| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Thiede, Sebastian
University of Twente
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2022Integration of Energy Oriented Manufacturing Simulation into the Life Cycle Evaluation of Lightweight Body Partscitations
- 2021Machine learning and simulation-based surrogate modeling for improved process chain operationcitations
- 2021Modeling energy and resource use in additive manufacturing of automotive series parts with multi-jet fusion and selective laser sinteringcitations
- 2020Modeling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteriescitations
- 2020Industrie 4.0 in der Galvanotechnik
- 2020Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network
- 2020Integrated computational product and production engineering for multi-material lightweight structurescitations
- 2020Agent-Based Simulation Approach for Occupational Safety and Health Planningcitations
- 2020Model-based analysis, control and dosing of electroplating electrolytescitations
- 2019Modelling the Impact of Manufacturing Uncertainties on Lithium-Ion Batteriescitations
- 2012A hierarchical evaluation scheme for industrial process chainscitations
- 2011Synergies from process and energy oriented process chain simulation - A case study from the aluminium die casting industrycitations
Places of action
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document
Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network
Abstract
The production of lithium-ion battery cells is characterized by a high degree of complexity due to numerous cause-effect relationships between process characteristics. Knowledge about the multi-stage production is spread among several experts, rendering tasks as failure analysis challenging. In this paper, a new method is presented that includes expert knowledge acquisition in production ramp-up by combining Failure Mode and Effects Analysis (FMEA) with a Bayesian Network. Special algorithms are presented that help detect and resolve inconsistencies between the expert-provided parameters which are bound to occur when collecting knowledge from several process experts. We show the effectiveness of this holistic method by building up a large scale, cross-process Bayesian Failure Network in lithium-ion battery production and its application for root cause analysis.