Knowledge base development for assembly planning using evidence theory.

This paper presents an approach to assembly planning in the early phase of product development. The product specification, workstation, environment, equipment and tools are not fully known in the early stage of product development. When comparing product variants at this stage there is a lack of data that affects the efficiency of the manufacturing process. It is therefore necessary to apply methods useful in processing incomplete and uncertain data. The main indicator which helps in comparing different product variants is manufacturing time standard. This papier is focused on assembly tool selection which is one of important data influenced assembly time. Based on the proposed algorithm and case study, a tool selection method using a decision tree induced from a training set with reduced uncertainty is presented.