Local characterisation of variances for the planning and configuration of process chains in micro manufacturing

During the last decades, a continuous trend towards miniaturisation leads to an increasing demand of metallic micromechanical components. To achieve an economic production on an industrial scale, the planning and configuration of process chains has become a key success factor. Thereby, the occurrence of so-called size-effects renders the planning and particularly the configuration of process chains complicated. While process planners have to ensure very small tolerances, these size-effects lead to comparably strong variances within the behaviour of processes. To cope with these, this article proposes an extension to the μ-ProPlAn methodology. While μ-ProPlAn, in its current state, provides tools to characterise and utilise interdependencies between production relevant parameters, it lacks the capabilities to quantify and use variances for the prediction of process results. In this article, we propose an extension to this methodology, enabling a local estimation of variance of relevant process parameters using sample data. As a result, the planning quality can be increased as additional goals and methods for optimisation become available.