Abstract
Many manufacturing companies control their production machines to produce good products within quality standards by using the results of research on physical or chemical models. Those models are developed from knowledge of the physical or chemical changes that occur when the products are processed and operational knowledge. However, it is difficult for some companies to research physical or chemical models. We study data-driven control systems to enable the stable production of good products when it is difficult to study and develop physical and chemical models or to use operational knowledge. In this paper, we propose an algorithm that builds an alternative model from actual operation data using machine learning and finds the optimal operating conditions under which the product is within the quality standards range using 0–1 integer programming. The effectiveness of the proposed algorithm was verified using operation data generated using a simulator for a food manufacturing process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kano, M., Nakagawa, Y.: Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry. Comput. Chem. Eng. 32, 12–24 (2008)
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (2013). https://doi.org/10.1007/978-0-85729-398-5
Åströom, K.J., Wittenmark, B.: Adaptive Control, 2nd edn. Dover Publications (2008)
Hjalmarsson, H., Gevers, M., Gunnarsson, S., Lequin, O.: Iterative feedback tuning: theory and applications. IEEE Control. Syst. Mag. 18(4), 26–41 (1998)
Hou, Z.-S., Wang, Z.: From model-based control to data-driven control: survey, classification and perspective. Inf. Sci. 235, 3–35 (2013)
Spall, J.C.: Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Autom. Control 37(3), 332–341 (1992)
Hou, Z., Jin, S.: Model-free adaptive control for a class of nonlinear discrete-time systems based on the partial form linearization. IFAC Proc. Vol. 41(2), 3509–3514 (2008)
Hjalmarsson, H., Gunnarsson, S., Gevers, M.: A convergent iterative restricted complexity control design scheme. In: Proceedings of the 33rd IEEE Conference on Decision and Control, pp. 1735–1740 (1994)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Tanizaki, T., Fukuyama, A., Uchino, K., Kurokawa, T., Nakagawa, S., Kataoka, T. (2024). Data-Driven Control System Using Machine Learning in Production Process. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-031-65894-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-65894-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65893-8
Online ISBN: 978-3-031-65894-5
eBook Packages: Computer ScienceComputer Science (R0)