Application of Machine Learning for Product Batch Oriented Control of Production Processes

Meiners M, Mayr A, Thomsen M, Franke J (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Procedia CIRP Curran

Book Volume: 93

Pages Range: 431-436

DOI: 10.1016/j.procir.2020.04.006

Abstract

The digitalization in manufacturing offers high potential for optimization in terms of quality and efficiency. In particular, machine learning techniques can be used to analyze data generated along the production chain for complex patterns. As the final product quality highly depends on interactions within the production chain, a process control system needs to consider all information of the supplied semi-finished products to achieve a continuously high quality. Using machine learning, this work presents a two-stage batch control system to optimize a batch process with high interdependencies between the delivered material, used equipment and process parameters concerning the final product quality.

Authors with CRIS profile

Moritz Meiners Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Andreas Mayr Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Maximilian Thomsen Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

APA:

Meiners, M., Mayr, A., Thomsen, M., & Franke, J. (2020). Application of Machine Learning for Product Batch Oriented Control of Production Processes. Procedia CIRP, 93, 431-436. https://dx.doi.org/10.1016/j.procir.2020.04.006

MLA:

Meiners, Moritz, et al. "Application of Machine Learning for Product Batch Oriented Control of Production Processes." Procedia CIRP 93 (2020): 431-436.

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