Surrogate Modelling as an Enabler for Self Optimisation for Production Processes

To persist the ever-increasing market challenges, production processes must be capable to raise their flexibility without incremented expenses. Therefore, the topic of self optimisation for production processes is gaining more and more in importance. In this paper, a process independent method for self optimisation with focus on surrogate modelling is presented. Especially the creation of surrogate models and the possibilities of model evaluation to improve optimisation methods will be a central theme. All described methods will be illustrated by reference to examples from the gas metal arc welding process.

[1]  Christian Bauckhage,et al.  Towards 3D Internet: Why, What, and How? , 2007, CW 2007.

[2]  Alan Bundy,et al.  Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence - IJCAI-95 , 1995 .

[3]  Junpeng Guo,et al.  Normalization of interval symbolic data , 2009, 2009 16th International Conference on Industrial Engineering and Engineering Management.

[4]  Rolf Isermann,et al.  Mechatronic Systems: Fundamentals , 2003 .

[5]  Dirk Gorissen,et al.  Sequential modeling of a low noise amplifier with neural networks and active learning , 2009, Neural Computing and Applications.

[6]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[7]  Edward R. Dougherty,et al.  Coefficient of determination in nonlinear signal processing , 2000, Signal Process..

[8]  J. Gausemeier.  Designing Tomorrow's Mechanical Engineering Products , 2005, 2005 International Conference on Information and Communication Technology.

[9]  Robert G. Sargent,et al.  Verification and validation of simulation models , 2009, IEEE Engineering Management Review.

[10]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[11]  Vicente J. Romero,et al.  Comparison of pure and "Latinized" centroidal Voronoi tessellation against various other statistical sampling methods , 2006, Reliab. Eng. Syst. Saf..

[12]  Andreas Zell,et al.  Design of the SNNS Neural Network Simulator , 1991, ÖGAI.

[13]  U. Reisgen,et al.  Strategy and approach towards application of self-optimization in complex gas-metal arc welding processes , 2010, 2010 8th IEEE International Conference on Industrial Informatics.

[14]  Gunnar Abrahamson,et al.  Terminology for model credibility , 1980 .

[15]  Dennis K. J. Lin,et al.  A construction method for orthogonal Latin hypercube designs , 2006 .