Production rescheduling through product quality prediction

In 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 environment jobs progress. Utilizing advanced monitoring prediction-based rescheduling method proposed react in-process scrap predictions, performed machine learning algorithms. Based overall rescheduled with higher efficiency, compared to after completion the whole machining process realization scrap. Series numerical experiments are presented demonstrate potentials rescheduling, early-stage detection.