ResearchProject Area B
Subproject B2

Subproject B2 Dexterous Regeneration Cell


MOTIVATION AND OBJECTIVES

Repair cell with electromagnetic guide

Complex repair components, such as tools for foundry and forming technology, components from aircraft engines or gas and steam turbines usually have free-form geometry features, individual material deposits and poor accessibility. The re-contouring of such components requires increased machine and process flexibility.

In subproject B2, a dexterous repair cell is being researched which represents the central component of the real regeneration path in the SFB 871. "Dexterousness" is understood as the ability to carry out a self-optimizing, best possible repair machining. The combination of novel machine functionalities and adapted cutting technologies enables the reliable 5-axis re-contouring of individual damage cases despite repair-specific variances, influences from upstream processes and flexibility of the workpiece or tool.

The video shows the 5-axis re-contouring of a turbine blade, which was repaired at the tip.

RESULTS

The prior work resulted in a machine demonstrator, which integrates the novel process technologies and machine functionalities. For the re-contouring of flexible and vibration-prone components, basic knowledge on tool selection, milling strategy and process control was developed and transferred to process planning. The suitability of electromagnetic guides for repair machining was evaluated. Methods for force reconstruction and control by means of electromagnetic guides were developed and applied to compensate for process force-related displacements. Critical process states can be detected and suppressed automatically during operation by adjusting the process parameters.

Multidimensional stability maps for process design

CURRENT RESEARCH AND OUTLOOK

Minimizing shape deviations despite varying workpiece properties is the main objective of subproject B2. Methods will be developed to identify local variances during machining. Tracing back the variances and identifying the causes will lead to an enhanced process planning for upcoming repair cases. Therefore, the dexterous machine tool will be improved by increased sensory abilities as well as artificial intelligence. A self-learning process force model is being researched in order to enable a precise prognosis of the process forces despite the individuality of the repair cases and finally to identify variant-related deviations in the process. On the machine side, a control architecture is being researched that makes it possible to combine data from NC control, machine-integrated sensor technology and process simulation. This is the prerequisite for learning the force model and for identifying local workpiece variants. For predictive control, position-dependent control values must be determined from the identified variances. In cooperation with TP C1, measures for improved process control are derived and interactions with upstream and downstream machining steps are identified. In this way, the control interventions can be optimized with regard to the machining result. In addition, an intelligent workpiece carrier is being researched that has multifunctional sensors for process monitoring along the entire repair path.


PUBLICATIONS

  • Denkena, B.; Boess, V.; Nespor, D.; Floeter, F.; Rust, F. (2015) Engine blade regeneration: a literature review on common technologies in terms of machiningInt J Adv Manuf Technol 81 (5-8), S. 917–924
    DOI: 10.1007/s00170-015-7256-2
  • Flöter, F.; Denkena, B. (2015) Analysis of Chatter Vibration and Tool Deflection in Milling with a Novel Active Machine Tool GuideIn: Applied Mechanics and Materials 794, S. 331–338
    DOI: 10.4028/www.scientific.net/AMM.794.331
  • Denkena, B.; Gümmer, O.; Flöter, F. (2014) Evaluation of electromagnetic guides in machine toolsCirp Annals 63, S. 357–360
    DOI: 10.1016/j.cirp.2014.03.130
  • Denkena, B.; Flöter, F. (2013) Adaptive Cutting Force Control with a Hybrid Axis SystemInternational Journal of Automation Technology 7 (4), S. 379–384
All publications of the Collaborative Research Centre

SUBPROJECT LEADER

Prof. Dr.-Ing. Berend Denkena
Address
An der Universität 2
30823 Garbsen
Building
Room
113
Address
An der Universität 2
30823 Garbsen
Building
Room
113

STAFF

Dipl.-Ing. Tim Schumacher
Address
An der Universität 2
30823 Garbsen
Address
An der Universität 2
30823 Garbsen
M. Sc. Arne Mücke
Address
An der Universität 2
30823 Garbsen
Address
An der Universität 2
30823 Garbsen