Abstract
Data fusion is the integration and analysis of data from multiple sensors to develop a more accurate understanding of a situation and determine how to respond to it. Although data fusion can be applied in many situations, this paper focuses on its application to manufacturing and how it changes some of the more traditional, less adaptive information models that support the design and manufacturing functions. The paper consists of four parts. The first section explains what data fusion is and its impact on manufacturing. The second section describes what an information system architecture is and explains the natural language-based information modeling methodology used by this research project. The third section identifies the major design and manufacturing functions, reviews the information models required to support them, and then shows how these models must be extended to support data fusion. The fourth section discusses the future directions of this work.
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