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Data-efficient Optimization of the Thermal Spray Coating Process
Thermal spray technology is used to coat critical parts in industrial machines and products, from turbine blades in jet engines to brake disks in automobiles to hard-chrome replacement, which require the highest achievable quality of the coating. Optimization of the thermal spray process to consistently achieve this high quality is challenging due to its stochastic, non-linear and transient behavior. Physical modelling is therefore at its limits, and data-driven techniques could be used to identify the process. The overall system can be divided into several sub-systems, and the input-output relationship in each subsystem can be estimated from data acquired at each stage with a data-driven approach using learning techniques in combination with empirical modeling.
Keywords: Optimzation; Thermal Spray; Coating;
The task of this thesis is to establish a general process understanding of the thermal spray process and build a data-driven process model which can be used for a subsequent optimization, using probabilistic modeling which requires only a limited amount of data. The optimization shall be performed on the model and depending on availability on a machine.
The task of this thesis is to establish a general process understanding of the thermal spray process and build a data-driven process model which can be used for a subsequent optimization, using probabilistic modeling which requires only a limited amount of data. The optimization shall be performed on the model and depending on availability on a machine.
- Literature review on thermal spray coating
- Establish process understanding of the thermal spray process, relating significant tuning parameters with production costs, process and product constraints.
- Build a data-driven model of the thermal spray coating process (neural networks, empirical models, physical models, etc.)
- Perform process optimization on the model and extend the optimization on a real machine (depending on availability)
- Literature review on thermal spray coating - Establish process understanding of the thermal spray process, relating significant tuning parameters with production costs, process and product constraints. - Build a data-driven model of the thermal spray coating process (neural networks, empirical models, physical models, etc.) - Perform process optimization on the model and extend the optimization on a real machine (depending on availability)