A Model of Design for Assembly using Data Warehouse, Genetic Algorithm, and Neural Network

Nowadays in industries, being aware of the importance of product design for facilitating the manufacturing process is of utmost prominence. Its principles and applications are a novel approach in intelligent design of parts as well as manufacturing and Assembly engineering and utilizing software applications of computer-aided-design has transformed into a valuable tool for design engineers. This study tries to introduce design for manufacturing and assembly using computers and data mining in order to help engineers to evaluate part manufacturing measures so that the engineers can reach an insight based on the present variables such as the number of parts, the type of the devices and necessary equipment for manufacturing different parts and also they can get a better understanding of selecting the type of manufacturing process in order to build and construct different products in comparison to cost estimations. Since data warehouse is the most useful technology in this regard and the necessary data for evaluating formulae can be modeled as multidimensional data and stored in time series in data inventories and apply different analysis methods on them, the current paper proposes a model for minimizing the costs in “design for assembly” using data warehouse, genetic algorithm and neural networks and eventually the results obtained from this model will be compared to the findings of the previous tools.