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Documents authored by Ravani, Bahram

Document
Complete Volume
DOI: 10.4230/OASIcs.iPMVM.2020
OASIcs, Volume 89, iPMVM 2020, Complete Volume

Authors: Christoph Garth, Jan C. Aurich, Barbara Linke, Ralf Müller, Bahram Ravani, Gunther H. Weber, and Benjamin Kirsch

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
OASIcs, Volume 89, iPMVM 2020, Complete Volume
Cite as

2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 1-364, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Proceedings{garth_et_al:OASIcs.iPMVM.2020,
  title =	{{OASIcs, Volume 89, iPMVM 2020, Complete Volume}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{1--364},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020},
  URN =		{urn:nbn:de:0030-drops-137486},
  doi =		{10.4230/OASIcs.iPMVM.2020},
  annote =	{Keywords: OASIcs, Volume 89, iPMVM 2020, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.iPMVM.2020.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Christoph Garth, Jan C. Aurich, Barbara Linke, Ralf Müller, Bahram Ravani, Gunther H. Weber, and Benjamin Kirsch

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{garth_et_al:OASIcs.iPMVM.2020.0,
  author =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{0:i--0:xii},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.0},
  URN =		{urn:nbn:de:0030-drops-137497},
  doi =		{10.4230/OASIcs.iPMVM.2020.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.iPMVM.2020.8
Determination of Aggregate Elastic Properties of Powder-Beds in Additive Manufacturing Using Convolutional Neural Networks

Authors: Ardalan R. Sofi and Bahram Ravani

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
The most popular strategy for the estimation of effective elastic properties of powder-beds in Additively Manufactured structures (AM structures) is through either the Finite Element Method (FEM) or the Discrete Element Method (DEM). Both of these techniques, however, are computationally expensive for practical applications. This paper presents a novel Convolutional Neural Network (CNN) regression approach to estimate the effective elastic properties of powder-beds in AM structures. In this approach, the time-consuming DEM is used for CNN training purposes and not at run time. The DEM is used to model the interactions of powder particles and to evaluate the macro-level continuum-mechanical state variables (volume average of stress and strain). For the Neural Network training purposes, the DEM code creates a dataset, including hundreds of AM structures with their corresponding mechanical properties. The approach utilizes methods from deep learning to train a CNN capable of reducing the computational time needed to predict the effective elastic properties of the aggregate. The saving in computational time could reach 99.9995% compared to DEM, and on average, the difference in predicted effective elastic properties between the DEM code and trained CNN is less than 4%. The resulting sub-second level computational time can be considered as a step towards the development of a near real-time process control system capable of predicting the effective elastic properties of the aggregate at any given stage of the manufacturing process.
Cite as

Ardalan R. Sofi and Bahram Ravani. Determination of Aggregate Elastic Properties of Powder-Beds in Additive Manufacturing Using Convolutional Neural Networks. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{sofi_et_al:OASIcs.iPMVM.2020.8,
  author =	{Sofi, Ardalan R. and Ravani, Bahram},
  title =	{{Determination of Aggregate Elastic Properties of Powder-Beds in Additive Manufacturing Using Convolutional Neural Networks}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{8:1--8:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.8},
  URN =		{urn:nbn:de:0030-drops-137574},
  doi =		{10.4230/OASIcs.iPMVM.2020.8},
  annote =	{Keywords: Additive Manufacturing, Convolutional Neural Network, Homogenization, Discrete Element Method, Powder-Bed}
}
Document
DOI: 10.4230/OASIcs.iPMVM.2020.15
Physics Simulation of Material Flows: Effects on the Performance of a Production System

Authors: Moritz Glatt, Bahram Ravani, and Jan C. Aurich

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
In cyber-physical production systems, material flows show complexity due to varying physical aspects of transported work pieces and autonomously selected transport routes. As a result, physically induced disturbances that may lead to delays or damages are hard to predict. The on-line usage of a physics engine offers potential to derive material flow parameters that enable safe transports with optimized accelerations. Previous work showed the feasibility of this approach and potential operational benefits through faster material flows. In consequence, the scope of this paper is to apply discrete-event simulation to investigate whether physics simulation of material flows leads to positive impacts on production system performance indicators such as throughput times and capacity utilization. The results indicate that increased velocity and acceleration of material flows can positively influence these indicators. In consequence, applying physics simulation to ensure safe transports with such high velocities and accelerations can improve the overall performance of a production system.
Cite as

Moritz Glatt, Bahram Ravani, and Jan C. Aurich. Physics Simulation of Material Flows: Effects on the Performance of a Production System. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 15:1-15:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{glatt_et_al:OASIcs.iPMVM.2020.15,
  author =	{Glatt, Moritz and Ravani, Bahram and Aurich, Jan C.},
  title =	{{Physics Simulation of Material Flows: Effects on the Performance of a Production System}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{15:1--15:26},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.15},
  URN =		{urn:nbn:de:0030-drops-137640},
  doi =		{10.4230/OASIcs.iPMVM.2020.15},
  annote =	{Keywords: Physics simulation, discrete-event simulation, cyber-physical production systems}
}
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