Frye, MaikMaikFryeGyulai, DávidDávidGyulaiBergmann, JúliaJúliaBergmannSchmitt, Robert H.Robert H.Schmitt2022-03-062022-03-062021https://publica.fraunhofer.de/handle/publica/27107610.1016/j.promfg.2021.07.022In production management, efficient scheduling is key towards smooth and balanced production. Scheduling can be well-supported by real-time data acquisition systems, resulting in decisions that rely on actual or predicted status of production environment and jobs in progress. Utilizing advanced monitoring systems, prediction-based rescheduling method is proposed that can react on in-process scrap predictions, performed by machine learning algorithms. Based on predictions, overall production can be rescheduled with higher efficiency, compared to rescheduling after completion of the whole machining process with realization of scrap. Series of numerical experiments are presented to demonstrate potentials in prediction-based rescheduling, with early-stage scrap detection.enMachine LearningProduction SchedulingProduct Quality PredictionData Quality658670journal article