Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks

Abstract

To deal with the unpredictability of dynamic markets, automated manufacturing systems rely on their capacity to adapt and change. With the need for more personalized and high-quality goods, the complexity of these systems evolves, prompting more agile and adaptable techniques. To enable dynamic as well as on systems reconfiguration aimed at responding swiftly to product changes by providing more efficient services. To increase production in response to market demand and meet the referred requirements, this proposed study employs Machine Learning Techniques for the Reconfiguration of Automated Manufacturing Systems. Gated Graph Neural Network (GGNN) based prediction model is generated using graph instances as input, and the prediction model provides a result for each graph instance, as well as activity level relevance and ratings for the relevant needs such as model accuracy and validation. For better use of the model effectiveness by the proposed methodology for the final model is validated for cost, time, and productivity.