https://puma.ub.uni-stuttgart.de/user/bertapiPUMA RSS feed for /user/bertapi2026-04-21T12:25:47+02:00https://puma.ub.uni-stuttgart.de/bibtex/2312810d1ef93e404652159e2824d6cf8/bertapibertapi2024-05-13T22:52:38+02:00auxetics imported isd myown rg-expmech-enveng <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Berta Pi Savall" itemprop="url" href="/person/148cfa46f28dfa99af93750946212e5cf/author/0"><span itemprop="name">B. Pi Savall</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Seyed Morteza Seyedpour" itemprop="url" href="/person/148cfa46f28dfa99af93750946212e5cf/author/1"><span itemprop="name">S. Seyedpour</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tim Ricken" itemprop="url" href="/person/148cfa46f28dfa99af93750946212e5cf/author/2"><span itemprop="name">T. Ricken</span></a></span></span>. </span><span class="additional-entrytype-information"><em>Seite <span itemprop="pagination">85--102</span>. </em><em><span itemprop="publisher">Springer Nature Switzerland</span>, </em><em>Cham, </em><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span>(<em><span>2024<meta content="2024" itemprop="datePublished"/></span></em>)</span>Mon May 13 22:52:38 CEST 2024ChamLectures Notes on Advanced Structured Materials 285--102Experimental Analysis of Strain and Thermal Behaviour on 3D Printed Flexible Auxetic Structures2024auxetics imported isd myown rg-expmech-enveng The unique features conferred by negative Poisson's ratio have made auxetic materials the focus of considerable attention among mechanical metamaterials. This study experimentally investigates the mechanical properties of a 3D printed soft auxetic structure under tensile loading conditions, highlighting the strain and temperature distributions and the dynamic Poisson's ratio, focusing on the elongation domain with the maximum auxetic effect. The study uses a combination of experimental testing with a DIC system to measure the strain field and an IRT camera to record temperatures over time. The study results show that the mechanical properties of auxetic structures can be controlled by adjusting the design and material parameters. This research contributes to understanding the behaviour of flexible auxetic structures and can be used in several fields, such as aerospace, biomedical engineering, and energy storage.https://puma.ub.uni-stuttgart.de/bibtex/245f5f89ee91731c1d5157d570b8b46b9/bertapibertapi2021-12-15T14:15:17+01:00ffnn lstm machine-learning myown plasticity stress-strain-curve <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Berta Pi Savall" itemprop="url" href="/person/12341f824a173435e1b3f360d80f507c2/author/0"><span itemprop="name">B. Pi Savall</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="André Mielke" itemprop="url" href="/person/12341f824a173435e1b3f360d80f507c2/author/1"><span itemprop="name">A. Mielke</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tim Ricken" itemprop="url" href="/person/12341f824a173435e1b3f360d80f507c2/author/2"><span itemprop="name">T. Ricken</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">PAMM</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">21 </span></span>(<span itemprop="issueNumber">1</span>): <span itemprop="pagination">e202100225</span></em> </span>(<em><span>2021<meta content="2021" itemprop="datePublished"/></span></em>)</span>Wed Dec 15 14:15:17 CET 2021PAMM1e202100225Data-Driven Stress Prediction for Thermoplastic Materials212021ffnn lstm machine-learning myown plasticity stress-strain-curve Abstract The present study applies two different machine learning (ML) algorithms to predict the stress-strain mapping for the non-linear behaviour of thermoplastic materials: a Long Short-Term Memory (LSTM) algorithm and a Feed-Forward Neural Network (FFNN). The approach of this work requires the generation of the stress-strain curve for specific material parameters. The training data are obtained from the von Mises material law and the Ramberg-Osgood equation. The four combinations of ML algorithms with constitutive laws are evaluated and show a good agreement with numerical data.