs, the reduction of tool wear is of special interest. The stochastic character of the diamond size, orientation, and position in sintered segments, as well as differences in the machined material, justifies the development of a statistically motivated simulation. In the simulations presented in the past, workpiece and tool are subdivided by Delaunay tessellations into predefined fragments. The heterogeneous nature of the ingredients of concrete is solved by Gaussian random fields. Before proceeding with the simulation of the whole drill core bit, we have to adjust the simulation parameters for the two main components of the drill, diamond and metal matrix, by minimizing the discrepancy between simulation results and the conducted experiments. Due to the fact that our simulation is an expensive black box function with stochastic outcome, we use the advantages of model based optimization methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ankenman, B., Nelson, B. L., & Staum, J. (2010). Stochastic kriging for simulation metamodeling. Operations Research, 58(2), 371–382.
Bischl, B., Bossek, J., Richter, J., Horn, D., & Lang, M. (2013). mlrMBO: mlr: Model-based optimization. R package version 1.0, https://github.com/berndbischl/mlr
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., & Keogh, E. (2008). Querying and mining of time series data: Experimental comparison of representations and distance measures. In Proceedings of the VLDB Endowment, 1 (2), August 23–28, 2008, Auckland (pp. 1542–1552).
Giorgino, T. (2009). Computing and visualizing dynamic time warping alignments in R: The dtw package. Journal of Statistical Software, 31(7), 1–24. http://www.jstatsoft.org/v31/i07/
Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21(4), 345–383.
Müller, M. (2007). Dynamic time warping. In Information retrieval for music and motion (pp. 69–84). New York: Springer.
Picheny, V., Wagner, T., & Ginsbourger, D. (2013). A benchmark of kriging-based infill criteria for noisy optimization. Structural and Multidisciplinary Optimization, 48(3), 607–626.
Raabe, N., Rautert, C., Ferreira, M., & Weihs, C. (2011). Geometrical process modeling and simulation of concrete machining based on Delaunay tessellations. In S. I. Ao, C. L. Douglas, W. S. Grundfest & J. Burgstone (Eds.), Proceedings of the World Congress on Engineering and Computer Science 2011 (WCECS’11), October 19–21, 2011. Lecture Notes in Engineering and Computer Science (Vol. II, pp. 991–996). San Francisco: International Association of Engineers, Newswood Limited.
Raabe, N., Thieler, A. M., Weihs, C., Fried, R., Rautert, C., & Biermann, D. (2012). Modeling material heterogeneity by Gaussian random fields for the simulation of inhomogeneous mineral subsoil machining. In P. Dini, P. Lorenz (Eds.), SIMUL 2012: The Fourth International Conference on Advances in System Simulation, November 18–23, 2012, Lisbon (pp. 97–102).
Schlather, M., Malinowski, A., Oesting, M., Boecker, D., Strokorb, K., Engelke, S., et al. (2014). RandomFields: Simulation Simulation and analysis of random fields. R package version 3.0.10, http://CRAN.R-project.org/package=RandomFields
Weihs, C., Raabe, N., Ferreira, M., & Rautert, C. (2014). Statistical process modelling for machining of inhomogeneous mineral subsoil. In German-Japanese interchange of data analysis results (pp. 253–263). New York: Springer.
Acknowledgements
This work has been supported by the Collaborative Research Center “Statistical modeling of nonlinear dynamic processes” (SFB 823) of the German Research Foundation (DFG)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Herbrandt, S. et al. (2016). Optimization of a Simulation for Inhomogeneous Mineral Subsoil Machining. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-25226-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25224-7
Online ISBN: 978-3-319-25226-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)