Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks

Abstract

To deal with the unpredictability of dynamic markets, automated manufacturing systems rely ontheir 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. Toenable dynamic as well as on systems reconfiguration aimed at responding swiftly to productchanges by providing more efficient services. To increase production in response to marketdemand and meet the referred requirements, this proposed study employs Machine LearningTechniques for the Reconfiguration of Automated Manufacturing Systems. Gated Graph NeuralNetwork (GGNN) based prediction model is generated using graph instances as input, and theprediction model provides a result for each graph instance, as well as activity level relevanceand ratings for the relevant needs such as model accuracy and validation. For better use of themodel effectiveness by the proposed methodology for the final model is validated for cost, time,and productivity.