The project will develop a methodology to predict engineering properties in injection molded parts. The methodology will incorporate the application of connectionist learning systems, neural networks, to the processing of non-newtonian viscoelastic polymeric materials. The desired engineering properties in injection molded parts are strongly dependent upon the thermomechanical history which, in turn, is greatly influenced by the processing parameters. The relationships between part properties and thermomechanical history are complex and highly non- linear, therefore the methodology to be developed will be based on a back propagation algorithm that provides the means for training networks with adaptive non-linear units. The networks will be trained with the thermomechanical history of a simple plaque, from the results of injection molding simulation programs, and the corresponding engineering properties. The network relates thermomechanical history to the part properties through the weighted connections developed during training. Thus, when thermomechanical histories from simulation results are known for every location within any part, the corresponding properties can be predicted. This methodology which is based on neural network computation will provide part designers with the ability to predict engineering properties in injection molded parts without molding and testing actual components.
