Development of a data structure for the description of nominal and measurement properties of optical elements
Recent advances in the digitalization of production processes allow a thorough analysis of the different process parameters. Machine learning algorithms help to find the different dependencies of the production parameters by modelling the production processes especially when a large number of parameters is involved. The knowledge gained of the dependencies of the production parameters can be used to greatly improve the production quality. To harness the full potential of this approach, the different production processes along a value chain must be considered as a whole instead of modelling every process independently. Especially in the field of optics production with tight production tolerances, that are often at the edge of todays manufacturability and metrology, the approach of consid ering all production processes as a whole holds great potential. The evaluation of the production parameters of the whole value chain can help predict and improve the quality of the final product, using techniques such as tolerance matching or enabling adaptive changes in the process without compromising the quality of the final product.