State- and Parameter-space Exploration and Process Optimisation

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The objective of this project is the reduction of the number of samples required for the optimisation of the parameters of multi-stage production processes. Beyond the state of the art, the following common properties of manufacturing processes are exploited in this project. (i) Multi-stage production processes allow the observation of early subprocesses and their results, which can be used for targeted online optimisation of the parameters of later stages. To make the best use of the available samples, Bayesian Optimisation – which can integrate probabilistic prior models and expert knowledge – is extended for the multi-stage setting. (ii) Finite-dimensional process parameters can also describe trajectories for feedforward control. Here the spatial relationship between the parameters gives additional insight to be used for improved surrogate models that guide the optimisation. (iii) Robust exploration and optimisation takes the stochasticity of the process into account and bounds the probability of the results deviating from a defined admissible set. A typical application in manufacturing process parameter optimisation is to prevent damage to the physical process hardware and to bound the probability of unusable scrap products resulting from the exploration and optimisation. (iv) In contrast to exploration in the parameter space, state-space exploration purposefully steers us to a target state, of the manufacturing process or of the resulting product. For this, one ideally uses an inverse surrogate model that maps the target state to matching process parameters. Combining state- and parameter-space optimisation allows us to exploit the different dimensionalities and sensitivities (with respect to their optimisation targets) of both approaches. In the multi-stage setting, being able to achieve, purposefully, a defined intermediate state (for varying initial conditions) helps in the exploration and optimisation of later process stages.