Identification of Correlations Between 3D Surfaces Using Data Mining Techniques: Predicting Springback in Sheet Metal Forming
A classification framework for identifying correlations between 3D surfaces in the context of sheet metal forming, especially Asymmetric Incremental Sheet Forming (AISF), is described. The objective is to predict “springback”, the deformation that results as a consequence of the application of a sheet metal forming processes. Central to the framework there are two proposed mechanisms to represent the geometry of 3D surfaces that are compatible with the concept of classification. The first is founded on the concept of a Local Geometry Matrix (LGM) that concisely describes the geometry surrounding a location in a 3D surface. The second, is founded on the concept of a Local Distance Measure (LDM) derived from the observation that springback is greater at locations that are away from edges and corners. The representations have been built into a classification framework directed at the prediction of springback values. The proposed framework and representations have been evaluated using two surfaces, a small and a large flat-topped pyramid, and by considering a variety of classification mechanisms and parameter settings.
Unable to display preview. Download preview PDF.
- 4.W. Cohen. Fast effective rule induction. In Twelfth International Conference on Machine Learning, pages 115–123. Morgan Kaufmann, 1995.Google Scholar
- 6.Z. Fu, J. Mo, L. Chen, andW. Chen. Using Genetic Algorithm-Back Propagation Neural Network Prediction and Finite-Element Model Simulation to Optimize the Process of Multiple- Step Incremental Air-Bending Forming of Sheet Metal. Materials and Design, 31(1):267 – 277, 2010.Google Scholar
- 7.Z. Guo, L. Zhang, and D. Zhang. A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing, 19(6):1657–1663, 2010. 8. W. Hao and S. Duncan. Optimization of Tool Trajectory for Incremental Sheet FormingGoogle Scholar
- 8.Using Closed Loop Control. In Automation Science and Engineering (CASE), 2011 IEEE Conference on, pages 779 –784, 2011.Google Scholar
- 10.G. John and P. Langley. Estimating Continuous Distributions in Bayesian Classifiers. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338–345, San Mateo, 1995. Morgan Kaufmann.Google Scholar
- 12.W. Liu, Z. Liang, T. Huang, Y. Chen, and J. Lian. Process Optimal Ccontrol of Sheet Metal Forming Springback Based on Evolutionary Strategy. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, pages 7940 –7945, June 2008.Google Scholar
- 15.V. Nasrollahi and B. Arezoo. Prediction of Springback in Sheet Metal Components With Holes on the Bending Area, Using Experiments, Finite Element and Neural Networks. Materials and Design, 36:331 – 336, 2012.Google Scholar
- 16.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.Google Scholar