Deep Learning for Robust Manufacturing: From Quality Control to Predictive Maintenance
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https://orcid.org/0009-0000-5908-4112
Anibal Bregon
Miguel A. Martinez-Prieto
Abstract
In today’s global manufacturing landscape, companies are required to balance speed, precision, and sustainability, thereby making intelligent, data-driven solutions a necessity. The convergence of Industry 4.0, cyber-physical systems and artificial intelligence technologies is leading to a new paradigm known as smart manufacturing, where the effective use of collected data can increase productivity, efficiency, and quality. This research explores the potential of deep learning to enhance industrial productivity by leveraging automated quality control of manufactured components and predictive maintenance of systems. Thus, this thesis focuses on two main objectives within an industrial context: (OB1) the development of models to enhance the quality control process, and (OB2) the development of models to implement a predictive maintenance strategy. These objectives are approached through three expected contributions. First, a quality control model for thermal images aligned with factory requirements (C1). Second, a contrastive learning model for anomaly detection in multiview images (C2). Third, a predictive maintenance model for die-casting molds (C3). Initial results are starting to show the advantage of these contributions to improve productivity in smart factories.
How to Cite
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Deep Learning, Industry 4.0, Quality Control, Predictive Maintenance
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