1 Automation and Process Control Engineer, Gist Limited, Bristol, United Kingdom.
2 Graduate Assistant, University of Northern IOWA, USA
3 General Electric HealthCare, Production Engineer, Noblesville, Indiana, United States.
4 Sortation Engineer, Gist Limited UK.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1272-1295
Article DOI: 10.30574/wjarr.2024.23.3.2800
Received on 02 August 2024; revised on 18 Septembe 2024; accepted on 11 September 2024
This article explores the integration of automation and deep learning in modern manufacturing to address critical challenges such as redundancy, defects, vibration analysis, and material strength. As manufacturing processes evolve, the need for more sophisticated methods to optimize production efficiency and product quality becomes paramount. Automation, coupled with deep learning techniques, offers powerful tools for enhancing manufacturing processes. These technologies enable predictive maintenance, reducing downtime by identifying potential equipment failures before they occur. Furthermore, deep learning algorithms can analyse complex data sets to detect defects in products with greater accuracy and speed than traditional methods. Vibration analysis, a key aspect of predictive maintenance, benefits from automated systems that monitor and diagnose issues in real-time, preventing costly disruptions. Additionally, deep learning models can assess material strength and predict potential failures, ensuring that products meet rigorous safety and quality standards. The synergy between automation and deep learning not only streamlines manufacturing processes but also enhances the ability to adapt to changing conditions, thereby minimizing operational inefficiencies. This article highlights the transformative impact of these technologies on the manufacturing industry, illustrating their potential through case studies and practical examples. By addressing key challenges such as redundancy and defects, automation and deep learning contribute to the creation of more reliable, efficient, and resilient manufacturing systems. The insights provided in this study underscore the importance of continued innovation in integrating these technologies to maintain a competitive edge in the rapidly evolving manufacturing landscape.
Automation; Redundancy; Defects; Vibration Analysis; Material Strength; Deep Learning
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Joseph Nnaemeka Chukwunweike, Andrew Nii Anang, Adewale Abayomi Adeniran and Jude Dike. Enhancing manufacturing efficiency and quality through automation and deep learning: addressing redundancy, defects, vibration analysis, and material strength optimization. World Journal of Advanced Research and Reviews, 2024, 23(3), 1272-1295. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2800