In modern automotive manufacturing the data associated with the products and the assembly processes required to build a car are not intelligently linked. This becomes problematic when trying to efficiently create best practice templates for assembly processes because the procedure for linking these best practice assembly processes with vehicle components becomes a daunting task. This research is aimed at the development of a decision support system and cyber infrastructure for efficiently linking product information and process information. Specifically, tools are developed to link product and process information in the automotive industry. The goal of this research is to develop and encode rules to guide the linking of product and process information. These rules are formed from historical data and provide suggestions to process planners as to which variants of parts can be assigned to the same standardized set of assembly instructions which exist as process sheet templates. The part data used consists of a standard text description of the part, the bounding box coordinates for all possible installation locations of the part, a unique identifier for the part, and a logic string which defines valid vehicle configurations such as platforms, models, and options for which the part is valid. A portion of this data is analyzed and used as the basis for development of the part grouping rules. Each rule will compare two parts with respect to the relevant part data and determine whether the two parts are capable of being assigned to the same process sheet. The development of the rules is discussed along with examples, followed with a discussion regarding the implementation of the rules into a prototype system. The rules are tested against part data not used in the development process, and a discussion of the results is presented. The paper concludes with conclusions drawn from testing of the rules and a discussion of future work.

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