Authors:
Pedro M. Goncalves
1
;
2
;
Miguel Brito
2
and
Jose Guilherme Cruz Moreira
1
Affiliations:
1
Bosch Car Multimedia Portugal S.A, Braga, Portugal
;
2
Centro Algoritmi, University of Minho, Braga, Portugal
Keyword(s):
Machine Learning, Deep Learning, Failure Prediction, PCB, Industry 4.0, Multimodal Classification.
Abstract:
In the era of Industry 4.0, where digital technologies revolutionize manufacturing, a wealth of data drive optimization efforts. Despite the opportunities, managing these vast datasets poses significant challenges. Printed Circuit Boards (PCBs) are pivotal in modern industry, yet their complex manufacturing process demands robust fault detection mechanisms to ensure quality and safety. Traditional classification models have limitations, exacerbated by imbalanced datasets and the sheer volume of data. Addressing these challenges, our research pioneers a multimodal classification approach, integrating PCB images and structured data to enhance fault prediction. Leveraging diverse data modalities, our methodology promises superior accuracy with reduced data requirements. Crucially, this work is conducted in collaboration with Bosch Car Multimedia, ensuring its relevance to industry needs. Our goals encompass crafting sophisticated models, curbing production costs, and establishing effici
ent data pipelines for real-time processing. This research marks a pivotal step towards efficient fault prediction in PCB manufacturing within the Industry 4.0 framework.
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