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Projects

Virtual Machining

Especially in the age of digitalization, the virtualization of manufacturing processes is becoming more and more important. The development of digital twins and digital shadows offers the possibility to design, analyze and adapt processes virtually. The aim of the funded project is to establish a new focus of "Virtual Machining", in which the disciplines of computer science and mechanical engineering are interdisciplinarily combined.

The initial projects include a close combination of the disciplines of materials engineering, mechanics and virtual machining along the materials chain in order to develop new models for process simulation taking material history into account.

In the context of this UA Ruhr research project, which is funded by the Mercator Research Center Ruhr and the Mercator Foundation, a cooperation with the Chair of Materials Engineering (LWT) of the Ruhr-University Bochum and the Institute of Mechanics of the University Duisburg-Essen is formed to investigate the chip formation process along the material chain.

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Material characterization
UAR_22
Material simulation
UAR_2.5
Chip formation simulation
UAR_3
Process simulation
UAR_4
Process optimization

Contact: Jim Bergmann, Florian Wöste


Stochastic Modeling of the Interaction of Tool Wear and the Machining Affected Zone in Nickel-Based Superalloys, and Application in Dynamic Stability

An important factor for the aerospace industry is the application of sophisticated materials meeting the high standards with regard to high temperatures and the pressure requirements. With its high level of strength and corrosion resistance, Inconel 718 is such a material. Machining these materials is a special challenge due to its high tool wear and occurring process forces. One possibility to minimize the tool wear and the process forces during machining is the usage of so-called trochoidal milling.

Simulation systems can be used to predict the process as accurately as possible. In addition, the simulation system can be used for the analysis and the process design. Therefore, appropriate models have to be developed, with which, for example, a description of the tool wear evolution is possible. The aim of this project is to analyze the interrelationships between the machining parameters and the tool wear evolution and to investigate their influence on the output machining affected zone (MAZ) depth. Through these fundamental investigations, models will be developed, with which a prediction of the process forces, the stabilities, and the resulting workpiece topographies are possible.

This research project is in cooperation with the Clemson University in South Carolina (USA) and is funded by the German Research Foundation (DFG) on the German side and on the American side by the National Science Foundation (NSF).

Laine_1

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Analysis of wear

Simulation

 Contact: Nils Potthoff


Modeling and simulation of the NC grinding process for the controlled generation of workpiece surfaces under consideration of tool topography and wear

Surface structuring of functional surfaces is relevant for many applications. An example is the structuring of forming tools in order to influence the material flow in the forming process. NC grinding on machining centers is a flexible process, which can be used for structuring freeform workpiece surfaces.

In order to design grinding processes for achieving specific surface structures, a geometric physically-based simulation system is developed in this project. This simulation can be used to predict the resulting surface topographies by taking the individual grains on the grinding tools as well as different process effects into account. The tool model is based on a stochastic distribution of grain models from a database, which was initialized by analyzing the surface topographies of real grinding tools. By conducting these measurements in different states of tool wear, these wear states can be taken into account in the simulation. Additionally, the quasi-static compliance and run-out of the tools were modeled. Due to the stochastic distribution of grain instances from a representative database, various tool shapes, e. g. cylinders, sphere, or cones, can be modeled without conducting further calibration experiments.

This research project is a collaboration with the Institute of Machining Technology and funded by the German Research Foundation (DFG).

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Simulation eines Schleifwerkzeugs (links) und Modellierung des Verschleißes einzelner Körner (rechts)

 Ansprechparnter: Tobias Siebrecht


Data Mining on Sensor Data of Automated Processes (SFB 876 – Project B3)

Project B3 is a cooperation project between the Chair 8 for Artificial Intelligence (LS8) and the “Virtual Machining” group of the Chair 14 for Software Engineering (LS14) of the Department of Computer Science and the Institute for Production Systems (IPS) of the Faculty of Mechanical Engineering. It investigates data mining in sensor data for quality prognosis and control of production processes under real-time conditions. In order to meet production requirements, the third phase will develop methods that can adapt to changes in process conditions online. The focus is both on the optimization of individual processes based on model predictions using the example of NC milling, which is realized through cooperation between the LS8 and the LS14, and on organizational improvements along entire process chains. The optimization of milling processes requires methods for the online adaption of parameter values on the machining center. In order to enable real-time predictions, various methods for aggregation and feature extraction from time series for online application are being investigated. While large quantities of data are usually available for learning the models in mass production, these would first have to be obtained for individual/small series production with high resource requirements. For this reason, methods are to be developed to train learning models on simulation data first and adapted them on the basis of fewer measurement data. The use of process simulations also offers the possibility of analyzing the limits of model predictions, the experimental investigation of which cannot be carried out for safety reasons. In addition, the learned models are to be adapted online based on the detection of process changes. For this purpose, methods are to be developed which recognize process-related shifts in the distribution in the running process and continuously adapt learned models for subsequent processes in real time.

 

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 Ansprechparnter: Felix Finkeldey


Modeling of the axis position-dependent process dynamics for simulation-supported optimization of NC milling machining of freeform surfaces

The constantly growing demands on manufacturing technology often require the execution of 5-axis machining. The planning of 5-axis processes in production is complex. The dynamic behavior of the tool-machine system also presents a challenge in terms of avoiding the regenerative effect due to the constantly changing engagement situation between tool and workpiece. Simulation systems are used in the development of corresponding manufacturing processes in order to shorten the planning and start-up phases. 

In this research project, a system for simulation-supported stability optimization of 5-axis processes is being developed taking into account the axis position-dependent dynamic behavior of the tool-machine system, the acceleration behavior of the axes and the collision areas.

 This research project is carried out in cooperation with the Institute of Machining Technology and is funded by the German Research Foundation (DFG).

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 Ansprechparnter: Ines Wilck


Current projects and research work at ISF, RG "Simulation and Process Design"