Abstract
The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.
Highlights
The timely inspection of parts and components along the production line is critical to ensure that each part adheres to strict quality criteria necessary to guarantee the safety of the product’s end-user, in sectors such as aerospace, naval and automotive.Regarding the latter, the adoption of lighter and robust materials has made it so that some parts cannot be welded, with structural adhesives playing an important role as an alternative that contributes to the reduction of noise, vibrations and infiltrations.the inspection of parts bonded with this method typically involves destructive tests that require the separation of bonded parts as a way to enable the analysis of the continuity, spread and consistency of the adhesive
Regarding the generation of synthetic structural adhesive defects, different seed and truncation values were used to generate a variety of images, with some examples shown in Figure 3 where the first column presents real images, while all others are generated by the trained Generative Adversarial Networks (GANs)
While generally a few thousand training images are still required for this purpose, we show that the usage of a much smaller dataset still yielded results capable of greatly improving the performance of object detection models in the specific task of structural adhesive inspection, in the case of imbalanced training data
Summary
The timely inspection of parts and components along the production line is critical to ensure that each part adheres to strict quality criteria necessary to guarantee the safety of the product’s end-user, in sectors such as aerospace, naval and automotive.Regarding the latter, the adoption of lighter and robust materials has made it so that some parts cannot be welded, with structural adhesives playing an important role as an alternative that contributes to the reduction of noise, vibrations and infiltrations.the inspection of parts bonded with this method typically involves destructive tests that require the separation of bonded parts as a way to enable the analysis of the continuity, spread and consistency of the adhesive. The timely inspection of parts and components along the production line is critical to ensure that each part adheres to strict quality criteria necessary to guarantee the safety of the product’s end-user, in sectors such as aerospace, naval and automotive. Regarding the latter, the adoption of lighter and robust materials has made it so that some parts cannot be welded, with structural adhesives playing an important role as an alternative that contributes to the reduction of noise, vibrations and infiltrations. Even in cases for which defect data can be made available, it is difficult to ensure that a balanced number of samples of each defect type is included
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
