TechRxiv
09438624.pdf (2.75 MB)

Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive With Deep Learning

Download (2.75 MB)
preprint
posted on 14.07.2021, 13:23 by Ricardo Peres, Magno Guedes, Fábio Miranda, José Barata
The advent of Industry 4.0 has shown the tremendous transformative potential of combining artificial intelligence, cyber-physical systems and Internet of Things concepts in industrial settings. Despite this, data availability is still a major roadblock for the successful adoption of data-driven solutions, particularly concerning deep learning approaches in manufacturing. Specifically in the quality control domain, annotated defect data can often be costly, time-consuming and inefficient to obtain, potentially compromising the viability of deep learning approaches due to data scarcity. In this context, we propose a novel method for generating annotated synthetic training data for automated quality inspections of structural adhesive applications, validated in an industrial cell for automotive parts. Our approach greatly reduces the cost of training deep learning models for this task, while simultaneously improving their performance in a scarce manufacturing data context with imbalanced training sets by 3.1% (mAP@0.50). Additional results can be seen at https://git.io/Jtc4b.

History

Email Address of Submitting Author

ricardo.peres@uninova.pt

ORCID of Submitting Author

0000-0003-3777-1346

Submitting Author's Institution

UNINOVA

Submitting Author's Country

Portugal

Usage metrics

Licence

Exports