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Logo: Institut für Kontinuumsmechanik/Leibniz Universität Hannover
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Logo: Institut für Kontinuumsmechanik/Leibniz Universität Hannover
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Using Artificial Intelligence to Realistically Model Complex Dynamic Systems in Real-Time

Project coordinators: Christian Weißenfels, Peter Wriggers

Simulation driven engineering is an essential part in the development process and a key component in the digitalization of industry. Based on virtual replicas of real dynamic systems engineers can develop optimal patient specific implants, sustainable automotive technologies or light weighted long-life batteries.

However for a reliable prediction using virtual replicas accurate models and solution schemes are a prerequisite. Many mathematical models have been formulated in recent years in order to reproduce the real behavior as accurately as possible. Additionally, new solution schemes were developed in order to reduce the computational effort and to increase the range of applications. Nevertheless even with these improved approaches a strong mismatch between simulation output and real physical behavior is observed in many cases.

Artificial neural networks have the potential to close this gap.These algorithms from Machine Learning are well suited to improve the computational models by directly including experimental or onboard measured data. Hence the simulation output of the virtual replica automatically assimilates to the real physical behavior. Additionally, artificial neural networks can enable real-time simulations by solving differential equations directly without using time consuming solution schemes.

Providing these enhanced modeling concepts the digital twin concept can become reality. For each dynamic system a virtual replica is build which automatically improves by comparison with real physical data. It can be used in the design process to develop improved dynamic systems or for life cycle predictions. Furthermore the digital twin can give machines a mind allowing them to make decisions self-dependly.

 

Projekte: Künstliche Intelligenz

Using Machine Learning to Improve the Modelling of Machining and Cutting Processes

Bild zum Projekt Using Machine Learning to Improve the Modelling of Machining and Cutting Processes

Leitung:

C. Weißenfels, P. Wriggers

Bearbeitung:

M.Sc. Dengpeng Huang

Förderung durch:

China Scholarship Council (CSC)

Kurzbeschreibung:

Metal cutting is a fundamental process in industrial production. The fast and accurate on-line prediction of metal cutting processes is crucial for the Intelligent Manufacturing (IM). With the advent of high-speed computing, robust numerical algorithms and machine learning technology, computational modelling serves as a tool for not only accurate but also fast predicting the complex machining processes and understanding the complex physics. In this work, the machine learning based numerical model is developed for simulation of metal cutting processes.

 

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