Design quality and robustness with neural networks

Data sets that are gathered for industrial applications are frequently noisy and present challenges that necessitate use of different methodologies, We present a successful example of generating concise and accurate neural-network models for multiple quality characteristics in injection molding. These models map process measurements to product quality. They are used for product and process design, addressing material and processing issues, and defining the operating window and its robustness. The models are developed based on data from designed and other experiments. Linear regression, decision tree induction, nonlinear regression, as well as "stepwise neural networks" were used for feature selection and model comparison. The final model consists of a neural network with three inputs, one hidden layer and five outputs, modeling five critical to quality variables (CTQ's) simultaneously with high accuracy, The neural network was visualized for validation and insight.