Simulation-Based Data Augmentation for the Quality Inspection of
Structural Adhesive With Deep Learning
Abstract
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%
(
[email protected]). Additional results can be seen at https://git.io/Jtc4b.