Abstract
Design of Experiment (DOE) is a widely used method for examining experiments especially in industrial production and robust design processes. This method is a set of statistical approaches in which mathematical models are developed through experimental testing to estimate possible outputs and given input values or parameters. The method aims to determine the main factors that affect the results with the smallest number of experimental studies. In this study, L16 (2^15) orthogonal array, which was used in the Taguchi parameter design was reconstructed with the Support Vector Machines learning model and the Pearson VII kernel function. With this model, array elements were successfully classified in 87.04%. The new and original array were compared and 3.8% difference was measured between their Signal to Noise (S / N) ratios in an exemplary experiment.
Highlights
ODAY, IN many manufacturing applications, Taguchi’s orthogonal array catalogs are used for industrial designs.Genichi Taguchi redesigned offline quality control methods [1], which were developed in Japan after World War II in the 1980s under the name of robust design in AT & T Bell laboratories.This method is generally called Taguchi orthogonal array design
In order to reconstruct the L16 (2 15) Taguchi array, we formed the classification model by using the support vector machine model which belongs to L4 (2 3), L8 (27), L8 (24), and L12 (211) 2-level arrays [21]
During the creation of the model, we tested with various kernel functions that were used for Support vector machines (SVM)
Summary
Abstract—Design of Experiment (DOE) is a widely used method for examining experiments especially in industrial production and robust design processes. This method is a set of statistical approaches in which mathematical models are developed through experimental testing to estimate possible outputs and given input values or parameters. L16 (2 15) orthogonal array, which was used in the Taguchi parameter design was reconstructed with the Support Vector Machines learning model and the Pearson VII kernel function. With this model, array elements were successfully classified in 87.04%.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Balkan Journal of Electrical and Computer Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
