PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Philipp Mell Co-author(s): Marco Arndt; marco.arndt@ima.uni-stuttgart.de
Martin Dazer; martin.dazer@ima.uni-stuttgart.de
| Non-orthogonality in DoE: practical relevance of the theoretical concept in terms of regression quality and test plan efficiency |
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| Whenever scientific problems aren’t well understood in their physical properties or cannot be solved analytically, the approach of statistical design of experiments (DoE) is the only alternative. Yet, many DoE approaches are mathematically derived and underly assumptions and restrictions which might be hard or even impossible to be met in practice. Therefore, numerous research gaps regarding the practical implementation of DoE test plans remain. One typical requirement is that the experimental design has to be orthogonal. This condition demands that the investigated factors be set exactly to the given factor levels. This is usually not impossible, inevitably leading to deviations from the ideal condition. The literature therefore suggests a number of different metrics to measure the non-orthogonality of a test plan, which are presented and compared in this paper. The question arises, how crucial the impact of a particular deviation from the ideal orthogonal design is. This can be assessed by studying two central quantities of a DoE test plan: First, the power, which shows how likely an existing effect is to be identified. Second, the accuracy of the estimated model parameters resulting from the regression model developed from the test results. The scope of this paper is the assessment of these two quantities for typical deviations from a perfectly orthogonal full factorial test plan, allowing a transfer between theoretical requirements and practical use. In the long term, the results can be utilized to make test plans more efficient by suggesting which cost-reducing types of non-orthogonality still produce acceptable results. In order to achieve this goal, a parameter study for several exemplary systems is performed in a Monte Carlo simulation. For both the orthogonal and non-orthogonal test plans, linear regression and significance analysis is performed in each iteration. After calculating the changes in test power and regression accuracy, it is assessed how crucial the different types of non-orthogonality are. Also, the results are compared with different non-orthogonality measures, to see which of them serves best in predicting the practical value of a DoE test plan. |
Paper PH20 Preview
Author and Presentation Info
Lead Author Name: Philipp Mell (philipp.mell@ima.uni-stuttgart.de)
Bio: Philipp Mell studied Automotive Engineering at the University of Stuttgart, Germany and received his academic degree Master of Science in 2020. He is working as a research assistant in the field of reliability engineering at the Institute of Machine Components. He is pursuing his PhD studies with a focus on life testing and statistical test planning. His further interests lie in the application of ML methods in reliability engineering.
Country: Germany
Company: Institute of Machine Components, University of Stuttgart
Job Title: Research assistant
Bio: Philipp Mell studied Automotive Engineering at the University of Stuttgart, Germany and received his academic degree Master of Science in 2020. He is working as a research assistant in the field of reliability engineering at the Institute of Machine Components. He is pursuing his PhD studies with a focus on life testing and statistical test planning. His further interests lie in the application of ML methods in reliability engineering.
Country: Germany
Company: Institute of Machine Components, University of Stuttgart
Job Title: Research assistant