Deep Learning in Industry 4.0: Transforming Manufacturing Through Data-Driven Innovation

Conference Paper

First Online: 04 January 2024
Pages: 222–236

Authors

• Kushagra Agrawal
• Nisharg Nargund

Conference

Distributed Computing and Intelligent Technology (ICDCIT 2024)

Book Series

Lecture Notes in Computer Science (LNCS, volume 14501)

Abstract

Industry 4.0 is reshaping manufacturing by seamlessly integrating data acquisition, analysis, and modeling, creating intelligent and interconnected production ecosystems. Driven by cyber-physical systems, the Internet of Things (IoT), and advanced analytics, it enables real-time monitoring, predictive maintenance, adaptable production, and enhanced customization. By amalgamating data from sensors, machines, and human inputs, Industry 4.0 provides holistic insights, resulting in heightened efficiency and optimized resource allocation.

Deep Learning (DL), a crucial facet of artificial intelligence, plays a pivotal role in this transformation. This article delves into DL fundamentals, Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning, discussing their functions and applications. It also elaborates on key DL components: neurons, layers, activation functions, weights, bias, loss functions, and optimizers, contributing to network efficacy. The piece underscores Industry 4.0’s principles: interoperability, virtualization, decentralization, real-time capabilities, service orientation, and modularity.

It highlights DL’s diverse applications within Industry 4.0 domains, including predictive maintenance, quality control, resource optimization, logistics, process enhancement, energy efficiency, and personalized production. Despite transformative potential, implementing DL in manufacturing poses challenges: data quality and quantity, model interpretability, computation demands, and scalability. The article anticipates trends, emphasizing explainable AI, federated learning, edge computing, and collaborative robotics. In conclusion, DL’s integration with Industry 4.0 heralds a monumental manufacturing paradigm shift, fostering adaptive, efficient, and data-driven production ecosystems. Despite challenges, a future envisions Industry 4.0 empowered by DL’s capabilities, ushering in a new era of production excellence, transparency, and collaboration.

Read it here : https://link.springer.com/chapter/10.1007/978-3-031-50583-6_15