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      Optimized random sampling for the load level method in Wöhler tests

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      1 , , 1 , * , 1
      Materials Testing
      Carl Hanser Verlag

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          Abstract

          For the optimal planning of Wöhler tests, DIN 50100 offers tables with different methods, such as the load level method, the pearl-string method and staircase method, to identify the required minimum sample size. Based on this norm, the sampling planning by means of the load level method was optimized in this work. Analytical formulas were developed with statistical methods. These formulas enable a fast estimation of the sample size for arbitrary use cases. Another optimization was the implementation of varying standard deviation within the area of fatigue strength in the load level method corresponding to DIN 50100. This optimization decreases the sample size compared to a constant standard deviation. Both optimizations were implemented via a MATLAB® based tool which also contains the methods of DIN 50100.

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          Most cited references8

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          FATIGUE PREDICTIONS AND SCATTER

          J. Schijve (1994)
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            Ein verbessertes Verfahren für die Auswertung von Treppenstufenversuchen

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              Über die Festigkeitsversuche mit Eisen und Stahl

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                Author and article information

                Journal
                mp
                Materials Testing
                Carl Hanser Verlag
                0025-5300
                2195-8572
                02 December 2019
                : 61
                : 12
                : 1145-1151
                Affiliations
                1 Stuttgart, Germany
                Author notes
                [* ] Correspondence Address, Stefan Jetter, Am Stettberg 47, 72336 Balingen, Germany, E-mail: stefan-jetter@ 123456gmx.de

                MSc Barbara Iglisch, was born in 1989. From 2012 to 2018 she studied Mechanical Engineering at the University of Stuttgart. After her Master's degree, she started working as a Testing Engineer at the P3 Systems GmbH in Böblingen.

                MSc Stefan Jetter, born in 1989, began his studies of Mechanical Engineering at the University of Stuttgart in 2009. After his Master's degree in 2014, he worked as a PhD student for the Daimler AG in cooperation with the Institute of Machine Components at the University of Stuttgart. Since 2017, he has been working in the chassis development at Daimler AG in Sindelfingen, responsible for the prediction of durability.

                Prof. Dr.-Ing Bernd Bertsche earned his degree and Doctorate in Mechanical Engineering at the University of Stuttgart. Afterwards, he worked for Mercedes-Benz AG in the passenger car development unit followed by a position as Professor at the University of Applied Sciences in Albstadt-Sigmaringen. Later, he received a Professorship at the University of Stuttgart. Since 2001, Professor Bertsche is the ordinary Professor and Director for the Institute of Machine Components at the University of Stuttgart.

                Article
                MP111433
                10.3139/120.111433
                b704275e-d2e5-4f88-84af-28c84247fed1
                © 2019, Carl Hanser Verlag, München
                History
                Page count
                References: 19, Pages: 7
                Categories
                Fachbeiträge/Technical Contributions

                Materials technology,Materials characterization,Materials science
                Materials technology, Materials characterization, Materials science

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