Machine learning pipeline for application in manufacturing
Publication date
2024-03
Document type
Conference paper
Author
Fitzner, Antje
Hülsmann, Tom
Ackermann, Thomas
Pouls, Kevin
Krauß, Jonathan
Mende, Felix
Leyendecker, Lars
Schmitt, Robert H.
Organisational unit
Fraunhofer Research Institution for Battery Cell Production FFB
RWTH Aachen
Book title
Machine learning for cyber physical systems
Part of the university bibliography
✅
Keyword
Machine learning
ML pipeline
Manufacturing
Data mining
Abstract
The integration of machine learning (ML) into manufacturing processes is crucial for optimizing efficiency, reducing costs, and enhancing overall productivity. This paper proposes a comprehensive ML pipeline tailored for manufacturing applications, leveraging the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) as its foundational framework. The proposed pipeline consists of key phases, namely business understanding, use case selection and specification, data integration, data preparation, modelling, deployment, and certification. These are designed to meet the unique requirements and challenges associated with ML implementation in manufacturing settings. Within each phase, sub-topics are defined to provide a granular understanding of the workflow. Responsibilities are clearly outlined to ensure a structured and efficient execution, promoting collaboration among stakeholders. Further, the input and output of each phase are defined. The methodology outlined in this research not only enhances the applicability of CRISP-DM in the manufacturing domain but also serves as a guide for practitioners seeking to implement ML solutions in a systematic and well-defined manner. The proposed pipeline aims to streamline the integration of ML technologies into manufacturing processes, facilitating informed decision-making and fostering the development of intelligent and adaptive manufacturing systems.
Version
Published version
Access right on openHSU
Open access