Data- and Response-Surface-Driven Design Assistant for Controlled Flexible Multibody Systems

• generic methods for multibody system modeling and optimization
• modeling, model-order reduction and simulation of elastic multibody systems
• control strategies, especially optimal control based on model predictive control

• sensitivity analysis and multi-objective optimization of multibody systems
• design concepts for complex technical systems on industrial scale (turbo machinery and automotive)
• metamodeling and AI strategies for design

The presented design assistant allows for an almost instantaneous design of path-specific four-bar linkages. In a first step, the user draws a target path which should be followed. A neural network is then queried on the backend and the proposed mechanism with its corresponding path are plotted. During a second step, the user can define a desired velocity profile for the automatic generation of a suitable controller. For the entire pipeline no profound mechanism knowledge is required as long as data can be collected by simulation.

• specific machine learning methods for learning multi-modal solution spaces while fulfilling physical constraints
• effective parameterizations of two- and three-dimensional workspaces
• experience on sensor fusion of different measurement instruments
• tell us about your mechanical and control design problems

• experience in elastic multibody system modeling, model-order reduction and simulation
• control concepts for multibody systems
• experience in multi-criteria optimization, problem formalization and process integration
• experience with design parameterization, function approximation and statistical sensitivity analysis
• theoretical and practical machine learning experience

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