Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation
AbstractStandard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN) are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive—as e.g., measuring surface topography. Thus, generation of artificial (simulated) data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted in a way that a distinction of these surfaces is possible by an ANN. The ARMAsel approach is proven to provide the relevant stochastic information for the training of the ANN with artificially generated lapped, reamed, ground, horizontally milled, milled and turned surface profiles. View Full-Text
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Eifler, M.; Ströer, F.; Rief, S.; Seewig, J. Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation. Technologies 2018, 6, 3.
Eifler M, Ströer F, Rief S, Seewig J. Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation. Technologies. 2018; 6(1):3.Chicago/Turabian Style
Eifler, Matthias; Ströer, Felix; Rief, Sebastian; Seewig, Jörg. 2018. "Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation." Technologies 6, no. 1: 3.
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