Artificial neural network based model for computation of injection mould complexity

Abstract

The cost of injection mould construction depends primarily on the mould complexity. The ability to estimate the mould complexity before releasing the final drawings for construction purposes will greatly help the designers to understand the implications of their design on cost. Mould complexity depends on several factors such as part geometry, parting line, materials, and number of cavities per mould. In most industries, the mould complexity evaluation is performed manually based on past experiences of mould makers. Faced with a shortage of experienced mould makers, there is a pressing need for development of computer-aided tools for mould complexity evaluation. In this study, a neural network-based design tool for computing the mould complexity index, which represents the degree of difficulty of mould manufacturing, has been developed and implemented using a 14-3-1 backpropagation network running on the CNAPS neuro-computer.