Version 1
: Received: 29 May 2024 / Approved: 30 May 2024 / Online: 30 May 2024 (15:52:31 CEST)
How to cite:
Anchundia, J.; Berrones, F.; Verdugo, A.; Comina, M. Real-Time Quality Inspection through Industry 4.0 Technologies. Preprints2024, 2024052072. https://doi.org/10.20944/preprints202405.2072.v1
Anchundia, J.; Berrones, F.; Verdugo, A.; Comina, M. Real-Time Quality Inspection through Industry 4.0 Technologies. Preprints 2024, 2024052072. https://doi.org/10.20944/preprints202405.2072.v1
Anchundia, J.; Berrones, F.; Verdugo, A.; Comina, M. Real-Time Quality Inspection through Industry 4.0 Technologies. Preprints2024, 2024052072. https://doi.org/10.20944/preprints202405.2072.v1
APA Style
Anchundia, J., Berrones, F., Verdugo, A., & Comina, M. (2024). Real-Time Quality Inspection through Industry 4.0 Technologies. Preprints. https://doi.org/10.20944/preprints202405.2072.v1
Chicago/Turabian Style
Anchundia, J., Alexandra Verdugo and Mayra Comina. 2024 "Real-Time Quality Inspection through Industry 4.0 Technologies" Preprints. https://doi.org/10.20944/preprints202405.2072.v1
Abstract
The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.
Keywords
Quality 4.0; digital transformation; Industry 4.0; artificial intelligence in quality control; automated inspection systems
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
