Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

An artificial neural network approach for tool path generation in incremental sheet metal free-forming

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This research considers a specific incremental sheet metal free-forming process, which allows for individualized component manufacturing. However, for a reasonable application in practice, an automation of the manual process is mandatory. Unfortunately, up to now, no general tool path generation strategies are available when free-forming processes are to be utilized. On this account, for the investigated driving process, a holistic concept for deriving tool paths for the production of sheet metal parts directly from a digital component model is presented adopting an artificial neural network architecture. Consequently, for the very first time an automated part production is possible in incremental sheet metal free-forming applications. For this, a suitable network input and output structure is designed. Balanced sample data sets are generated for appropriate training. An associated network topology is determined and undergoes a training and testing phase. The influence of different training algorithms, network configurations, as well as training sets have been studied in relation to a feedforward network structure with backpropagation. Finally, the proposed computer integrated manufacturing system is subject to validation and verification by automated sheet part production, which is followed by concluding remarks on the capabilities and limits of the concept.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Alliez, P., Cohen-Steiner, D., Devillers, O., Lévy, B., & Desbrun, M. (2003). Anisotropic polygonal remeshing. ACM Transactions on Graphics, 22, 485–493.

    Article  Google Scholar 

  • Arainz-Gonzlez, A., Fernndez-Valdivielso, A., Bustillo, A., & de Lacalle, L. (2016). Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling. International Journal of Advaced Manufacturing Technology, 83, 847–859.

    Article  Google Scholar 

  • ASCE. (2000). Artificial neural networks in hydrology. i: Preliminary concepts. Journal of Hydrologic Engineering, 5, 115–123.

    Article  Google Scholar 

  • Ashhab, M., Breitsprecher, T., & Wartzack, S. (2014). Neural network based modeling and optimization of deep drawing extrusion combined process. Journal of Intelligent Manufacturing, 25, 77–84.

    Article  Google Scholar 

  • Back, A., & Trappenberg, T. (2001). Selecting inputs for modeling using normalized higher order statistics and independent component analysis. IEEE Transactions on Neural Networks, 12, 612–617.

    Article  Google Scholar 

  • Basheer, I., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.

    Article  Google Scholar 

  • Battiti, R. (1994). Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5, 537–550.

    Article  Google Scholar 

  • Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., & Taubin, G. (1999). The ball-pivoting algorithm for surface reconstruction. IEEE Transactions on Visualization and Computer Graphics, 5, 349–359.

    Article  Google Scholar 

  • Botsch, M., Pauly, M., Kobbelt, L., Alliez, P., Lévy, B., Bischoff, S., & Rössl, C. (2008). Geometric modeling based on polygonal meshes. Eurographics Course Notes

  • Carreira-Perpinn, M. (1997). A review of dimension reduction techniques. Technical Report CS-96-06, Department of Computer Science, University of Sheffield

  • Cohen-Steiner, D., & Morvan, JM. (2003). Restricted delaunay triangulations and normal cycle. In Proceedings of the 19th Annual Symposium on Computational Geometry, (pp. 312–321).

  • Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2, 303–314.

    Article  Google Scholar 

  • de Carmo, M. (1976). Differential geometry of curves and surfaces. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Dirksen, S. (2016). Dimensionality reduction with subgaussian matrices: A unified theory. Foundations of Computational Mathematics, 16, 1367–1396.

    Article  Google Scholar 

  • Fahlman, S. (1988). An empirical study of learning speed in backpropagation. Technical Report CMU-CS-88-162, Carnegie-Mellon University, Pittsburgh

  • Floaster, M., & Hormann, K. (2005). Surface parameterization: A tutorial and Survey. Berlin: Springer.

    Google Scholar 

  • Griffin, J. (2015). The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1165-y.

  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

    Google Scholar 

  • Hagan, M., Demuth, H., & Beale, M. (1996). Neural network design. Boston: PWS Publishing.

    Google Scholar 

  • Hecht-Nielsen, R. (1990). Neurocomputing. Boston: Addison-Wesley.

    Google Scholar 

  • Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580.

  • Hoffmann, H., Neugebauer, R., & Spur, G. (2012). Handbuch umformen. Munich: Carl Hanser.

    Book  Google Scholar 

  • Hormann, K., Lévy, B., & Sheffer, A. (2007). Mesh parametrization: Theory and practice. Siggraph Course Notes

  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.

    Article  Google Scholar 

  • Huang, S., & Huang, Y. (1991). Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Transactions on Neural Networks, 2, 47–55.

    Article  Google Scholar 

  • Hunt, K., Sbarbaro, D., Zbikowski, R., & Gawthrop, P. (1992). Neural networks for control systems—A survey. Automatica, 28, 1083–1112.

    Article  Google Scholar 

  • Hunt, K., Irwin, G., & Warwick, K. (1995). Neural network engineering in dynamic control systems. London: Springer.

    Book  Google Scholar 

  • Jadid, M., & Fairbairn, D. (1996). Neural-network applications in predicting moment-curvature parameters from experimental data. Engineering Applications of Artificial Intelligence, 9, 309–319.

    Article  Google Scholar 

  • Jamli, M., Ariffin, A., & Wahab, D. (2015). Incorporating feedforward neural network within finite element analysis for l-bending springback prediction. Expert Systems with Applications, 42, 2604–2614.

    Article  Google Scholar 

  • Jeswiet, J., Micari, F., Hirt, G., Bramley, A., Duflou, J., & Allwood, J. (2005). Asymmetric single point incremental forming of sheet metal. CIRP Annals of Manufacturing Technology, 54, 623–649.

    Article  Google Scholar 

  • Khan, M., Coenen, F., Dixon, C., El-Salhi, S., Penalva, M., & Rivero, A. (2014). An intelligent process model: Predicting springback in single point incremental forming. International Journal of Advaced Manufacturing Technology, 76, 2071–2082.

    Article  Google Scholar 

  • Lazoglu, I., Manav, C., & Murtezaoglu, Y. (2009). Tool path optimization for free form surface machining. CIRP Annals-Manufacturing Technology, 58, 101–104.

    Article  Google Scholar 

  • Lee, Y., Oh, S., & Kim, M. (1991). The effect of initial weights on premature saturation in backpropagation learning. In Proceedings of the International Joint Conference on Neural Networks, (pp. 765–770).

  • Liu, G., Kadirkamanathan, V., & Billings, S. (1998). On-line identification of nonlinear systems using volterra polynomial basis function neural networks. Neural Networks, 11, 1645–1657.

    Article  Google Scholar 

  • Liu, N., Yang, H., Li, H., Yan, S., Zhang, H., & Tang, W. (2015). Bp artificial neural network modeling for accurate radius prediction and application in incremental in-plane bending. International Journal of Advaced Manufacturing Technology, 80, 971–984.

    Article  Google Scholar 

  • Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431–441.

    Article  Google Scholar 

  • Masters, T. (1993). Practical neural network recipes in C++. San Diego: Academic Press.

    Google Scholar 

  • Narendra, K. (1996). Neural networks for control: Theory and practice. Proceedings of the IEEE, 84, 1385–1406.

    Article  Google Scholar 

  • Narendra, K., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1, 4–27.

    Article  Google Scholar 

  • Nguyen, D., & Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In Proceedings of the International Joint Conference on Neural Networks, (pp. 21–26).

  • Oberhofer, W., & Zimmerer, T. (1996). Wie Künstliche Neuronale Netze lernen: Ein Blick in die Black Box der Backpropagation Netzwerke. Regensburger Diskussionsbeiträge 287

  • Opritescu, D., & Volk, W. (2015). Automated driving for individualized sheet metal part production—A neural network approach. Robotics and Computer-Integrated Manufacturing, 35, 144–150.

    Article  Google Scholar 

  • Pohlak, M., Majak, J., & Küttner, R. (2007). Manufacturability and limitations in incremental sheet forming. Proceedings of the Estonian Academy of Sciences and Engineering, 13, 129–139.

    Google Scholar 

  • Psaltis, D., Sideris, A., & Yamamura, A. (1988). A multilayered neural network controller. IEEE Control Systems Magazine, 8, 17–21.

    Article  Google Scholar 

  • Qattawi, A., Mayyas, A., Thiruvengadam, H., Kumar, V., Dongri, S., & Omar, M. (2014). Design considerations of flat patterns analysis techniques when applied for folding 3-d sheet metal geometries. Journal of Intelligent Manufacturing, 25, 109–128.

    Article  Google Scholar 

  • Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The rprop algorithm. In Proceedings of the IEEE International Conference on Neural Networks, (pp. 586–591).

  • Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning representations by backpropagating errors. Nature, 323, 533–536.

    Article  Google Scholar 

  • Schmidthuber, J. (2014). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

    Article  Google Scholar 

  • Sola, J., & Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44, 1464–1468.

  • Sorzano, C., Vargas, J., & Pascual-Montano, A. (2014). A survey of dimensionality reduction techniques. arXiv:1403.2877.

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overftting. Journal of Machine Learning Research, 15, 1929–1958.

    Google Scholar 

  • Taubin, G. (1995). Estimating the tensor of curvature of a surface from a polyhedral approximation. In Proceedings of the 5th International Conference on Computer Vision, (pp. 902–907).

  • Tenenbaum, J., de Silva, V., & Langford, J. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319–2323.

    Article  Google Scholar 

  • Upadhaya, B., & Eryureka, E. (1992). Application of neural network for sensor validation and plant monitoring. Nuclear Technology, 97, 170–176.

    Article  Google Scholar 

  • van der Maaten, L., & Postma, E. (2009). Dimensionality reduction: A comparative review. Technical Report TiCC TR 2009005, Tilburg centre for Creatice Computing, Tilburg University

  • Volk, W., Opritescu, D., Gritzmann, P., & Schmiedl, F. (2013). Automatisiertes Kopiertreiben Analyse und Katalogisierung von Bauteilen und Fertigungsstrategien. Hanover: European Research Association for Sheet Metal Working.

    Google Scholar 

  • Wang, T., Gao, H., & Qiu, J. (2016). A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Transactions on Neural Networks and Learning Systems, 27, 416–425.

    Article  Google Scholar 

  • Wintgen, P. (1982). Normal cycle and integral curvature for polyhedra in riemmannian manifolds. In G. Soos & J. Szenthe (Eds.), Differential geometry (pp. 805–816). Amsterdam: North-Holland.

    Google Scholar 

  • Zähle, M. (1986). Integral and current representations of federer’s curvature measures. Archiv der Mathematik, 46, 557–567.

    Article  Google Scholar 

  • Zeroudi, N., & Fontaine, M. (2015). Prediction of tool deflection and tool path compensation in ball-end milling. Journal of Intelligent Manufacturing, 26, 425–445.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christoph Hartmann.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hartmann, C., Opritescu, D. & Volk, W. An artificial neural network approach for tool path generation in incremental sheet metal free-forming. J Intell Manuf 30, 757–770 (2019). https://doi.org/10.1007/s10845-016-1279-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-016-1279-x

Keywords

Navigation