The nature of statistical learning theory
The nature of statistical learning theory
Machine vision system for curved surface inspection
Machine Vision and Applications - special issue on high performance computing for industrial visual inspection
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Design and Analysis of Experiments
Design and Analysis of Experiments
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In presented paper a design model is developed which is used to find an optimal design space in order to improve precision and accuracy of reversed 3D models. Significant parameters and their mutual effects in scanning process are recognized with the help of Design of Experiments (DOE) principles, and in second step recognized significant parameters are also optimized using the RSM (Response Surface Methodology). Back propagated neural network is used for prediction of response factors whose input variables are not possible to set during experimentation due to the nature of the scan device. With neural network (NN) predicted response-design data, the Analysis of Variance (ANOVA) test was performed. On a basis of significant parameters the analytical model is established, which enables prediction and optimization of critical scan parameters, according to the prescribed standard deviation (abberation) of analysed 3D CAD models. After getting knowledge about the relationships between significant parameters it is possible to predict and optimize them using a model established in the RSM on a basis of D-optimal Design of Experiment test (DOE). At the end also the precision assessment of predicted response factors by DOE approach and by neural network approach is done and results are beeing compared.