Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307774 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 35 [Issue:] 7 [Publisher:] Springer US [Place:] New York, NY [Year:] 2023 [Pages:] 3479-3499
Publisher: 
Springer US, New York, NY
Abstract: 
The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.
Subjects: 
Failure prognosis
Predictive quality control
Supervised learning
Human–machine interaction
Interactive machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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