Industrial Transfer Learning: Boosting Machine Learning in Production

Tercan, Hasan; Guajardo, Alexandro; Meisen, Tobias

doi:10.1109/indin41052.2019.8972099

Cited by 50Domain Adaptation and Few-Shot LearningDigital Transformation in IndustryData Stream Mining Techniques

Industrial Transfer Learning: Boosting Machine Learning in Production

Tercan, Hasan, Guajardo, Alexandro, Meisen, Tobias·2019·2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

DOI 10.1109/indin41052.2019.8972099

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Abstract

In the field of production, machine learning offers great potentials to develop innovative solutions for optimization or automation. However, it faces challenges with regard to the availability of data and the high training effort of the learning models in the event of changes in the production process. In this paper, we address these challenges by introducing deep transfer learning for production. We demonstrate its potentials and benefits in a real application for predictive quality in injection molding and propose a novel approach for the continual training of neural networks across manufactured products. By creating a neural network that leverages knowledge from previous products without forgetting them, the approach shows better learning rates and more accurate predictions while requiring much less data for training. Our code is publicly available1to reproduce our results and build upon them.

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The incorporation of transfer learning, using pre-trained models on extensive datasets like COCO, can accelerate the training process and improve initial performance.
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Authors: Tercan, H., Guajardo, A., & Meisen, T.
Affiliations: University of Wuppertal (DE)
RWTH Aachen University (DE)
ORCID: Tercan, Hasan
Meisen, Tobias
Venue: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
Publisher: IEEE
Published: 2019-07
Pages: 274-279
ISSN: 1935-4576 (ISSN-L)
DOI: 10.1109/indin41052.2019.8972099
OpenAlex: W3003183227
Cited by: 50
References: 21
Citations by year: 2026: 3 · 2025: 3 · 2024: 10 · 2023: 11 · 2022: 10 · 2021: 11 · 2020: 2
FWCI: 2.20
Type: conference paper
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE International Conference on Industrial Informatics (INDIN) (publishedVersion)
RWTH Publications (RWTH Aachen) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
Keywords: deep transfer learning, injection molding, predictive quality, continuous training, artificial neural networks, data scarcity, production process optimization, manufacturing automation

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Industrial Transfer Learning: Boosting Machine Learning in Production

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