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Adaptive scheduling through machine learning-based process parameter prediction

: Frye, Maik; Gyulai, David; Bergmann, Julia; Schmitt, Robert H.


MM Science journal 12 (2019), Special Issue HSM 2019, 15th International Conference on High Speed Machining, pp.3060-3066
ISSN: 1803-1269 (Print)
ISSN: 1805-0476 (Online)
European Commission EC
H2020; 739592; EPIC
Journal Article
Fraunhofer IPT ()
Artificial intelligence; Machine learning; Data analytics; Adaptive scheduling; Process parameter prediction; Process optimization; Job shop scheduling

Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive ML-models on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation.