Head of the Group
Prof. Dr.-Ing. Arnold Gillner
Chair for Layer Technology LLT
The working group Compensation Methods discusses the various methods for avoiding and compensating for process defects and geometric component deviations resulting from melt flow, heat flow or solidification. Across the different processes, mechanisms for the compensation of occurring defects with respect to warpage, shrinkage, formation of surface defects and other quality-reducing effects in melt-based manufacturing processes are discussed and common solution scenarios are developed. In accordance with the structure of the SFB 1120, this working group reflects the possibilities for fulfilling the objective of reducing the defect rate and improving the manufacturing accuracy.
K1: In-situ process sizing and process control
The working group on in-situ process size measurement and process control deals with the possibilities of intervening in the various melt-related processes in order to regulate or control the process by means of in-situ process size measurement. By deepening the understanding of the different processes in project phases 1 and 2, the control strategies for phase 3 were further developed. The melting and solidification dynamics characteristic of the individual processes were evaluated and the implementation of a temporally dynamic control system was considered. In this context, the in-situ detection and determination of the selected process parameters was addressed and the appropriate sensor technology was determined, taking into account the temporal resolutions. Photodiodes are used, for example, in laser cutting to record geometric and thermal process information in real time. They can detect electromagnetic radiation in different wavelength ranges with very high temporal resolution up to the MHz range, which favors real-time process evaluation. In addition, the reliability of the measurement or the informative value of the measurement signals is correlated with in-situ process analyses, such as X-ray investigations using synchrotron radiation at the German Electron Synchrotron in Hamburg (DESY).
A01, A02, A04, A07, A08, A10
, A11, A12,
K2: Material influence / microstructure composition
The working group Material Influencing / Microstructure Composition deals with the adjustment of desired microstructures and phase formations in the solidified melt on several levels. Due to the large number of processes investigated in the work packages (e.g. brazing, welding, injection molding) and the materials used (various alloys, plastics), the focus in phase two was on a) the exchange of results achieved in the work packages and b) mutual support with analysis and simulation methods. Thus, a joint publication of A11 and B07 on the simulation of non-equilibrium solidification in laser cladding was developed from AK K2. The representative and statically meaningful evaluation of microstructures and the often insufficiently known material-specific characteristic values at high temperatures were identified as cross-TP challenges. Among other things, flash differential scanning calorimetry (SP B04) was made available for the determination of these characteristic values. In phase 3, AK K2 will continue to support the TP in implementing the knowledge gained on material/structure influence by exchanging expertise and providing the necessary analytical instruments.
Active members: A05, A06, A07, A11, A12,
K3: Thermal compensation for process control in complex geometries
Energy management in all melt-based manufacturing processes is a basic component for the precise control of component quality. To this end, Working Group K3 Thermal Compensation in Process Control in Complex Geometries is looking at the targeted thermal influence of the melt in the processes. This involves targeted energy input and energy dissipation, and the resulting effects, such as shrinkage, warpage, overheating of the melt with melt ejection and, for casting processes, molding accuracies are considered. The layer system developed in phase 2 for active temperature control and in-situ temperature measurement is being further developed across subprojects and for specific processes, see also SP A12 [E1]. This enables locally resolved energy input by means of a sensor matrix on surfaces of casting tools, for example. Furthermore, this sensor matrix can be used for spatially resolved temperature determination in the mold or in joining processes. The aim is to achieve demand-oriented dynamic temperature control to compensate for distortion and to develop process strategies for energy management by spatially resolved measurement of the temperature in the various processes.
Active members: A01, A10, A11, A12, B01, B08
K4: Shape compensation for complex geometries in injection molding and casting processes
The focus of this working group is on geometric compensation. In particular, methods are developed and applied which can also be used with complex geometries. One of the main focuses of the joint work in this working group is to make compensation methods developed for injection molding processes also usable for casting processes with metallic materials. A method for determining an optimum cooling channel layout in injection molding, which is being developed in project B01, has already been successfully adapted for gravity die casting in project B08. This made it possible to reduce the relative dispersion of the warpage. Furthermore, methods for geometric compensation by adapting the injection molding cavity are being developed in project B02. These approaches have already been tested for permanent mold casting in cooperation with project B09. In this context, there is potential for cooperation, particularly with regard to an optimization-suitable, low-dimensional geometry description, a suitable objective functional and the optimization methods used. In addition to the transfer of methods with a plastics or injection molding orientation to metallic materials and corresponding processes, there are also promising collaboration opportunities between the projects that are focused on plastics. Specifically, projects B01 and B02 aim to combine the optimization methods for the design of the cooling channel layout and the cavity geometry.
Active members: A02, B01, B02, B08, B09
K5: Melt flow influence
The working group on melt flow influence, which was newly established for Phase 3, deals specifically with the influence of flow in highly dynamic, melt-based processes. The melt flow, caused by thermal gradients in the process as well as mechanical forces and pressures, is significantly responsible for the component quality, especially the surface roughness and the pore formation. The exchange across subprojects relates to the development of compensation strategies for influencing the melt flow. Approaches for influencing the process include the process-dependent adaptation of the number of energy sources, magnetic fields or the addition of further alloying elements. The diagnostic methodology of phase contrast imaging by means of synchrotron radiation established from phase 2 is used as a central tool to evaluate and further develop derived compensation methods with respect to the melt flow.
Active members: A01, A03, A04, A07, A08, A09, A11
K6: Numerical optimization strategies for compensation measures.
Setting and maintaining a robust process window in which a defined quality is achieved is an essential element of any compensation strategy. The underlying problem is usually formulated as an inverse task, where suitable input parameters for a defined set of quality parameters have to be determined or controlled online during the process. Tools from numerical optimization are used to solve this problem. AI-based algo-rithms for forward modeling of the studied process are used to identify suitable operating points in the parameter space. Genetic algorithms can identify promising candidates for improved process control by recombining known solutions. Suitable model reduction techniques and the restriction to well-defined process domains allow an inverse formulation of the forward model in individual cases, so that the inverse task is solved directly. Compensation is complicated by measurement errors and fluctuations not captured by the model, which require a mapping of probabilities within the numerical optimization.