The student Laura Muñiz Garcia obtained an EXCELLENT CUM LAUDE
The student Laura Muñiz Garcia obtained an EXCELLENT CUM LAUDE
The student Laura Muñiz Garcia obtained an EXCELLENT CUM LAUDE
- Thesis title: Development of intelligent deep drawing and bending processes through model-based adaptive process control
Court:
- Presidency: Javad Hazrati Marangalou (University of Twente)
- Vocal:Eduardo Garcia Magraner (Ford Motor Company)
- Vocal: Naiara Ortega Rodríguez (UPV/EHU)
- Vocal: Eneko Saenz de Argandoña (Mondragon Unibertsitatea)
- Secretary:Nagore Otegi Martinez (Mondragon Unibertsitatea)
Abstract:
The increased complexity of geometries and the improved properties of sheet metal components result in narrower process windows, highlighting the need for better process control to minimize deviations and to ensure the production of high-quality parts. For a process to be controllable, part quality indicators need to be both detectable and measurable. These indicators must be modifiable through another process variable, for example an actuator. The process adjustment can be made manually using operator expertise or automatically, using advanced controllers and expert systems.
The main objective of this thesis is to develop metamodel-based process control systems for bending and deep drawing applications to reduce defective parts. Therefore, the research is divided into two use cases: the U-bending process for a seat rail component and the deep drawing process for the inner door panel of the FORD Kuga.
The U-bending use case introduces modern controllers to regulate the bending angle after springback of a seat rail component made by a TIER1 company. The component is produced using either cold-rolled dual-phase DP980 or complex-phase CP980 high-strength steels, depending on material availability. Material fluctuations caused significant variability in the final bending angle. Both classical controllers and advanced controllers, combining a classical approach with a metamodel-based feedforward term, were developed and validated in an industrial servomechanical press.
In the stamping use case, two approaches were applied. First, a sensitivity analysis and metamodel-based multi-objective optimization control using variational simulations addressed mechanical and frictional fluctuations. Since direct measurement of key quality indicators like thinning or wrinkling was not possible in reality, draw-in was identified as a valid observable parameter through correlation analysis. Draw-in objectives and controllable variables were defined, and the control was validated with simulation data.
In the second approach, a sensitivity analysis and metamodel-based stepwise optimization control was developed using experimental data from the FORD Almussafes press line. Key parameters were incorporated into a metamodel-based optimization after preprocessing. Extensive machine learning tests identified the best fit. Draw-in, measured at the most representative failure point, was the observable variable, while cushion force and shim height were controllable variables. Virtual scenario testing produced promising results, and the process window was successfully validated on the press line.
This PhD thesis provides insights to control sheet metal forming processes, reducing defect rates and optimizing production. It emphasizes the importance of identifying the effects of controllable and uncontrollable variables through sensitivity analysis, as well as correctly identifying observable variables.