Challenges and Opportunities for Deep Learning Applications in Industry 4.0

Author:

Navadia Nipun R.1,Kaur Gurleen1,Bhadwaj Harshit2,Singh Taranjeet2,Singh Yashpal3,Malik Indu4,Bhardwaj Arpit5,Sakalle Aditi6

Affiliation:

1. Dronacharya Group of Institutions,Greater Noida,India

2. Mangalmay Institute of Engineering and Technology,Greater Noida,India

3. Mangalmay Institute of Engineering and Technology,Greater Noida, India

4. Galgotias University, Greater Noida, India

5. Bennett University, Greater Noida, India

6. USICT, Gautam Buddha University,Greater Noida, India

Abstract

Manufacturing plays a prominent role in the development and economic growth of countries. A dynamic shift from a manual mass production model to an integrated automated industry towards automation includes several stages. Along with the boost in the economy, manufacturers also face several challenges, including several aspects. Machine Learning can prove to be an essential tool and optimize the production process, respond quickly to the changes and market demand respectively, predict certain aspects of the particular industry to improve performance, maintain machine health and other aspects. Machine Learning technology can prove its effectiveness when applied to a specific issue in the sector— such as filtering out the primary use cases of Machine Learning manufacturing specifically, 'Predictive quality and yield' and 'Predictive maintenance.' Supervised Machine Learning and Unsupervised Machine Learning may provide the accuracy to predict the outputs and the underlying patterns.

Publisher

BENTHAM SCIENCE PUBLISHERS

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