Abstract
Launches of new products or manufacturing processes are challenging due to requirements regarding quality and capability. This article observes a production process for GFRP leaf springs based on the VARTM technology. The production of structural automotive applications in high volumes requires automated production lines. A multiplicity of sensors alongside that production processes acquires a huge quantity of data about the current production conditions. The fields of statistics and data mining offer new possibilities to analyse these data. An approach for the exploration of an optimised set of parameters is developed. Thereby the main influencing factors for particular defects are determined. Predictive models to forecast the probability of possible defects are established. These findings confirm that data mining encourages and accelerates the launch of new products and processes at an early stage of production. The time to market and the manufacturing costs for fibre-reinforced plastics can be reduced.
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