Abstract
The application and functional scope of digital assembly planning tools have been permanently increasing in order to deal with product and process complexity. Consequently a large amount of assembly-related data is stored in different systems alongside the product emergence process. By means of data mining techniques an intelligent utilization of this data can be accomplished for future assembly planning. This paper presents an approach for data mining-supported generation of assembly process plans to enhance planning efficiency. The approach is based on the classification and clustering of both product and process data as well as on the identification of their correlations.
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